diff --git a/README.md b/README.md index beb16ce..bfca0d2 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,10 @@ Clone the repository: git clone https://github.com/geo-kit/GeoCode.git +To run reservoir simulations with [JutulDarcy](https://github.com/sintefmath/JutulDarcy.jl), +install [Julia](https://julialang.org/downloads/) and instantiate the driver dependencies once: + + julia --project=geocode/bin -e "using Pkg; Pkg.instantiate()" > [!Note] > Note: the project is in developement. 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b/geocode/bin/Project.toml new file mode 100644 index 0000000..b42250f --- /dev/null +++ b/geocode/bin/Project.toml @@ -0,0 +1,6 @@ +[deps] +Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" +HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f" +JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1" +Jutul = "2b460a1a-8a2b-45b2-b125-b5c536396eb9" +JutulDarcy = "82210473-ab04-4dce-b31b-11573c4f8e0a" diff --git a/geocode/bin/jutul_optimize.jl b/geocode/bin/jutul_optimize.jl new file mode 100644 index 0000000..dd92b22 --- /dev/null +++ b/geocode/bin/jutul_optimize.jl @@ -0,0 +1,393 @@ +#!/usr/bin/env julia +# Universal forecast BHP optimization driver. +# +# Loads a .DATA deck, simulates its history, then builds an N-month forecast +# and optimizes per-period BHP per individual well to maximize forecast NPV via +# JutulDarcy adjoint gradients + L-BFGS. +# +# Well roles (producer/injector) are derived from the deck's historical control +# types (last non-Disabled control wins), NOT from well-name prefixes, so the +# driver is deck-agnostic like jutul_run.jl. +# +# CLI: +# jutul_optimize.jl --case= --out= +# --months=N +# --oil-price= --gas-price= --water-price= +# --water-cost= --gas-cost= --discount-rate= +# --bhp-prod-min= --bhp-prod-max= +# --bhp-inj-min= --bhp-inj-max= (all BHP in bar) +# --max-it=N (optional, defaults to Jutul unit_box_bfgs default: 25) +# Prices/costs are $/m3 (oil, water) and $/m3 (gas); see liquid_unit/gas_unit=1. +# +# Env L-BFGS caps: OPTI_MAX_IT, OPTI_MAX_INITIAL_UPDATE, +# OPTI_LINE_SEARCH_MAX_IT, OPTI_GRAD_TOL. +# +# Writes /optimal_bhp.csv, /production.csv, /summary.json. + +using JutulDarcy +using JutulDarcy: replace_target, BottomHolePressureTarget, InjectorControl, + ProducerControl, DisabledControl, well_symbols, report_timesteps, + npv_objective, compute_well_qoi +import Jutul +import Jutul: JutulCase +using Dates +using JSON3 + +# tNavigator-specific keyword unknown to GeoEnergyIO: skip instead of failing +# (mirrors geocode/bin/jutul_run.jl). +JutulDarcy.GeoEnergyIO.InputParser.skip_kw!(:RUNCTRL, 1) + +const BAR = 1.0e5 # bar -> Pa + +# --------------------------------------------------------------------------- +# CLI parsing +# --------------------------------------------------------------------------- +function parse_args(argv) + d = Dict{String,Any}() + floats = Set(["oil-price", "gas-price", "water-price", "water-cost", + "gas-cost", "discount-rate", "bhp-prod-min", "bhp-prod-max", + "bhp-inj-min", "bhp-inj-max"]) + for a in argv + startswith(a, "--") || continue + kv = split(a[3:end], "=", limit = 2) + length(kv) == 2 || continue + k, v = String(kv[1]), String(kv[2]) + if k in ("case", "out", "history-cache") + d[k] = v + elseif k in ("months", "max-it") + d[k] = parse(Int, v) + elseif k in floats + d[k] = parse(Float64, v) + end + end + haskey(d, "case") || error("--case= is required") + haskey(d, "out") || error("--out= is required") + required = ("months", "oil-price", "gas-price", "water-price", "water-cost", + "gas-cost", "discount-rate", "bhp-prod-min", "bhp-prod-max", + "bhp-inj-min", "bhp-inj-max") + missing = filter(k -> !haskey(d, k), required) + isempty(missing) || + error("Missing required options: " * join(("--" * k for k in missing), ", ")) + return d +end + +# --------------------------------------------------------------------------- +# Role classification: scan the deck's historical forces, last non-Disabled +# control wins (matches JutulDarcy's set_active_controls! semantics). +# --------------------------------------------------------------------------- +# Classify wells by their control at the END of history (the forecast's +# starting state). The forecast continues from there, so we optimize the wells +# actually operating at that point. Wells shut (Disabled) or absent at the +# forecast start are left shut -- we do NOT re-open them, because a well shut +# for depletion / lost perforations cannot be revived just by setting a BHP +# target, and reopening an economically-shut well would ignore workover cost. +function classify_roles(case) + forces = case.forces isa Vector ? case.forces : [case.forces] + ctrls = forces[end][:Facility].control + producers = Symbol[] + injectors = Symbol[] + shut = Symbol[] + for w in collect(well_symbols(case.model)) + c = haskey(ctrls, w) ? ctrls[w] : nothing + if c isa ProducerControl + push!(producers, w) + elseif c isa InjectorControl + push!(injectors, w) + else + push!(shut, w) + end + end + return producers, injectors, shut +end + +# --------------------------------------------------------------------------- +# Forecast construction +# --------------------------------------------------------------------------- +function history_end_date(case, total_dt_s) + start = try + DateTime(case.input_data["RUNSPEC"]["START"]) + catch + DateTime(2000, 1, 1) + end + return start + Second(round(Int, total_dt_s)) +end + +# Wells passed here are active at the forecast start, so their control is a +# valid Producer/InjectorControl whose target we can swap for a BHP target. +function set_bhp_target!(fac, well, bhp) + fac.control[well] = replace_target(fac.control[well], BottomHolePressureTarget(bhp)) + fac.limits[well] = JutulDarcy.default_limits(fac.control[well]) +end + +function main(argv) + args = parse_args(argv) + outdir = args["out"] + mkpath(outdir) + months = args["months"] + + println("="^70) + println(" Forecast BHP optimization: $(basename(args["case"]))") + println(" months=$months") + println("="^70) + + # -- Load history ------------------------------------------------------- + case = setup_case_from_data_file(args["case"]; backend = :csr) + history_cache = get(args, "history-cache", nothing) + if history_cache !== nothing && isdir(history_cache) + println("\n[1/5] Loading cached history ...") + result_hist = simulate_reservoir(case; + output_path = history_cache, restart = true) + else + println("\n[1/5] Simulating history ...") + result_hist = simulate_reservoir(case) + end + state0_forecast = Jutul.setup_state(case.model, result_hist.result.states[end]) + + producers, injectors, shut = classify_roles(case) + isempty(producers) && isempty(injectors) && + error("No wells are operating at the forecast start; nothing to optimize.") + if !isempty(shut) + println(" Shut at forecast start (left shut, not optimized): $shut") + end + println(" Producers: $producers") + println(" Injectors: $injectors") + + # -- Build N-month forecast forces from the last history force ---------- + println("\n[2/5] Building $months-month forecast ...") + nperiods = months + hist_end = history_end_date(case, sum(Float64.(case.dt))) + fdates = [hist_end + Month(i) for i in 0:nperiods] + dt_forecast = Float64[ + Dates.value(Millisecond(fdates[i+1] - fdates[i])) / 1000.0 + for i in 1:nperiods + ] + + # The base forecast continues the final historical controls unchanged. + last_force = case.forces isa Vector ? case.forces[end] : case.forces + base_forces = [deepcopy(last_force) for _ in 1:nperiods] + case_base = JutulCase(case.model, dt_forecast, base_forces; + state0 = state0_forecast, parameters = case.parameters, + input_data = case.input_data) + + # control_specs: (label, wells, bhp_min, bhp_max, x0_norm, role). + # Start BHP optimization from the final historical BHP, clipped to the + # requested bounds. One control per individual well. + function spec_for(label, wells, role) + prefix = role == "producer" ? "bhp-prod" : "bhp-inj" + bmin = args["$prefix-min"] * BAR + bmax = args["$prefix-max"] * BAR + bhp0 = sum(Float64(compute_well_qoi(case.model, + result_hist.result.states[end], last_force, w, :bhp)) for w in wells) / length(wells) + x0n = clamp((bhp0 - bmin) / (bmax - bmin), 0.0, 1.0) + return (label, wells, bmin, bmax, x0n, role) + end + specs = Tuple{Symbol,Vector{Symbol},Float64,Float64,Float64,String}[] + for w in producers + push!(specs, spec_for(w, [w], "producer")) + end + for w in injectors + push!(specs, spec_for(w, [w], "injector")) + end + ncontrols = length(specs) + println(" Variables: $(ncontrols * nperiods) ($ncontrols controls x $nperiods periods)") + + # Build clean forecast forces with setup_reservoir_forces (controls only, + # no historical per-well perforation masks, which the adjoint cannot + # vectorize). Active wells get their historical control with an initial BHP + # target; wells shut at the forecast start stay disabled. + orig_fac = last_force[:Facility] + init_controls = Dict{Symbol,Any}() + for (_, wells, bmin, bmax, x0n, _) in specs + for w in wells + init_controls[w] = replace_target(orig_fac.control[w], + BottomHolePressureTarget(bmin + x0n * (bmax - bmin))) + end + end + for w in shut + init_controls[w] = DisabledControl() + end + forecast_forces = [setup_reservoir_forces(case.model; control = deepcopy(init_controls)) + for _ in 1:nperiods] + + case_forecast = JutulCase(case.model, dt_forecast, forecast_forces; + state0 = state0_forecast, parameters = case.parameters, + input_data = case.input_data) + + # -- Optimization setup ------------------------------------------------- + x0 = zeros(ncontrols * nperiods) + for i in 1:nperiods, (c, spec) in enumerate(specs) + x0[(i-1)*ncontrols+c] = spec[5] + end + + function set_forecast_bhp!(forces, x) + xm = reshape(x, ncontrols, nperiods) + for s in 1:nperiods + fac = forces[s][:Facility] + for (c, (_, wells, bmin, bmax, _, _)) in enumerate(specs) + bhp = bmin + clamp(xm[c, s], 0.0, 1.0) * (bmax - bmin) + for w in wells + set_bhp_target!(fac, w, bhp) + end + end + end + end + + econ = (oil_price = args["oil-price"], gas_price = args["gas-price"], + water_price = args["water-price"], water_cost = args["water-cost"], + gas_cost = args["gas-cost"], discount_rate = args["discount-rate"], + liquid_unit = 1.0, gas_unit = 1.0) # 1.0 -> prices are $/m3 + + function evaluate_npv(case_eval, r) + dt_mini = report_timesteps(r.reports, ministeps = true) + npv_obj(m, st, dt, si, fo) = npv_objective(m, st, dt, si, fo; + injectors = injectors, producers = producers, timesteps = dt_mini, econ...) + obj = Jutul.evaluate_objective(npv_obj, case_eval.model, + r.states, case_eval.dt, case_eval.forces) + return obj, npv_obj, dt_mini + end + + cache = Dict{Symbol,Any}() + function bhp_objective!(x; grad = true) + set_forecast_bhp!(case_forecast.forces, x) + sim = simulate_reservoir(case_forecast; output_substates = true, info_level = -1) + r = sim.result + obj, npv_obj, dt_mini = evaluate_npv(case_forecast, r) + grad || return obj + forces = case_forecast.forces + + targets = Jutul.force_targets(case_forecast.model) + targets[:Facility][:control] = :control + targets[:Facility][:limits] = nothing + key = (length(r.states), length(dt_mini)) + if get(cache, :key, nothing) != key + cache[:storage] = Jutul.setup_adjoint_forces_storage( + case_forecast.model, r.states, forces, case_forecast.dt, npv_obj; + state0 = case_forecast.state0, targets = targets, + parameters = case_forecast.parameters, eachstep = true, di_sparse = true) + cache[:key] = key + end + dforces, _, _ = Jutul.solve_adjoint_forces!(cache[:storage], + case_forecast.model, r.states, r.reports, npv_obj, forces; + state0 = case_forecast.state0, parameters = case_forecast.parameters) + df = zeros(ncontrols, nperiods) + for s in 1:nperiods, (c, (_, wells, bmin, bmax, _, _)) in enumerate(specs) + g = 0.0 + for w in wells + g += dforces[s][:Facility].control[w].target.value + end + df[c, s] = g * (bmax - bmin) + end + return (obj, vec(df)) + end + + # -- Optimize ----------------------------------------------------------- + println("\n[3/5] Simulating base and optimizing (L-BFGS + adjoint) ...") + base_sim = simulate_reservoir(case_base; info_level = -1) + base_npv, _, _ = evaluate_npv(case_base, base_sim.result) + max_it = get(args, "max-it", parse(Int, get(ENV, "OPTI_MAX_IT", "25"))) + _, _, opt_history = Jutul.unit_box_bfgs(x0, bhp_objective!; + maximize = true, max_it = max_it, + max_initial_update = parse(Float64, get(ENV, "OPTI_MAX_INITIAL_UPDATE", "0.05")), + line_searchmax_it = parse(Int, get(ENV, "OPTI_LINE_SEARCH_MAX_IT", "5")), + grad_tol = parse(Float64, get(ENV, "OPTI_GRAD_TOL", "1.0e-3")), + print = true) + best_ix = argmax(opt_history.val) + x_best = opt_history.u[best_ix] + opt_npv = bhp_objective!(x_best; grad = false) + + converged = opt_npv >= base_npv + if !converged + println(" Optimizer did not improve on continued historical controls.") + end + improvement = (opt_npv - base_npv) / max(abs(base_npv), eps()) * 100.0 + println(" base NPV=$(round(base_npv/1e6, digits=3)) MM, " * + "opt NPV=$(round(opt_npv/1e6, digits=3)) MM ($(round(improvement, digits=1))%)") + + # -- Simulate optimized forecast for production export ------------------- + println("\n[4/5] Simulating optimized forecast ...") + prod_base = extract_production(base_sim, producers, injectors, nperiods) + set_forecast_bhp!(case_forecast.forces, x_best) + prod_opt = extract_production(simulate_reservoir(case_forecast; info_level = -1), + producers, injectors, nperiods) + + # -- Export ------------------------------------------------------------- + println("\n[5/5] Writing CSV + summary ...") + fmt(x) = round(x, digits = 3) + open(joinpath(outdir, "production.csv"), "w") do io + println(io, "period,start_date,end_date,well,role," * + "base_oil_rate_m3_day,opt_oil_rate_m3_day," * + "base_gas_rate_m3_day,opt_gas_rate_m3_day," * + "base_water_rate_m3_day,opt_water_rate_m3_day," * + "base_water_inj_m3_day,opt_water_inj_m3_day") + for i in 1:nperiods + for w in [producers; injectors] + base = prod_base[w] + opt = prod_opt[w] + println(io, "$i,$(fdates[i]),$(fdates[i+1]),$w,$(base.role)," * + "$(fmt(base.oil[i])),$(fmt(opt.oil[i]))," * + "$(fmt(base.gas[i])),$(fmt(opt.gas[i]))," * + "$(fmt(base.water[i])),$(fmt(opt.water[i]))," * + "$(fmt(base.winj[i])),$(fmt(opt.winj[i]))") + end + end + end + + xb = reshape(x_best, ncontrols, nperiods) + open(joinpath(outdir, "optimal_bhp.csv"), "w") do io + println(io, "period,start_date,end_date,control,role,bhp_bar") + for i in 1:nperiods, (c, (label, _, bmin, bmax, _, role)) in enumerate(specs) + bhp = bmin + clamp(xb[c, i], 0.0, 1.0) * (bmax - bmin) + println(io, "$i,$(fdates[i]),$(fdates[i+1]),$label,$role,$(fmt(bhp/BAR))") + end + end + + summary = Dict{String,Any}( + "case" => args["case"], + "months" => months, "periods" => nperiods, + "prices" => Dict("oil" => args["oil-price"], "gas" => args["gas-price"], + "water" => args["water-price"], "water_inj" => args["water-cost"], + "gas_inj" => args["gas-cost"], "unit" => "USD/m3"), + "discount_rate" => args["discount-rate"], + "n_wells_producer" => length(producers), + "n_wells_injector" => length(injectors), + "n_wells_shut" => length(shut), + "shut_wells" => string.(shut), + "n_variables" => ncontrols * nperiods, + "base_npv" => base_npv, "opt_npv" => opt_npv, + "improvement_pct" => improvement, "max_it" => max_it, + "iterations" => length(opt_history.val), + "converged" => converged, + ) + open(joinpath(outdir, "summary.json"), "w") do io + JSON3.pretty(io, summary) + end + println("\nDone. Wrote production.csv, optimal_bhp.csv, summary.json to $outdir") + return 0 +end + +# Per-well rates in m3/day. +function extract_production(sim, producers, injectors, nstep) + ws = sim.wells + production = Dict{Symbol,Any}() + n(v) = Float64.(v) .* 86400.0 + for w in producers + wd = ws[w] + oil = haskey(wd, :orat) ? max.(-n(wd[:orat]), 0.0)[1:nstep] : zeros(nstep) + gas = haskey(wd, :grat) ? max.(-n(wd[:grat]), 0.0)[1:nstep] : zeros(nstep) + water = haskey(wd, :wrat) ? max.(-n(wd[:wrat]), 0.0)[1:nstep] : zeros(nstep) + production[w] = (role = "producer", oil = oil, gas = gas, water = water, + winj = zeros(nstep)) + end + for w in injectors + wd = ws[w] + winj = haskey(wd, :wrat) ? max.(n(wd[:wrat]), 0.0)[1:nstep] : zeros(nstep) + production[w] = (role = "injector", oil = zeros(nstep), + gas = zeros(nstep), + water = zeros(nstep), winj = winj) + end + return production +end + +if abspath(PROGRAM_FILE) == @__FILE__ + exit(main(ARGS)) +end diff --git a/geocode/bin/jutul_run.jl b/geocode/bin/jutul_run.jl new file mode 100644 index 0000000..4a05b87 --- /dev/null +++ b/geocode/bin/jutul_run.jl @@ -0,0 +1,218 @@ +#!/usr/bin/env julia +# JutulDarcy driver: setup_case_from_data_file -> simulate_reservoir -> +# export states.h5, wells.h5, cell_indices.h5 and manifest.json. +# The JLD2 cache written by simulate_reservoir is kept under /jutul_state/ +# for native JutulDarcy restart. +# +# CLI: +# jutul_run.jl --case= --out= [--restart=none|latest|step:N] + +using HDF5 +using JSON3 +using Dates +using JutulDarcy + +# tNavigator-specific keywords unknown to GeoEnergyIO: skip instead of failing. +JutulDarcy.GeoEnergyIO.InputParser.skip_kw!(:RUNCTRL, 1) + +# Production and injection rates are split into separate non-negative columns. +const WELLS_COLUMNS = ["time_days", "WBHP", "WOPR", "WWPR", "WGPR", "WWIR", "WGIR"] +const WELLS_UNITS = Dict( + "time_days" => "d", "WBHP" => "bar", + "WOPR" => "sm3/d", "WWPR" => "sm3/d", "WGPR" => "sm3/d", + "WWIR" => "sm3/d", "WGIR" => "sm3/d", +) + +function parse_args(argv) + out = Dict{String,Any}("restart" => "none") + for a in argv + if startswith(a, "--case=") out["case"] = a[8:end] + elseif startswith(a, "--out=") out["out"] = a[7:end] + elseif startswith(a, "--restart=") out["restart"] = a[11:end] + end + end + haskey(out, "case") || error("--case= is required") + haskey(out, "out") || error("--out= is required") + return out +end + +# Translate the restart arg: "none" -> false; "latest" -> true; "step:N" -> N. +function parse_restart(s::String) + s == "none" && return false + s == "latest" && return true + startswith(s, "step:") && return parse(Int, s[6:end]) + error("unrecognised --restart value: $s") +end + +# Extract a (ncells, nsteps) matrix from a vector of per-step JutulDarcy state dicts. +function stack_state(states, var::Symbol, nphases_idx::Union{Nothing,Int}=nothing) + nsteps = length(states) + first = states[1][var] + if nphases_idx === nothing + ncells = length(first) + m = zeros(Float64, ncells, nsteps) + for s in 1:nsteps + m[:, s] = Vector{Float64}(states[s][var]) + end + return m + else + # Saturations is (nphases, ncells); take the requested phase row. + ncells = size(first, 2) + m = zeros(Float64, ncells, nsteps) + for s in 1:nsteps + row = states[s][var][nphases_idx, :] + m[:, s] = Vector{Float64}(row) + end + return m + end +end + +# Map JutulDarcy phase to the dataset name. +function saturation_dataset_name(phase) + s = string(phase) + occursin("Aqueous", s) && return "/swat" + occursin("Liquid", s) && return "/soil" + occursin("Vapor", s) && return "/sgas" + occursin("Vapour", s) && return "/sgas" + return nothing +end + +function run_simulation(args) + @info "Setting up case from $(args["case"])..." + case = setup_case_from_data_file(args["case"]) + + jutul_state_dir = joinpath(args["out"], "jutul_state") + mkpath(jutul_state_dir) + + restart_arg = parse_restart(args["restart"]) + @info "Running simulate_reservoir(output_path=$jutul_state_dir, restart=$restart_arg)..." + result = simulate_reservoir(case; output_path = jutul_state_dir, restart = restart_arg) + return case, result.wells, result.states +end + +function export_states(case, states, outpath::String) + pressure = stack_state(states, :Pressure) + ncells = size(pressure, 1) + + phases = try + case.model.models.Reservoir.system.phases + catch + [] + end + + # Prepend state0 so the exported shape is (ncells, nsteps + 1). + p0_vec = Vector{Float64}(case.state0[:Reservoir][:Pressure]) + pressure = hcat(p0_vec, pressure) + + sat_datasets = Dict{String,Matrix{Float64}}() + if !isempty(phases) + s0_mat = Matrix{Float64}(case.state0[:Reservoir][:Saturations]) + for (i, p) in enumerate(phases) + name = saturation_dataset_name(p) + name === nothing && continue + sat = stack_state(states, :Saturations, i) + s0 = Vector{Float64}(s0_mat[i, :]) + sat_datasets[name] = hcat(s0, sat) + end + end + + total_steps = size(pressure, 2) + # state0 is at t=0, step k ends sum(case.dt[1:k]) seconds after start; + # full DateTime so sub-day report steps stay distinct. + start = DateTime(case.input_data["RUNSPEC"]["START"]) + cum_s = vcat(0.0, cumsum(Vector{Float64}(case.dt))) + @assert length(cum_s) == total_steps "dt length inconsistent with solved steps" + + h5open(outpath, "w") do f + f["/pressure"] = pressure + for (name, data) in sat_datasets + f[name] = data + end + f["/dates_iso8601"] = String[string(start + Second(round(Int, cum_s[s]))) for s in 1:total_steps] + end + return ncells, total_steps, collect(keys(sat_datasets)) +end + +# Look up the (nx, ny, nz) DIMENS from a parsed RUNSPEC block; GeoEnergyIO +# may wrap the vector in a dict. +function _grid_dims(case) + dims = case.input_data["RUNSPEC"]["DIMENS"] + if isa(dims, AbstractDict) + dims = dims["DIMENS"] + end + return Int(dims[1]), Int(dims[2]), Int(dims[3]) +end + +function export_wells(ws, outpath::String) + names = collect(keys(ws.wells)) + nsteps = length(ws.time) + ncols = length(WELLS_COLUMNS) + h5open(outpath, "w") do f + g = create_group(f, "wells") + for w in names + wd = ws[w] + mat = zeros(Float64, ncols, nsteps) + for s in 1:nsteps + # JutulDarcy surface rates are signed (negative = out of the + # reservoir = production). Split into non-negative production + # and injection columns so both plot as positive numbers. + orat = haskey(wd, :orat) ? Float64(wd[:orat][s]) * 86400.0 : 0.0 + wrat = haskey(wd, :wrat) ? Float64(wd[:wrat][s]) * 86400.0 : 0.0 + grat = haskey(wd, :grat) ? Float64(wd[:grat][s]) * 86400.0 : 0.0 + mat[1, s] = Float64(ws.time[s] / 86400.0) # seconds -> days + mat[2, s] = haskey(wd, :bhp) ? Float64(wd[:bhp][s]) / 1e5 : NaN # Pa -> bar + mat[3, s] = max(-orat, 0.0) # WOPR + mat[4, s] = max(-wrat, 0.0) # WWPR + mat[5, s] = max(-grat, 0.0) # WGPR + mat[6, s] = max(wrat, 0.0) # WWIR + mat[7, s] = max(grat, 0.0) # WGIR + end + ds = create_dataset(g, string(w), datatype(Float64), dataspace(size(mat))) + write(ds, mat) + attrs(ds)["columns"] = join(WELLS_COLUMNS, ",") + attrs(ds)["units"] = join([WELLS_UNITS[c] for c in WELLS_COLUMNS], ",") + end + end + return [string(n) for n in names] +end + +function export_cell_indices(case, outpath::String) + # cell_map[i] is the 1-based natural-grid index of the i-th active cell; + # rebase to 0 for numpy consumers. + cm = case.model.models.Reservoir.data_domain.representation.cell_map + h5open(outpath, "w") do f + f["/active_to_natural"] = Int64.(cm .- 1) + end +end + +function main(argv) + args = parse_args(argv) + outdir = args["out"] + mkpath(outdir) + + case, ws, states = run_simulation(args) + ncells, nsteps, sat_names = export_states(case, states, joinpath(outdir, "states.h5")) + well_names = export_wells(ws, joinpath(outdir, "wells.h5")) + export_cell_indices(case, joinpath(outdir, "cell_indices.h5")) + nx, ny, nz = _grid_dims(case) + + manifest = Dict{String,Any}( + "simulator" => "jutul", + "jutuldarcy" => string(pkgversion(JutulDarcy)), + "case" => args["case"], + "grid" => Dict("nx" => nx, "ny" => ny, "nz" => nz, "n_active" => ncells), + "states" => Dict("file" => "states.h5", "datasets" => sat_names, "n_steps" => nsteps), + "wells" => Dict("file" => "wells.h5", "names" => well_names, + "columns" => WELLS_COLUMNS, "units" => WELLS_UNITS), + "restart" => args["restart"], + "completed_at" => string(now(UTC)) * "Z", + ) + open(joinpath(outdir, "manifest.json"), "w") do io + JSON3.pretty(io, manifest) + end + return 0 +end + +if abspath(PROGRAM_FILE) == @__FILE__ + exit(main(ARGS)) +end diff --git a/geocode/field/__init__.py b/geocode/field/__init__.py index 4766646..1d6c2be 100644 --- a/geocode/field/__init__.py +++ b/geocode/field/__init__.py @@ -5,4 +5,4 @@ from .wells import Wells from .field import Field from .tables import Tables -from .utils.misc import execute_tnav_models +from .utils.misc import execute_tnav_models, execute_julia_simulate, execute_julia_optimize diff --git a/geocode/field/faults.py b/geocode/field/faults.py index 40e97aa..d460ce6 100644 --- a/geocode/field/faults.py +++ b/geocode/field/faults.py @@ -7,7 +7,19 @@ from .utils.decorators import apply_to_each_node -FACES = {'X': [1, 3, 5, 7], 'Y': [2, 3, 6, 7], 'Z': [4, 5, 6, 7]} +# Corner indices of each cell face for Eclipse FAULTS directions; X/Y/Z and +# the I/J/K aliases mean the + face, the - suffix selects the opposite one. +_FACE_LOW = {'X': [0, 2, 4, 6], 'Y': [0, 1, 4, 5], 'Z': [0, 1, 2, 3]} +_FACE_HIGH = {'X': [1, 3, 5, 7], 'Y': [2, 3, 6, 7], 'Z': [4, 5, 6, 7]} +FACES = {} +for _axis_letter, _axis_alias in (('X', 'I'), ('Y', 'J'), ('Z', 'K')): + FACES[_axis_letter] = _FACE_HIGH[_axis_letter] + FACES[_axis_letter + '+'] = _FACE_HIGH[_axis_letter] + FACES[_axis_letter + '-'] = _FACE_LOW[_axis_letter] + FACES[_axis_alias] = _FACE_HIGH[_axis_letter] + FACES[_axis_alias + '+'] = _FACE_HIGH[_axis_letter] + FACES[_axis_alias + '-'] = _FACE_LOW[_axis_letter] +del _axis_letter, _axis_alias, _FACE_LOW, _FACE_HIGH class FaultsNode(BaseTreeNode): """Faults node.""" diff --git a/geocode/field/utils/misc.py b/geocode/field/utils/misc.py index 26fb654..122370e 100644 --- a/geocode/field/utils/misc.py +++ b/geocode/field/utils/misc.py @@ -1,10 +1,18 @@ """Miscellaneous utils.""" -from pathlib import Path -import subprocess +import codecs +import os +import re +import shutil import signal +import subprocess +import sys +import time from contextlib import contextmanager +from pathlib import Path + import numpy as np import psutil +from IPython.display import display from tqdm import tqdm @@ -87,3 +95,153 @@ def execute_tnav_models(models, license_url, except Exception as err: kill(p.pid) raise err + + +def execute_julia_simulate(case_path, *, result_dir=None, timeout_s=None): + """Run the JutulDarcy driver (jutul_run.jl) as a Julia subprocess. + + The driver writes states.h5, wells.h5, cell_indices.h5 and manifest.json + under /result/. Pass result_dir to override that location, + e.g. when several decks share a directory. + + Parameters + ---------- + case_path : str | Path + Path to the .DATA file. + result_dir : str | Path | None + Output directory. Defaults to /result/. + timeout_s : int | None + Subprocess timeout in seconds. + + Returns + ------- + Path + The model result directory. + """ + case_path = Path(case_path) + result_dir = Path(result_dir) if result_dir else case_path.parent / "result" + if result_dir.exists(): + shutil.rmtree(result_dir) + result_dir.mkdir(parents=True) + + script = Path(__file__).parents[2] / "bin" / "jutul_run.jl" + argv = [os.environ.get("JULIA", "julia"), + "--threads=auto", + f"--project={script.parent}", + str(script), + f"--case={case_path}", + f"--out={result_dir}", + "--restart=none"] + + _stream_julia(argv, result_dir / "julia.log", timeout_s, script.name) + return result_dir + + +def _stream_julia(argv, logpath, timeout_s, script_name): + "Run a Julia subprocess, streaming stdout to the console and julia.log." + deadline = None if timeout_s is None else time.monotonic() + timeout_s + notebook = not hasattr(sys.stdout, "buffer") + if notebook: + output = display({"text/plain": ""}, raw=True, display_id=True) + decoder = codecs.getincrementaldecoder("utf-8")("replace") + stable, buf = [], "" + with open(logpath, "wb") as log: + p = subprocess.Popen(argv, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)#pylint:disable=consider-using-with + # Stream driver output (including the JutulDarcy progress bar) to the + # console while keeping a copy in julia.log. + while True: + chunk = p.stdout.read1(4096) + if not chunk: + break + log.write(chunk) + if notebook: + # Keep the driver's phase labels ([1/5] ..., "base NPV ...") + # persistently, and collapse the repeating progress bars into a + # single live trailing line. \r is an in-place redraw, so the + # text after the last \r is a line's final state. + buf = re.sub(r"\x1b\[[0-?]*[ -/]*[@-~]", "", buf + decoder.decode(chunk)) + *complete, buf = buf.split("\n") + for line in complete: + line = line.split("\r")[-1].strip() + if not line or line.startswith(("Progress ", "Reading ")): + continue + if not stable or stable[-1] != line: + stable.append(line) + stable[:] = stable[-400:] + live = buf.split("\r")[-1].strip() + output.update( + {"text/plain": "\n".join(stable + ([live] if live else []))}, + raw=True) + else: + sys.stdout.buffer.write(chunk) + sys.stdout.flush() + if deadline is not None and time.monotonic() > deadline: + kill(p.pid) + raise TimeoutError(f"{script_name} exceeded {timeout_s}s, see {logpath}") + rc = p.wait() + if rc != 0: + raise RuntimeError(f"{script_name} exited with code {rc}, see {logpath}") + if notebook: + output.update( + {"text/plain": "\n".join( + stable + [f"{script_name} completed; full output: {logpath}"])}, + raw=True) + + +def execute_julia_optimize(case_path, out_dir=None, *, params, timeout_s=None): + """Run the forecast BHP optimization driver (jutul_optimize.jl). + + The driver writes optimal_bhp.csv, production.csv and summary.json under + out_dir; consumers read them directly. + + Parameters + ---------- + case_path : str | Path + Path to the .DATA file. + out_dir : str | Path | None + Directory that receives the driver output (created if missing). + Defaults to /optimization/. + params : dict + Required CLI options: months, oil-price, gas-price, water-price, + water-cost, gas-cost, discount-rate, bhp-prod-min/max and + bhp-inj-min/max. history-cache is optional. + + Two are given in user-friendly units and converted here to the + driver's raw units: + - water-price: positive water-handling cost in $/m3 (negated, since + produced water is a cost in npv_objective); + - discount-rate: percent per year (divided by 100). + timeout_s : int | None + Subprocess timeout in seconds. + + Returns + ------- + Path + The out_dir. + """ + required = ("months", "oil-price", "gas-price", "water-price", "water-cost", + "gas-cost", "discount-rate", "bhp-prod-min", "bhp-prod-max", + "bhp-inj-min", "bhp-inj-max") + missing = [key for key in required if key not in params] + if missing: + raise ValueError(f"Missing required params: {', '.join(missing)}") + + params = dict(params) + params["water-price"] = -abs(float(params["water-price"])) + params["discount-rate"] = float(params["discount-rate"]) / 100.0 + + case_path = Path(case_path) + out_dir = Path(out_dir) if out_dir else case_path.parent / "optimization" + out_dir.mkdir(parents=True, exist_ok=True) + script = Path(__file__).parents[2] / "bin" / "jutul_optimize.jl" + argv = [os.environ.get("JULIA", "julia"), + "--threads=auto", + f"--project={script.parent}", + str(script), + f"--case={case_path}", + f"--out={out_dir}"] + for key, value in params.items(): + argv.append(f"--{key}={value}") + + _stream_julia(argv, out_dir / "julia.log", timeout_s, script.name) + return out_dir diff --git a/notebooks/Julia.ipynb b/notebooks/Julia.ipynb new file mode 100644 index 0000000..81cae99 --- /dev/null +++ b/notebooks/Julia.ipynb @@ -0,0 +1,1696 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c6b034df", + "metadata": {}, + "source": [ + "# Running Julia reservoir simulation and optimization from Python\n", + "\n", + "This notebook is a worked end-to-end example of the Julia/JutulDarcy integration in GeoCode. It mirrors the style of `Basics.ipynb`: every step shows what is being executed, which files are created, how to inspect the data, and how to visualize the results.\n", + "\n", + "We use the Egg model from `open_data/egg` and do four things:\n", + "\n", + "1. run the base Julia simulation from Python;\n", + "2. inspect the generated HDF5/JSON artifacts;\n", + "3. plot states and well rates with pandas/matplotlib;\n", + "4. run a **three-month** BHP optimization and compare base vs optimized production.\n", + "\n", + "Before the first run, instantiate the Julia project once from the repository root:\n", + "\n", + "```bash\n", + "julia --project=geocode/bin -e 'using Pkg; Pkg.instantiate()'\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "d99cf190", + "metadata": {}, + "source": [ + "## Imports and robust project paths\n", + "\n", + "The notebook may be executed from the repository root or from the `notebooks/` directory. The path setup below detects the project root and then imports the public GeoCode helpers:\n", + "\n", + "* `execute_julia_simulate` → runs `geocode/bin/jutul_run.jl`;\n", + "* `execute_julia_optimize` → runs `geocode/bin/jutul_optimize.jl`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "de9a46a3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:40:50.656093Z", + "iopub.status.busy": "2026-07-22T10:40:50.655942Z", + "iopub.status.idle": "2026-07-22T10:40:53.695968Z", + "shell.execute_reply": "2026-07-22T10:40:53.695437Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "WindowsPath('d:/GitHub/geo-kit/GeoCode')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pathlib import Path\n", + "import json\n", + "import sys\n", + "\n", + "import h5py\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "PROJECT_ROOT = Path.cwd().parent if Path.cwd().name == 'notebooks' else Path.cwd()\n", + "if str(PROJECT_ROOT) not in sys.path:\n", + " sys.path.insert(0, str(PROJECT_ROOT))\n", + "\n", + "from geocode import execute_julia_optimize, execute_julia_simulate\n", + "\n", + "plt.rcParams['figure.dpi'] = 110\n", + "PROJECT_ROOT" + ] + }, + { + "cell_type": "markdown", + "id": "2771bf07", + "metadata": {}, + "source": [ + "## Input deck and output directories\n", + "\n", + "The wrappers accept the main `.DATA` file. For this example the simulation artifacts are written under `open_data/egg/result/`, while optimization artifacts are written under `open_data/egg/optimization/`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f7b74997", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:40:53.697634Z", + "iopub.status.busy": "2026-07-22T10:40:53.697403Z", + "iopub.status.idle": "2026-07-22T10:40:53.702497Z", + "shell.execute_reply": "2026-07-22T10:40:53.702017Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'case': 'open_data\\\\egg\\\\Egg_Model_ECL.DATA',\n", + " 'simulation_output': 'open_data\\\\egg\\\\result',\n", + " 'optimization_output': 'open_data\\\\egg\\\\optimization'}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "case_path = PROJECT_ROOT / 'open_data' / 'egg' / 'Egg_Model_ECL.DATA'\n", + "result_dir = case_path.parent / 'result'\n", + "optimization_dir = case_path.parent / 'optimization'\n", + "\n", + "assert case_path.exists(), case_path\n", + "{\n", + " 'case': str(case_path.relative_to(PROJECT_ROOT)),\n", + " 'simulation_output': str(result_dir.relative_to(PROJECT_ROOT)),\n", + " 'optimization_output': str(optimization_dir.relative_to(PROJECT_ROOT)),\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "74866bee", + "metadata": {}, + "source": [ + "## Run the Julia simulation\n", + "\n", + "The simulation cell starts Julia through the GeoCode wrapper. The Julia driver exports all data needed for post-processing:\n", + "\n", + "* `manifest.json` — metadata, grid size, well names and column units;\n", + "* `states.h5` — pressure and saturation arrays by report date;\n", + "* `wells.h5` — one dataset per well under the `wells/` group;\n", + "* `cell_indices.h5` — active-cell to natural-grid mapping.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "313d6ceb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:40:53.704118Z", + "iopub.status.busy": "2026-07-22T10:40:53.703975Z", + "iopub.status.idle": "2026-07-22T10:42:20.002871Z", + "shell.execute_reply": "2026-07-22T10:42:20.001523Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[ Info: Setting up case from d:\\GitHub\\geo-kit\\GeoCode\\open_data\\egg\\Egg_Model_ECL.DATA...\n", + "[ Info: Running simulate_reservoir(output_path=d:\\GitHub\\geo-kit\\GeoCode\\open_data\\egg\\result\\jutul_state, restart=false)...\n", + "Jutul: Simulating 9 years, 44.69 weeks as 120 report steps\n", + "Stats: 518 iterations in 50.88 s (98.23 ms each)\n", + "╭────────────────┬───────────┬───────────────┬──────────╮\n", + "│ Iteration type │ Avg/step │ Avg/ministep │ Total │\n", + "│ │ 120 steps │ 154 ministeps │ (wasted) │\n", + "├────────────────┼───────────┼───────────────┼──────────┤\n", + "│ Newton │ 4.31667 │ 3.36364 │ 518 (0) │\n", + "│ Linearization │ 5.6 │ 4.36364 │ 672 (0) │\n", + "│ Linear solver │ 21.8083 │ 16.9935 │ 2617 (0) │\n", + "│ Precond apply │ 43.6167 │ 33.987 │ 5234 (0) │\n", + "╰────────────────┴───────────┴───────────────┴──────────╯\n", + "╭───────────────┬─────────┬────────────┬─────────╮\n", + "│ Timing type │ Each │ Relative │ Total │\n", + "│ │ ms │ Percentage │ s │\n", + "├───────────────┼─────────┼────────────┼─────────┤\n", + "│ Properties │ 0.4708 │ 0.49 % │ 0.2439 │\n", + "│ Equations │ 4.7673 │ 6.47 % │ 3.2036 │\n", + "│ Assembly │ 2.4749 │ 3.36 % │ 1.6631 │\n", + "│ Linear solve │ 2.5000 │ 2.61 % │ 1.2950 │\n", + "│ Linear setup │ 60.9760 │ 63.78 % │ 31.5855 │\n", + "│ Precond apply │ 1.2374 │ 13.08 % │ 6.4765 │\n", + "│ Update │ 2.1805 │ 2.28 % │ 1.1295 │\n", + "│ Convergence │ 2.5715 │ 3.49 % │ 1.7281 │\n", + "│ Input/Output │ 0.0000 │ 0.00 % │ 0.0000 │\n", + "│ Other │ 4.2487 │ 4.44 % │ 2.2008 │\n", + "├───────────────┼─────────┼────────────┼─────────┤\n", + "│ Total │ 95.6101 │ 100.00 % │ 49.5260 │\n", + "╰───────────────┴─────────┴────────────┴─────────╯\n", + "jutul_run.jl completed; full output: d:\\GitHub\\geo-kit\\GeoCode\\open_data\\egg\\result\\julia.log" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "WindowsPath('open_data/egg/result')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_dir = execute_julia_simulate(case_path)\n", + "run_dir.relative_to(PROJECT_ROOT)" + ] + }, + { + "cell_type": "markdown", + "id": "0aef0b56", + "metadata": {}, + "source": [ + "## Check generated files\n", + "\n", + "A small file listing is the first verification step. If this cell fails, later HDF5 reads would be misleading, so we assert that the expected files exist.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2686c7d1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:42:20.005108Z", + "iopub.status.busy": "2026-07-22T10:42:20.004956Z", + "iopub.status.idle": "2026-07-22T10:42:20.020901Z", + "shell.execute_reply": "2026-07-22T10:42:20.020259Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "file", + "rawType": "str", + "type": "string" + }, + { + "name": "size_kb", + "rawType": "float64", + "type": "float" + } + ], + "ref": "441c0b80-cc72-40a5-8071-637b6337436f", + "rows": [ + [ + "0", + "cell_indices.h5", + "146.9" + ], + [ + "1", + "julia.log", + "39.4" + ], + [ + "2", + "jutul_state", + "28.0" + ], + [ + "3", + "manifest.json", + "1.2" + ], + [ + "4", + "states.h5", + "52627.0" + ], + [ + "5", + "wells.h5", + "84.8" + ] + ], + "shape": { + "columns": 2, + "rows": 6 + } + }, + "text/html": [ + "
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" + ], + "text/plain": [ + " file size_kb\n", + "0 cell_indices.h5 146.9\n", + "1 julia.log 39.4\n", + "2 jutul_state 28.0\n", + "3 manifest.json 1.2\n", + "4 states.h5 52627.0\n", + "5 wells.h5 84.8" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expected_simulation_files = {'manifest.json', 'states.h5', 'wells.h5', 'cell_indices.h5'}\n", + "simulation_files = {p.name for p in run_dir.iterdir()}\n", + "missing = expected_simulation_files - simulation_files\n", + "assert not missing, missing\n", + "\n", + "pd.DataFrame(\n", + " [{'file': p.name, 'size_kb': round(p.stat().st_size / 1024, 1)} for p in sorted(run_dir.iterdir())]\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "8f15f7d5", + "metadata": {}, + "source": [ + "## Read the simulation manifest\n", + "\n", + "The manifest is intentionally small and human-readable. It is also the safest source for well names and well-result column order.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1b2f2c74", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:42:20.022319Z", + "iopub.status.busy": "2026-07-22T10:42:20.022156Z", + "iopub.status.idle": "2026-07-22T10:42:20.027222Z", + "shell.execute_reply": "2026-07-22T10:42:20.026700Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'simulator': 'jutul',\n", + " 'jutuldarcy': '0.3.7',\n", + " 'grid': {'nz': 7, 'nx': 60, 'ny': 60, 'n_active': 18553},\n", + " 'state_steps': 121,\n", + " 'well_count': 12,\n", + " 'well_columns': ['time_days', 'WBHP', 'WOPR', 'WWPR', 'WGPR', 'WWIR', 'WGIR']}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "manifest = json.loads((run_dir / 'manifest.json').read_text())\n", + "manifest_summary = {\n", + " 'simulator': manifest['simulator'],\n", + " 'jutuldarcy': manifest['jutuldarcy'],\n", + " 'grid': manifest['grid'],\n", + " 'state_steps': manifest['states']['n_steps'],\n", + " 'well_count': len(manifest['wells']['names']),\n", + " 'well_columns': manifest['wells']['columns'],\n", + "}\n", + "manifest_summary\n" + ] + }, + { + "cell_type": "markdown", + "id": "38f7f306", + "metadata": {}, + "source": [ + "## Inspect state arrays\n", + "\n", + "`states.h5` stores dynamic variables as matrices with shape `(n_report_steps, n_active_cells)`. A row is one reservoir snapshot; a column is the time series of one active cell.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "757e4244", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:42:20.028709Z", + "iopub.status.busy": "2026-07-22T10:42:20.028552Z", + "iopub.status.idle": "2026-07-22T10:42:20.042714Z", + "shell.execute_reply": "2026-07-22T10:42:20.042195Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'datasets': {'dates_iso8601': (121,),\n", + " 'pressure': (121, 18553),\n", + " 'soil': (121, 18553),\n", + " 'swat': (121, 18553)},\n", + " 'first_date': '2011-06-15',\n", + " 'last_date': '2021-04-23',\n", + " 'n_dates': 121}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with h5py.File(run_dir / 'states.h5') as h5:\n", + " state_shapes = {name: h5[name].shape for name in h5 if hasattr(h5[name], 'shape')}\n", + " dates = pd.to_datetime([d.decode() if isinstance(d, bytes) else str(d) for d in h5['dates_iso8601'][:]])\n", + "\n", + "{\n", + " 'datasets': state_shapes,\n", + " 'first_date': dates[0].date().isoformat(),\n", + " 'last_date': dates[-1].date().isoformat(),\n", + " 'n_dates': len(dates),\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "9b4b1be2", + "metadata": {}, + "source": [ + "### Example: pressure envelope over time\n", + "\n", + "For a quick numerical sanity check, compute min/mean/max pressure over all active cells for each report date. This is not a replacement for the 3D view, but it immediately shows whether the pressure range is finite and stable.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f3776316", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:42:20.044168Z", + "iopub.status.busy": "2026-07-22T10:42:20.044017Z", + "iopub.status.idle": "2026-07-22T10:42:20.063025Z", + "shell.execute_reply": "2026-07-22T10:42:20.062332Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "date", + "rawType": "datetime64[us]", + "type": "datetime" + }, + { + "name": "min_pressure", + "rawType": "float64", + "type": "float" + }, + { + "name": "mean_pressure", + "rawType": "float64", + "type": "float" + }, + { + "name": "max_pressure", + "rawType": "float64", + "type": "float" + } + ], + "ref": "51d23faa-fbee-475e-8676-44773db8e51c", + "rows": [ + [ + "116", + "2020-12-24 00:00:00", + "39609361.3351684", + "40147611.290926695", + "40517828.50913723" + ], + [ + "117", + "2021-01-23 00:00:00", + "39608836.92488274", + "40145739.74643861", + "40515517.08017448" + ], + [ + "118", + "2021-02-22 00:00:00", + "39608323.821945846", + "40143899.06331225", + "40513244.80738233" + ], + [ + "119", + "2021-03-24 00:00:00", + "39607822.47113623", + "40142094.78106554", + "40511014.25112763" + ], + [ + "120", + "2021-04-23 00:00:00", + "39607331.70694895", + "40140319.777193025", + "40508819.66622539" + ] + ], + "shape": { + "columns": 4, + "rows": 5 + } + }, + "text/html": [ + "
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" + ], + "text/plain": [ + " date min_pressure mean_pressure max_pressure\n", + "116 2020-12-24 3.960936e+07 4.014761e+07 4.051783e+07\n", + "117 2021-01-23 3.960884e+07 4.014574e+07 4.051552e+07\n", + "118 2021-02-22 3.960832e+07 4.014390e+07 4.051324e+07\n", + "119 2021-03-24 3.960782e+07 4.014209e+07 4.051101e+07\n", + "120 2021-04-23 3.960733e+07 4.014032e+07 4.050882e+07" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with h5py.File(run_dir / 'states.h5') as h5:\n", + " pressure = h5['pressure'][:]\n", + "\n", + "pressure_stats = pd.DataFrame({\n", + " 'date': dates,\n", + " 'min_pressure': pressure.min(axis=1),\n", + " 'mean_pressure': pressure.mean(axis=1),\n", + " 'max_pressure': pressure.max(axis=1),\n", + "})\n", + "pressure_stats.tail()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "75e925fa", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:42:20.064430Z", + "iopub.status.busy": "2026-07-22T10:42:20.064296Z", + "iopub.status.idle": "2026-07-22T10:42:20.409689Z", + "shell.execute_reply": "2026-07-22T10:42:20.409156Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(9, 4.2))\n", + "ax.plot(pressure_stats['date'], pressure_stats['mean_pressure'] / 1e6, label='mean')\n", + "ax.fill_between(\n", + " pressure_stats['date'],\n", + " pressure_stats['min_pressure'] / 1e6,\n", + " pressure_stats['max_pressure'] / 1e6,\n", + " alpha=0.20,\n", + " label='min–max envelope',\n", + ")\n", + "ax.set_title('Reservoir pressure envelope from states.h5')\n", + "ax.set_xlabel('Date')\n", + "ax.set_ylabel('Pressure, MPa')\n", + "ax.grid(True, alpha=0.3)\n", + "ax.legend()\n", + "fig.autofmt_xdate()\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "f13d9584", + "metadata": {}, + "source": [ + "### Example: reconstruct one active-cell slice\n", + "\n", + "`cell_indices.h5` maps the compact active-cell vector back to natural grid indices. The following example reconstructs a single `K` layer from the final pressure state. Inactive cells stay as `NaN`, which makes the reservoir outline visible in the heatmap.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9614d3f5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:42:20.411859Z", + "iopub.status.busy": "2026-07-22T10:42:20.411674Z", + "iopub.status.idle": "2026-07-22T10:42:20.418227Z", + "shell.execute_reply": "2026-07-22T10:42:20.417711Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'active_cells': 18553, 'grid_shape_kji': (7, 60, 60), 'selected_k_layer': 3}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with h5py.File(run_dir / 'cell_indices.h5') as h5:\n", + " active_to_natural = h5['active_to_natural'][:]\n", + "\n", + "nx, ny, nz = manifest['grid']['nx'], manifest['grid']['ny'], manifest['grid']['nz']\n", + "final_pressure_grid = np.full((nz, ny, nx), np.nan)\n", + "\n", + "k = active_to_natural // (nx * ny)\n", + "rem = active_to_natural % (nx * ny)\n", + "j = rem // nx\n", + "i = rem % nx\n", + "final_pressure_grid[k, j, i] = pressure[-1]\n", + "\n", + "{\n", + " 'active_cells': int(np.isfinite(final_pressure_grid).sum()),\n", + " 'grid_shape_kji': final_pressure_grid.shape,\n", + " 'selected_k_layer': nz // 2,\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "493a7a65", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:42:20.419892Z", + "iopub.status.busy": "2026-07-22T10:42:20.419724Z", + "iopub.status.idle": "2026-07-22T10:42:20.654780Z", + "shell.execute_reply": "2026-07-22T10:42:20.653754Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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ATKFYPHFbqWdftmWevT0v195vH821f8rCmueQZE5lyLJsfUTy7R/Zsok9Ey/bLt+6bKidfcd5LaPW9lgL+0cinpd4v+YN+8i6LAAA2YICEwAAZLyUXMmHMZhJo8AEAAAZLyVB6xSYSWOSDwAAAAJFDyYAAMh4nCJPL/RgAgAAIFD0YAIAgIxHTFF6oQcTAAAAgaIHs5EsLrK3b9Lc3p7jmCoXiTqyLC3Lu3Iuw44kW1eOZjjXnoMZsrX7nCLoOZZ3tZdb8kdXfzfUumxJnj38NB62t/fc/FNrOwBkM/3LFPgs8mBXl1XYdwAAAAgUPZgAACDjMYs8vdCDCQAAgEDRgwkAADJeOPzHLdB12ofGw4ICEwAAZDxOkacXTpEDAAAgUPRg+vBWT3t93iovcdu2ljbVtoV93Xn59vZIjr09HA2nLoYo6lg+1/G9xrZ+VwaFq90RwRTPdbQ34idm/oKdre2xFH5d7N2diCQA6U1/vQceUxTw+rIJPZgAAAAIFD2YAAAg46XkUpFM8kkaPZgAAAAIFD2YAAAg4+l4yaDHTDIGM3n0YAIAACBQ9GACAICMFw6FJBJwl2M4rv9lIGYy6MEEAABAoOjB9CGaY/+m1NJSvhfk2Zdt7TcHM+rIqrRMtQs7cipDjm+IkQL7YRXOjdjXb2t3TBH0HNvmRf0tH4sm/ibrOb6uxR3XHIs7lk9lzqXLnEX2DE4XcjQBpBpjMNMLBSYAAMh4KYkp4jxv0th1AAAACBQ9mAAAIONxijy90IMJAACAQNGDCQAAMl44BWMwdZ1IDrsOAAAAgaIHEwAANImg9XDQQetmdQStJ4MC04eoPc5RwpYsypbN7J3H+c3sK8/Jsy/vyrK0tUfyHTmVjnMQIVeOpiMn07a8a91exJGD6cqadHwibFmVcZ+/1/zmXMZDqfslGPYc2afmaheJLZw3xL5+y/Lden9iXzkAIO1QYAIAgIzHLPL0whhMAAAABIoeTAAAkPG4kk96ocAEAAAZLxQOfpJPiPO8SWPXAQAAIFD0YAIAgIzHJJ/0Qg8mAAAAAkUPpg8FjizKXEsOZrMW9qzJvPywrxzMiCOk05Z1GXblVPrNwcx1BIja2l05mI4Mz7hjv7nYcjTjYS/pDM0gcixd6/ezbDjueG2OYU+2nEuVW5Z4A75YPsi67I4dZthXDiAr6GUdg760I5eKTB49mAAAAAgUPZgAACDjpe5SkUgGPZgAAAAIFD2YAAAg42lmZdBjJsnBTB49mAAAAAgUPZgAACDjpess8p9++kkKCwtlq622ktzc3Frt5eXlsmDBAmnVqpV07NixXutcunSpuSldZ/fu3aWgoEDSCT2YAAAg4+kEn1Tc/Pj666+lT58+0q9fP5k/f36t9meeeUa6dOkiw4cPl169esm+++4ry5Ytc673ySeflFGjRpnbAQccIG3atJFDDjlEfv31V0kX9GD60LKlI2syJ/GBmd884ivnMic/+ZxLV9ZlynMwCyJJt7tyLr08Rw6m5T0xy7uyKq05mPZlyyI+czJ9fB2MhXw+t8/fFIX59ucvaxNL2LagZe1v/FX9K36StX1JrIO1/anoddZ2AEhGSUmJjBkzRvbcc09566236iw+R48eLbfeequceeaZsnbtWhkxYoQcd9xx8sYbb1jXffbZZ5tbBS0stTg99thj5d13302LN4weTAAA0DQuFRkO+OajA/OSSy6R1q1bm+KxLrfffrtsvvnmle16ivyyyy6TN998U2bNmrVRz9W1a1c55ZRT5P3335f169dLOqDABAAACNCHH34oU6ZMkfvvv19CobqrVC0Gd9ttt2r37bHHHpVtG2vlypXSrFkzyc/Pl3TAKXIAAJDxghgzWXudf/y7ePHiavdrb6Pe6qITesaOHSuXXnqpGX/5/fff1/m4RYsWmZ7HmuvVIlHb6kPXXVRUJDNmzDA9ojfddJPk5KRHaZceWwEAAJCmBg8eXO1nPZV9+eWX1/nYCRMmmFPj559/vnOMZjQarXW/zgrXtvrQQnbVqlWmINXxmyNHjpR0QYEJAAAyXiqD1qdPny6dO3euvD9R7+XMmTPlvvvuk4cfflh++OEHc9/ChQvNv3PmzDFRQptttlnlOrS3syrP82TDhg0J11+TbpdavXq1nHTSSTJkyBDzvDqrvLGlTYG59dZbmyyoqrbddlv597//Xe2+WCxmZlw9/fTT5k0YOnSoTJo0STbddNMG3mIAAJANtLjs1q2b83Ha86i1yzXXXFN537p168y/2qOpcUR33323+VlPn8+dO7fa8toTWVpaajIzN4YWlNqjuv3228t7771nIosaW9oUmLqTr7vuOjn00EMr78vLy6v1uHPOOUeeffZZM3BWi8qLLrrIDIr98ssvzbiFhtSshSMqKBpOOoYo7Ij6ycm3v3URR9RQpEVu0jFDEvEbQ2TfNlsUkSuGKFZg37aY44gvy7PH6cRyEreXW9qCiDFysUYohfw9d5l9t0uZY9xTmeO1r4smfmMWRtpZl10da2ltX1He1trer/BBa3txLHF48Zz2o63LAmg4oXDI3IJd58Y9ftCgQfLtt99Wu++FF16Qww8/3HSYaWdahYMOOkhuvPFGU4C2bNmy8rFa++yzzz6Vj9PeyV9++UV69+5dWRdpEVoztL2ipzQdei/TqsBUWjBuueWW1jT8u+66y3Q9H3jggea+xx9/3Hyr0ILz//7v/xpwawEAAJJz9tlnywMPPGDC0i+++GL5+eefzcQg7Thr37595eO06Bw3bpx888030rdvX3Of1kA65lLHhmrnmnayXXHFFea+mjPTG0tGxRS99tprZnzCwQcfXHmfvgm77LKLvPLKK426bQAAoPEEnoEZ0JjOVq1ayXbbbVfrrKxOBNI4ox49ephiU8du6ixwLTKratu2rVm+avyQDhPUIYPXXnutnHvuuTJt2jS54YYbTJ0UDnogalPowbz66qvNuAUd6zBs2DA577zzqp32nj17trlOZ0VXcgW9vJIGk9poQr7eKtSMHAAAAJkrHEpBTFEAqxs+fHit0+YV9DKRembWRocOVh0+WHEafOLEieaWrtKmwNSZT3qKWwe9aoK9dhc/+uijZoZUixYtzGN0nELz5s1rLavtFYNoE5k8ebKZDAQAAIAsKTD12pmRyB8zCXbYYQfp37+/6RLW8Qk6sUdp97AObK1r1pYruV5zqU4++eRqPZg1c60AAEBm0gk5wU/y8Tf5MpulTYFZUVxW0Gn+Ounnu+++q7yve/fusnTpUhNnVDWpXi/yrm02ttR9AAAABCc9RoLWYc2aNbJ8+fJq+ZZ77rmnlJWVyUcffVR5nxab+nPF9TsBAED2Bq0HedvYmCKkWQ/mM888Y2aH6yBWzXXSwlIT6fX/x4wZU/k4DVXXGeM6hV9nSunYyyuvvNIk4Z955pkNvt3RZvZwQNtg44gl69G05zrWbcnYNO2OrElbFqUzx9KxbeLI0bTlXJr2Zom3LdbC/rrKc+2nR8odOZel+fb24ry4jyxJfzmZrixLP89d7PhNUBKxP3mxIxu1uMYZiprWhmtn3lZYIfYzD6vj9hzM1WVtks65VKXlids7L7RPLvSK7du2ZKsh1nYAyFRpUZvvvPPO8vLLL0uHDh3MjCq9+LtO2tE0eg0WrVmM6qWWdKa59m5OnTpVXnzxxcpLLwEAgOwNWg/6hgzuwdTxkw899JA8+OCD8vvvv8smm2xSbYxlVVpYaiSRXty9qKjI/BwKcQAAAACki7QoMKtO9NEezPrQ4FG9AQAApCYHkw6sjD5FDgAAgKYjrXowAQAAks/BDHbfMYs8eRSYAAAg44UjIXMLep1IDqfIAQAAECh6MH2IOLIoQ5ZvPn5zLiOOnEtnDmaLaNI5mJ5j21w5mHFLzqVptzx/aTN/OZfFzZLPuVQl0cTLlzs+Ta6cS1dWpYstJzPm+BJe5hgYX5hjPyY2ROwvvjRkX355KHFe5Ip4a+uya8vtOZmF5S2s7SVlza3tZaWJczC9kmbWZSPF9nV3/+IHa/uiHbe2tgP4n1TEChFTlDx6MAEAABAoejABAEDT6MEMeMwkPZjJowcTAAAAgaIHEwAAZL5UXNqRS0UmjR5MAAAABIoeTAAAkPHIwUwvFJgAACDj6QSfwCf5ELSeNApMi9XHtfOVVRmOhJM+aMOOLElXzmXYknNpnt+SNek1z/GVg+nl2TMP43n25cvyE++b0gJHjqWrPT/5nEtXVmW5/WVLLOwlnWNplvfxe7PMsW3FOfaVr4vk+sq5XBfKt7avkMRZl8VennXZDXF7FuWGUntOZlmJPSfTlnXpyrkMl0d9tW82fZ61fcHgXtZ2AGgsFJgAACDjhULBxwrpOpEcJvkAAAAgUPRgAgCAjMcYzPRCDyYAAAACRQ8mAADIePRgphd6MAEAABAoejABAEDGC4XDErLEAya3Tnu8HBKjwEwhW9alK0ohlBvxlYMpjnYvP5KyHEtXTqYt51IVt0z8gS5sbs+xLCzwl3PpyqK05WD6yak0z53C8wlljuOtJBxOac7lSrFnUa6JJ86iXBFra112bWniDM365FxKoX35qCXrMqfE/rpDPt/UWLTM2t7tq1mJl80tti67eNudkt4uAHChwAQAABmPMZjphQITAABkvhRcKlLXieQwyQcAAACBogcTAABkPJ3bEHQPZtCXnswm9GACAAAgUPRgAgCAJjLJJ+CYoog9mQSJUWA2Elc3ftgRUxRyxBC51u9Z2l0xRLECR3uO/bnLCuxRQbYootUtYtZl1zrW7YrrcUnnSLR4KPFrK3McD64YoqJQrrV9g9jjetZ49qig1fHEMUZLSja1P3epPQJJilpam3PXt7G25xU1S9gWKYuKH/Gwvz9e5fkbErZ5UXtMUecF06ztizcbnvR2AQAFJgAAyHjEFKUXxmACAAAgUPRgAgCAzKdDoILOrWQWedLowQQAAECg6MEEAAAZjzGY6YUCEwAAZDyNKAo+pogTvclizwEAACBQ9GBaeHGv0S4hFcoN+2p3DnS2bLsXta+7PNe+7vI8+34rbmZvL8pLnA24xB5pKAvz7Q8o8Mqs7S3L7e35scTbFm3kPN5YKLk2VebIwSx3fBddLwX2di9xlqRaWtYhYdvastbWZctK7BmbUUfOZbO19vXnbUic8RmO+/uOXppXam33Ivbc1xJbY479WA5F7O3dfnvF2h4O27dtYadDrO1A4Jjkk1bowQQAAECg6MEEAAAZj0k+6YUeTAAAAASKHkwAAJD5dO5B0LO+gw5uzyL0YAIAACBQ9GACAICMp8kuOg4z6HUiOfRgAgAAIFD0YFp4MXteo5/lg/6WVYtj/basy5jPnMuSfHt7adTeXmhZfmG+PfPw+bK9rO3b5/5obd8q+qu1vb0UJ2wLe/YgzIi/w8kpbnnb4mJ/T2Mhe3uJRK3thZ49B3N1zJ5Puq48cXtJWXPrslJoz7HML7QfM83X2NefuyE3YVvYkX3qiskMOwJKY9Fya7tnyaJ05VyGw+W+ci4jYfv6t175SMK2H9odb10WSH4MZsB/WxmDmTQKTAAAkPG4VGR64RQ5AAAAAkUPJgAAyHxcKjKt0IMJAACAQNGDCQAAMl+aTvJ5+eWXZfXq1XLIIYdIq1atarX//PPP8vnnn0vr1q1l9913l7y8vHqtt6SkRL788ktZsmSJbLHFFrL99ttLOqHABAAASIFXXnnFFJae58n3339fq8C8/PLL5frrr5d99tlHFixYICtXrpRXX31V+vbta13vLbfcIldffbV07NhRevfuLR999JH06dNHnnjiCenatWtavJecIgcAAE2nBzPoW5JWrFghJ598sowbN67O9jfeeEMmTZokL730krz44oumN3LAgAEyevRoU5DaPProo3LllVfKrFmz5IUXXpDZs2ebnszTTjtN0gU9mCnMwYxbQvKc6051u4/svpjjqIlF7O3lOfZti4UTty8IbWJdtvBfl1rb3z/+Zmt7l2YrrO1tQiWSiVw5l6Uh+5taKPacy/VeM2t7iWc/5VMUS7z+shJ7jmVOsT3HssUqe05my6X51vZIsY/PUtT1xylxxqYqbF2YfA6mI6Qz4sjJzImUWtuj4eTbB6+fYl12eos/W9uBTHDGGWfI8OHD5eCDD5YHH3ywVvv9998vO+20k4wYMcL8HAqF5C9/+Yvsscce8sknn8jQoUMTrvuee+6RHXfcsfLnNm3ayJ/+9Ce57bbbJF3QgwkAADKffpEOB3xzfDlP5LHHHpN33nlHbr311oSPmT59ugwaNKjafRU/a5tN1eKy6ljOLl26SLqgBxMAAMBi8eLF1X7WsZR1TdhRv/76q5x11lly5513SocOHazr3GST6mfl8vPzpWXLluZ098b49NNP5ZlnnpG//vWvafM+0oMJAAAynxkzGQ749kcP5uDBg6V79+6Vt8mTJyfcjJNOOsnMBh81apR1c+PxuITDtcswva+83H4p16oWLVpkTo/3799fLr3UPkysIdGDCQAAMl8KY4r0lHXnzp0r707Ue/nxxx/Lf/7zH7nuuuvkkUceMfdpBJHSyTy//fabGZep2rZtK2vWrKm2fCwWk/Xr10u7du3qtXnLli0zM9B1e1577TXTA5ouKDABAAAstLjs1q2bcx/pZJvjjjtOZs6caW7ql19+Mf++//775t+KArNfv37y3XffVVteZ4NrkemKKVKarbnvvvuax0+bNs1EFqUTCkwAAJD50uBSkdtss01lz2UFjRF69913Td7l1ltvXXn/UUcdJeecc44pQCuK14cfftj0XlbMLFfz5883OZc6G13D2FVhYaEccMABsmrVKnnvvffSanJPBQpMAACABnbSSSeZ2ebaC3n22WebWeAaoP7Pf/5TmjX7X/SbFpCapfnNN99UFpialalRRhrUru1VHXPMMRKNRhv9/cz6AnPFyVtLXkFyc51cS3kRH/l5pYnz7VSoyD4AONLCcXDFE2+bIz7PyZVzWebYL5F44m+MO5YtsC7768n/Z23fLFx9JmBN7eL23MHm5YnfF9fbHXYE57rEHXEZMcs3bcsurZdysYeblnv29sKYIyfTkoPpldiXbbbaPlap5XJ7hmfucnueo6yzHBP59nzPSDTHVw5mpINjecuHNSenpNFyLlVBTlHCtmbhDdZlgaZ0qcju3bub0+YVxWEFLQLfeustU1DOmDHDjKP84IMPakUX9erVyyyvp98r9OzZU4499lhzSl1vVR1xxBEUmAAAAE3ZTjvtVOu0eYXc3Fxz9R3bFXh0RrreqkqnQPVEsr4HEwAANAEV4ehBrxNJIQcTAAAAgaIHEwAAZDwvEjK3oNeJ5NCDCQAAgKZfYGoC/pAhQ8x1POsydepUkwelOVEXX3yxrF27tsG3EQAApBFN2ggHfHOkdyCDCkzNddIw0q+++koWLlxYq33ixIly7rnnypFHHikXXHCBvP3227LXXntJaakjagQAADT5U+RB39AExmBu2LBBTjjhBLnmmmvkr3/9a612TbvX4vPuu++WsWPHmvu23357kwf1r3/9S0499dSNfk4v9sctGXGx5xqGYonb42X2sMlwqb09vr7MvnyB/a0Ntbfn91mfO8VfS3Is78emG+z5n4cVTLe2hx3vWX65o93ynvoVa8TfYxHPfrzlhOwfkmLPfjyVONrLY4nzIEPl9kzXgvX2nMzcNY737Jdfrc2xFYnbIx172NfdsqW1OSdqzw8NOz5sniXLMhK2/47IiyTOqVS5jpzLXEeOpi3rsl3OKuuyJ5Sfa22fmnOLtR1A40urHkztkdTLJZ1yyil1tr/66qtSXl5uQkQr6OWRhg4dKi+++GIDbikAAEi7iiYVN2R2D+abb74pDz30kHz99dcSSjDm4YcffpAOHTpI27Ztq93fu3dveeedd6zr13GaVcdqLl5sv6ILAAAAMrjAXL16tYwfP16uuOIKc0mkRLRAbFnHKSe9vJJros/kyZNl0qRJgWwvAABIL3rF2uBjigJdXVZJi87fc845R7p27Wom79joJZX0FHlNOsFH22wmTJggixYtqrxNn24fqwcAAIAM7sF89913JRwOyy677FJ53/r16+Xhhx+W//73v2bsZbt27UwR+vvvv0ssFpNIJFLtdLe22Wgvp94AAEATjikKep3I3ALz+eefl5KSkmr37bPPPibn8swzz6w8Lb7rrrua3srPPvtMdt55Z3NfPB430UZHH310o2w7AAAA0rDAHDBgQK37tIdSZ4hr4HqFPfbYQ/r37y+XXnqpvPTSS+a0+O233y7Lli2T008/Pann9uKeuSW3rOsRiR8Qd8UQRR3ZSY5xJvEie5xPuDjx+sOOqJ6IfdUSduyXqI88nohnXza/3P7ksbDjtcXt64/5GFRS5lrW0e7YNF8ilmNV5Yj9eCx3DFQqtcQQ+ZVT5oj6WWOP44ktmWdtX/FB4rHb7YddY102Et3S2h4qs+/XnDLHr+icxFFEOY4YoQJXTJFj+bxQ9U6BmlrlJB4X3yayzrpsm5C9/SrvcGv7xNDz1nY0TV44ZG5BrxMZXGDWl55Gf/bZZ+VPf/qT9OjRQ9q0aWOKy8cee0z69OnT2JsHAACAdC4w33rrLdl0001r3b/FFlvIl19+KXPmzJGioiJTWOblJR8aDgAAMp8X/uMW9DrRxArMgQMHWts1+xIAAEBRYKYXanMAAABkRw8mAADARk3yCTponUk+SaMHEwAAAIGiBxMAAGQ8xmCml6wvMONxz9xSIWTJe4w58u8iMccFUGP+tjlUljj3MFJkz0SMNrNvW06+I6PTkaNp44ixlNwyx3OH7CuIu7IoLadLyiKujE37ul3LxxzbLmI53kL+TlbkiT0TsUV4g7U9ErYf755lqmYobj/eyh2ZsV6LAvu2dd/G2t5x/9sTb1ubjtZlpXkz+7ZF7a8tHk6cc+kSDbtyMO3vWZ5j+daRxDmXrqzLFiH7c7cOrbdvmyS/XwA0jKwvMAEAQBOQgpgiBhImjzGYAAAACBQ9mAAAIOMxBjO90IMJAACAQNGDCQAAMl485J6omcw6kRwKTAAAkPE4RZ5eOEUOAACAQGV9D2a83JNYWWpyMEUSBx+Giu1LRhzZfuHcsL+czFLLtpXYnzun1J7dFy11ZVHaN8363MkvWs+cS3t7zNIe9plzacuxrE8OZtQSEhp1bZzPr5quHMyWlkxEtSIn8QeizJGhGXcEjBZ1sO/XAulsbQ91aJ90jqU42kva2Xd8UQv7frXJdeRYNnfkYDZ3vKe2nEtX1qUr57KVFFrbW3r2X6BPxXaxth+d85G1HZlJf38HfoqcbriksesAAAAQqKzvwQQAAJmPMZjphR5MAAAABIoeTAAA0CR6MIMeMxn4pSezCLsOAAAAgaIHEwAAZDwv5IlnSdNIdp1IDj2YAAAACFTW92DGNAezvO4cvXDY3zWiYmW2Vnt2X06+I4syZn/rvJh9/V5RecK2UGHiNhVpYX/unAL79xbXGBnbF1D/l+1yZHQ6vv2W59ja/W6c/bkjjshFW8xm1LMfD7me/XjLC1kPZskXe+Zi68ha+/ojRQnbCvPseYwbWtnzGMO28FJ9T/Pzre05xXmJ1x33dzwVtitytC+ztoejJZKsvHCJr2xTW86lah5K/Npa+8y5bB63H2/NYvbfYe8WDUzYtmfLz6zLIn2Rg5lesr7ABAAAmY8CM71wihwAAACBogcTAABkPB2S4hqWksw6s8Ebb7wh7777rvz222/ief97zdttt51ccMEFSa2TAhMAACBLTZw4UW699VbZYostTIGp/3799deSk5MjnTt3Tnq9nCIHAAAZzwv9bxxmUDddZ1O2evVqmTx5snz88cfm3/79+8v06dPlu+++k44dO8ruu++e9LopMAEAALLQnDlzzGnwfv36STgcltLSPxIaevXqJRdddJE89dRTSa+bU+QAACDjMQZz461Zs0batGlj/r9Dhw6yePHiyrZNNtlEli2zR6XZZH2BWVbmSVmCftywY3BvJCeUdPewI3ZQ4qX23MJ4mSPn0rG8xBK/tpBj3ZH19oy5/Jyo/bkdeZHlucnmUPoX9nE+JOzY5RHHp82VDxp1HDNRSxBmrnNZ+8bnif09bx1ab21vE7FnVbaIJl5+Tb593Rva2n8BxsP2F1/Uwp6DGS1JfECGHW+aK4NzbfuV1vby5qut7bk5ibMsIyFHlq7Y2/ND9pzMvJAj+1QSv2/NxL7uAq/MV25rfrn990Qzy+q/WjbIumz/jjOs7UCm2nLLLWXp0qXy5JNPym677SZ33XWXbLXVVkmvL+sLTAAAkPnIwdx4Os5yyJAh5v+bNWsmV155pYwePdrMJNcJPlpkJosCEwAAZDy9sJb/q73VXmdTtsMOO5hbhf/7v/+TQw89VBYsWCADBgyQli1bJr1uJvkAAABkkRUrVsj48eOlS5cupudyp512kldeecW0bbbZZrLHHnv4Ki4VPZgAAKCJnCIPOmhdmqQTTjhB3nvvPTn44IPNJB+NKTrkkEPkiy++qNaj6QcFJgAAQJZYvHixTJs2zYSpV0zi0TGXxx57rDz44IMmdD0ITbQ2BwAA2YSg9fqZP3++GV9ZdYZ4KBSSUaNGmbagZH0PZmlJXEoSRNOUltm72tu0te++sI/yPeZ47vIie2xMTqk9xsOzxRRZ2uoVY1Rof+7cHNdh13ijql0xSDmWbSsXx7Ll9teV6zheYiH7+vPKk1+2Wcx+PLVwRNaUhAut7W1C9piitjmrEratyLMvu6FlnrW92NoqUlZqjykKxSMJ2yJl9kiueMT+WShpY49YijRba22P5iR+dbkRe4xQjiPGKN8RQ9RCiqztzS1RRAWefd1RzxGbFbe35zt+h7UoDmfdKVGgQlFRkRl3WVPz5s1NW1CyvsAEAACZLx7yzC3odTZFixYtkltuuaXafbNnz651f48ePeSII45I6jkoMAEAALLI7Nmz5bzzzquzrer9I0aMoMAEAADZSy+c5bh4VlLrbGqGDRsm69bZhx5VyHEOabMsm/SSAAAASMjzPLniiitk1apVctFFF5nre1cVj8fl+eefl88++0xat24tf/rTn6R37971XrfOBn/ttdfMqWwNSa+PSCQiLVq0SPm71gRrcwAAkK2Xigz65sdtt90m11xzjYn+WblyZbW2WCwmhx12mDklrZNu9LT19ttvLy+//LJzvZ9++qn06tVLrr76annppZfkqaeeknRDDyYAAMh4Xgom+eg6k/Xjjz/KpZdeKhdeeKFcfvnltdofeeQR0/v43XffVfZa6tVzTjnlFPn5558lLy9xQsamm24q77zzjrnqzkEHHSSrV6+u93bpcvvvv3+9Hjt8+HB59dVXJRkUmAAAAAEqLy+XMWPGyNlnn53wyjiPP/64uSRj1VPiJ510kun1/O9//ysjR45MuP6ePXsmvW3ac1pSUmJyMHWGuMYTJbL55psn/TxZX2BGoyGJ5tadT5iXH/aVcxkKJ849DEX8ZT16cfu3Ks+RgykxS46cI0NOSu0ZdKGoIyez2N6eY9mx4Zh9v8Wi9m13ne5wZVX6OV3iiO5zXuKsPJL8tuU7voWXOZ67edieW1gUtudBNg/Zs9VaRxIPOG+TW/20Uk1xS06lKnbkPZaV2XMwJRZNKiNTeVF7Cmckf721Pd+RAZoXSbxf8xzZpS3CG+zrduRg5om9PVcSZ6vmevb3JOr4sEQcv6IcvyakxPF7wuarZYOs7f07zkh63Wg6k3yuvvpqWb9+vVxyySWml7IuX331lQk3r2rbbbeVcDhsrrJjKzD96N+/v/z1r3+VRx99VO644w5TZI4dO1b22msv89xBYQwmAACA4/KKv/zyS+Vt7drEF0HQ63lfffXV8sADD1hPc+uYzLZt29aata2nyZcvX56y96NDhw5y3XXXycKFC+XZZ581PZp6TXLtFdWJSD/88EMgz0OBCQAAMp72e8dDAd/+/7oHDx4s3bt3r7xNnjy5zm0oLi42p8bPOOMMGTp0aL1mgtd1X5A9iYnoc+y7775mLOiSJUtk0qRJ8vHHH5teVD1V71fWnyIHAACwmT59unTu3Lny51atWiWc3a2TdnbZZRc599xzzX3z5s0z/+pscr3/tNNOMz9rZFHNnsrS0lKTUVkzzijVdBa7vj695ebmWnto64sCEwAAZLx4CsZgVoxt18KrW7duzsdrdNDNN99c7b6KUPOuXbua2d8VBg4caE6nV/Xll1+aHsyddtpJGsI333wjU6dONeMxCwsL5aijjpI333xTdt99d9/rpsAEAAAIgBahFT2XFV544QV58MEH5YQTTpCtt9668v4TTzzRTLDR3lE9Ba9uv/12U6TutttulY/T9scee0z+/ve/S6dOncQvLXh1fKgWljNnzpS9995bbrrpJpPJWVBQIEFJusDUblztRk1k2bJl0rFjx2RXDwAAUG8V4yaDFPT6qtKCTsdq6jhIvYKPZl/qzPJXXnnFXG2ngp5y16D2k08+ubLA1AlCeoWginaNHaoobF2FqJ7G13B3jSnSnM4uXbqY3kvtxaxJe13rm5kZWIGpg0J1XIG+wKo7QmcjaaDo77//Lvfee2+yqwcAAMh4/fr1M6fNq54erzBlyhQZP368zJgxQ0aMGGGKuZozy7V3U5evOgZUZ5tXZGHWvERkNGqPjaugVw667LLLrI+p2KYGLTD79u0r559/vnz00UcmLFSrZS0qR48ebcYUPPHEE5IJcnLDCXMwXZO4Ijn2B0Siib/6hC0ZmSpkj9cTz5FV6Wy3ZFl6ReX2bStwZP9ZW0XCZfZH5JQmbi8X19dJe7tjtzpzNEPWt9yxbTmerwzOvBROKoy5cjIj9lzCgnCZtb15uCjpTMY2UfsVKmKe/V0Nhx05mOX2HMyYJQfT8xy/A8L2z1JebqG1vVmOPSezICfxfm2VYx+kn+/Iycx35FzmVM6vrVvES9wermPmbPVlHe2OHGBXTmaZ5QFRV4gm0pa+dUG/fUGsr1evXrVOm1el4y1tYy51VrfeqtJJRrZ12gwbNqxyXKiLFrLJSnpJrag///xzMyB0wIABMnHiRLnqqqtMha336w4FAABoCEFcO7yudTY1kUhEWrRokfLn8bXrtthiC/nggw+kTZs25nJIPXr0MD2aFJcAAADZy1eBuWrVKjn66KNNqr1enF1PjWuXrQ42BQAAaCg6IScW8C2Vk3yauqQLzO+//96cGp8/f74ZnKoTet5++23597//bdLrK4JFAQAAkF2SLjA//PBD2XXXXU0+U0Wu0x577GFCQnUGlCbWAwAANITALxNJD6YvSU/y0cwmzWSqSafhawq8Fp4AAADIPkkXmBU5TTr+UsM5586da6bZH3LIIeb0eBCXGQIAAKiPmIQkFgoFvk40wqUi77nnHjOpRy+NpFf10etnjhkzRsaNG2eS4tu3by/pLhoNSTQ3nFQOZihiP/BsWZchRw6mOyfT30HvlcaSysg0z+1qd2VwOjLsQuVe0gesOyfTLuQY0W2JRKxHAqjPX1T2WEKrcNxfuy3TUBWH7RuX58jJbBFKnIPZOmLPc4xF7TmYkZA9B7MoYr80Wlk88RXL4o4Mzqhjv+RHipLOuVStLPvGli2qmoeKfOVgRsS+X+3L2j8rYVe766PmYPuYuyZ1uJ77q2WDrO39O86wrwBIAzq3RtOBNCR+woQJss022zTcGMxFixaZJ9VrWc6ZM8dcdkg1b95c9ttvvzovOQQAAJAKjMEMTjgclvz8fBNFqRfW0XqvwXow33//fRk+fLgJWq9Jr2/5448/JrtqAAAANBId8vjf//7X/P+KFSvMddIbrMDUJPjS0rpPoaxevbpBUuIBAABUPBT8GExdZ7Zr3759UkMekz5FvvPOO8s777wjr7zySrX7teh86qmnTGQRAABAQwg6ZD0V1zZPV9OmTZPDDz/cXPNcU4L0Qjp6CXA/ki4we/bsKaeffrocdNBBsueee8rjjz8un332meyyyy7SsWNH2X///X1tGAAAAFLrsssuMzVbs2bNTOfhmjVrTFLQ119/LW+88UbjzCK/9dZbzcyiBx98UBYsWGA2SCvgSy+91AwQra/FixebS02qUCgkm2yyiQwZMkTGjh1rZqfXpL2mzzzzjGzYsMFcNeiMM86QvLw8Py8FAABk/CSfoE+RS5O2bNkyufHGG81knkGDBslbb70l1157rWk78MAD5cknn5R999234QtMLQa1uNObH61btza9oUqjjnQw6dVXXy3333+/vPfee9WKxxtuuEEmTZpkbhrqrjvi+eefN927Oi4UAAAA9bvs93bbbWeKy5q6d+8uL730kiTLV4EZFO2W1VPtVemp9oEDB5prm1fMVP/999/lkksuMUWm5jMpvVxl7969TSzSCSecsNHPHc4JSSSa3FcUZ1alpd31nL5zLl1ZlLb2mCMU0bFuceVcOpYPlyVuj/s8oOM5jv3qeIKQtd31nqU2JzNcnHj5XEd4X16Zv3ZNILXZ0GqdtX1pOHEmY5uIfdkcR85lXrjE2l4St5/9KLdkXZZ6eb4yOPNCJb62vbkl67JNeF1Kcy5znJ9GoGExyWfjtWnTxsROlpWVSTRaPehZO/u0I69BCkyNHtJ4ovrQ65PvtttuyW6X9OnTx/z722+/Vd736quvSklJiYwaNaryvs0339yMGXjuueeSKjABAACyUd++fc1QxPHjx8ttt91Web9O8pkyZYpcddVVDVNgfvLJJ+bKPRXKy8tNwWdWlJNjfq74/9NOO81Xgak9klpNa2h7hVmzZpmp8jqJqGYxq6fSbdauXWtuVcd9AgCAJnSpyIAv7djULxUZDoflgQcekEMPPdSMt+zcubOplXr16mXGXuq8mgYpMHXSjd5UPB6Xvfbay1S/F154oTlXrxXvQw89ZAaMXn755Ru9MXfffbc53//rr7/K0qVLzeylip7MinzNVq1a1VpO79M2m8mTJ5txmwAAAPjD3nvvLd9++62ZsD179uzKKzIeeeSRZq5Ng4/B1IT34uJiufPOOyvv01nk2sM5b948ue+++0zhuTF233136dq1qzktrhN8zjrrLPnPf/5j7jMbm5MjsVjtcUF6X82xAzXpZY5OPvnkaj2YgwcP3qjtAwAA6TsGM/hZ5E27B3PatGly3XXXmVrryiuvDHTdSReYv/zyi/To0aPOts0220zmz5+/0evUmUx6U2PGjJEtttjCnP+vKGK7dOliejZ1pnnVqnrJkiWmW9dGeznr6v0EAADIRm3atJHly5enZN2+gta18v3pp5+q3V9UVCSPPfaYmXzjd2Z5t27dTCFbQbMxtddUwz8raLH56aefmjYAAJCdYhJOya0p69u3rxlz+cUXXwS+7qR7MPV0tsYI7bDDDnLMMceYglMDO5999llzurrq6WgXDfbs1KmTeaEVXnvtNVNInnjiiZX3DR8+XLbaaiv5xz/+YS5HqYNTp06daorQU089NanXEckJm1syQpHkY4xcMUTOdkdEUkojjkrt8SVS6ogviYaTjjkKlzkWtTf7/lURsq7A9Z6kNsYo4mPZPHsajhQ4Yo7ijh3bupk9Eqd97v8m4NVUHra/snxH1E9OyB6hVO7lJB1TVO7Y6zmuqB9HjJFr21uEEscUtQ6t9xVT5Iohijjac73Ery3iOdbt+Ci42htT2PFLaNYS+9Cs7TpND3aDsggxRRtv4cKFpn7TyEed6KNnpqteKEdjIE866aSGLTD1FPXLL79sTl/rzCO9uo7mJWmE0EUXXWTGY9aXzgzXAnHFihXmxekkHx2H+fe//13OPPPM/21sTo65gs8hhxxiriCks8m//PJLueeee0yhCwAAgPrR0+M6J0VnjetEH71VpVdLbPACU2l2kk7qqRpdlIwBAwbIRx99ZE6369jNDh06mKpZT5PX1K9fP5kzZ47MmDHDnI7XZTemmAUAAE1PXMLmFvQ6m7IhQ4bUKiqDkhZX8qmg4zbrM3ZTezK1qgYAAED68VVgahbm66+/bq7wUxG4XkFPWe+///5+tw8AAMApFtJbwEHrTTulKD0LTC0u99lnH3nnnXfM2Mu8vOrX5NWxmBSYAAAA6UkzzQ866KCE7cOGDTMXwGnQAlOvST5z5kzTe6njJQEAABpLPAWxQk19DOZWW20lt9xyS7X79Iz0u+++a8LX9bLfyUq6wNRZ3np5IYpLAACAzNOlS5c6YyX//Oc/yymnnFIti7zBCkwtLDVOKNNplqUrzzKZnMs/1u0jB9Ox7nDE37cqL+ZKjLRw5GSGyhzrdrVbeI4MTUesoJuPfFFX/l3EkeHpNyfTs+yakGPbcovsz12wxL7xJdvbP0TNy+xvTLPcxFmWbcSR5xiy5znmh+3t670CaSzOHExHjmbzUFHSOZd5UuarPdcrTzon0xGr2qhc2+b8nHuO390+fvXCLhXB6E09aN3mgAMOMNnmp59+uiQj6T230047mdncd9xxh7m6DgAAAJqGn3/+WcrKnD0jwfdg6hV0NLtSJ/mcc845UlBQvRdAr8CjxScAAECqlYci5hb0OpuyRYsWmV7KqvQS3JpJ/uCDD8qUKVMavsDceeedaw0MrUqvtAMAANAQ4hJKwSSfUJPvpbz22mur3aeXitSxmVdeeaWMHTu24QvMrbfe2twAAACQeXbffXdZsmRJ07+SDwAAQDLKJWxuQQp6fdlko/bc/fffL6FQyExpr/j/RLe6pr0DAAAgfXz99dfy1ltvVf78l7/8Rbbddlu56KKLzHjMBunB1ET3hx9+WLbcckvp2LGj+f9E9DEAAAANIS4RiUkk8HU2ZfF4XE444QR58sknzc+vvvqq6UDUDEyd5LPjjjvKkUcemfoCs1evXuZW9edMFw6FnHmWSWdZ2nIwfeZcup67UZU6gt6i9nZ/r8zn6QxHxqdYcjhDcdc3vcZ7z2wZmaq0wL7tJb2i9vZcfwGkLeKJczBj4bCvvEZXTmaeo73c8gfGlVNpW7Y+y7vabVmX+qfWxt1u/5zmevblo17i5SM+ekVSnXWZ6pxLV/ucRTtb23t3/9S+AmAjzJ07V8rLyyvn1Lzwwgty7rnnyuWXXy5bbLGFvPLKK0kXmAwuAAAAGU+/0KXi1pStWLFCWrVqVfnzhx9+aK7SqLp162bak8UkHwAAgCy0xRZbyMyZM+W7776T5cuXy8KFC2Xw4MGmbc6cOeZa5cmiwAQAABmvXEIpmEWexsPRArDpppvKmDFjZLvttjM/X3zxxZKbm2sm9zzyyCPy0EMPJb1uCkwAAIAsddddd8lJJ51kisqK3ss1a9bIZZddJv369Ut6vRSYAAAg4zGLPDkaLTlo0KBq97Vp00YOPfRQ8WOjCsyVK1ea8/P1kZeXJ5tttpk0a9Ys2W0DAACol3IvYm5BCnp96ZqDuWzZssrJPZqDqXFFhx12mFx11VWmAE15gfncc8+ZbKT60vP4p59+utx6663JbBsAAACaeg7myJEj5aWXXqrXYzds2GBmJd10000yfPhw312tjcFPzqUr6zKtcy4dWZCeK+cy1758qMy+vG1pV36oNeDOtPvbr54l6zIUdxwPjt3mV9xySMUd+8WZkxm1L1+Yb39xZY71Ry2ZigWePacy4tixfvMggw5u3hiubcuxZFX6WVbleuVJv2fm+S05mKnmzLK0fFZTnXMZdqwfyYuZS0VGAl9nUzY3hTmYG1Vgdu/e3dw2hg4a/eijjzKywAQAAMimHMx77rmnMgfztddeS99JPgceeKAsWLAg1U8DAACyWCqC0Zt60PoWmZyDqRtasbEAAABID+RgAgAAOMZLBz8Gs2n3YCpyMAEAAJCSHMyioiJzqlwn/Wy//fa+58407elRAAAgi3IwcwK+Nf0ezFWrVsmZZ54prVu3NpeMnDBhgrlPI4q06EwWBSYAAECWOuKII2T69OkmC1OzL1Xbtm1lt912k3/9619JrzfrLxXZ9r7vpX23bnXunFWj21p3niuT0ZZ16Tfn0u/yniXr0os5gtxK7fl3oSL7tnk+XptrWSdLjmV9hCwv3ctxvG7H1znPZ1alLesy5vgSXp5jf+6iPEfOZcTzlf2Xa8lULHPlLTryHF05mXlS3mg5eOU+1+3KsrTJdbxu135tTGHPfrxFHZueE2u8nMtI+u7WjMcs8o33ww8/yDfffCPz5883cUVvvfVWZdvOO+9scjHPOOOMJNZMgQkAAJqAWAouFanrbMqWLl1qooqqZmFW0Et9c4ocAAAgzRQVFcnBBx9sTjdrxmRNOtbxwgsvlH322cdcMUcvyV1ffpat0LNnT/n222/r3LYZM2ZInz59JFlZf4ocAABkPh1yEnzQur9hLH/961/lk08+MSHmegntqtavXy+77rqrdOzYUf7+97/LTz/9JGPGjJGff/7ZTLSx8bNsVT169DDF71577SWXXnqpxGIxKSsrk2effVbuvvtuee+995J+7RSYAAAAAXv77bfl0UcflauuusrM0q4rf3LRokXm8ow6qaaicLzkkkvkpJNOMrO6E/GzbE0PP/ywHHXUUTJu3LjK+7788kvzHH379pVkMYscAABkvFjgEUU5Zp3JWLNmjSnYbrzxRuncuXOdj9EJNMOHD68sEJWe6taezjfffNO6fj/LVrVs2TKZN2+e6an86quv5KmnnpJXXnnFFK/HHXec+EGBCQAAYLF48WL55ZdfKm9r16617q+zzz7bjF8cP358wsd8//33ta71vfnmm0s0GjWzu238LFvVrFmzTK+n2mGHHUxP5gEHHLBRPaCJcIrcRwxROvMTY+SV+svRcC0ddm2bLVPHFTPkaPf9jvo4JtwxQ/7abbvNFUPkanc9t4sjxUiilvctP+SIEXLFwoTsTx4Lpe5z7oo4ioUc7Y3YBxBxRAGl9Lkdn2PX8RSJO44JyzHT2DFErueHK2g94DGY/399gwcPrnb/ZZddJpdffnmdyzz//PNmwo1OnrHRIrVly5a1rqrTokUL0wOaqmWr6tatmxm3mQoUmAAAABYaRF71VHddsT5KJ/OcdtppcvXVV5sZ2jba26iXZaxJJ9loW6qWrWrLLbeUbbbZRq688kozIz0nJ7iykAITAABkvJhEJBZwWaPrVFpcam+fi/Za6rjGJ554woxnVCtXrjT/6pjGYcOGmXGZqmvXrubUe1U6UUdvrufys2xVH330kXz++edm3KUWxfo6w+H/nTnRmeoPPfSQJIMCEwAAZLx0CFofMGCAvP/++9Xu058vuugiOf/88804x6rFmxZ4VemscLXLLrtYn8fPslV16tTJ9LgmoiHsyaLABAAACIBOjtFcyZqnzdWOO+4oW2+9deX9f/7zn2XIkCEmykh7N4uLi+Uf//iHWb5///6Vj3vttddM1JHGCelEno1Z1kULyERjSf1iFjkAAMh45V64cqJPcLfUlUmDBw+W++67T04//XTTs7nZZptJSUmJPP7449Ue9/vvv5veycLCwo1eNhFdl4bAb7/99iZsfeTIkbV6RP2iBxMAACBF9thjD3OaXIvAmjQUfdSoUSZ2SCcO1YweUhobpMvXPF1dn2UT0YxOzdLUK/hor6peFlL/X8eQbsx6bCgwAQBAxtMJPuWBT/Lxv7527drVOm1eVfPmzWXgwIEJ2zfZZBNzS2bZuixdulRefPFF+fjjj2WnnXaqnH1+xBFHyD333CM33XSTBIECswnmWNaHVxpL2OaKYYs4cyw9XzmbIVt7c/tTh1zP3Yg5l37Fw17S7a5lYz4zOP0KW96ZaNx+vLgOx4jjXXflaPoRd2RsxhwHTWkoknSOpt8MTddzu9j2e1QS//5RjsNVovbFJcfVXp66nMtU51gunDckYVuPXp+k9snRJMybN8+M06woLpVGG2mvZrIzxutCgQkAADKezvje2Fnf9VlnU1NYWFgrpF3pafaq4zz9osAEAADIIkuWLJFHHnmk1mUja96vuZgjRoxI6jkoMAEAQMbTISfB52A2zbCdWbNmyZgxY+psq3q/FpcUmAAAALDSCUc//fST1EdBQYEkix5MAACQ8XTGd8xLv1nk6SY/P995nfQgNL09BwAAso6eHo8EfIo86FPu2aRpDi4AAABAo6EHM015jjxHV06ma3nruh3Lxi0Zmirs2DZfqYOu1+Ujx9KvkM/8O9dYcs9HVqUrxzIeSv54UZG4fb9H4/b151oOqYgjWDDmeMvLXDmZKTxknFmUzgxPxwO85DIy1QbJtbbnhSxhkfUQsSTq5jtyMCOuj7mjPeLIsrRlXaY65zLs+Ky4l/f3/E1Z3AsHHiuk60Ry2HMAAAAIFD2YAAAg4+kEn8An+QS8vmxCDyYAAAACRWkOAAAynoaiB3+pSPrhksWeAwAAQKDowQQAABkv7kVSMIucHMxk0YMJAACAQNGDmULxWOLAsrCjtk9lzqWLZ9nuegUPppLrdTsC9Fxbnrq9KhJ3fBEuz7E/e7njtdmWL3Ms68rJ9MuV3Re1tIc9+7ZHHc8dcQQbloVSeDy7MjwdR2TEcUSWhhO/cRskz7psiWPPlTiyKpuHSpLOwSwL+8zYdGRJ5jhWbzskXMdqY+dY+n3+pkx7G4PucaQHM3kUmAAAIOPphJwwk3zSBqfIAQAAECh6MAEAQNM4Re4ai5TEOpEcejABAAAQKHowAQBAxiOmKL2kTYG5fPlyefrpp+W7776Tdu3ayciRI2WXXXap87GffPKJPPvss7JhwwYZOnSoHHvssRK2zKYEAABAw0mLquyFF16QHj16yHPPPSd9+vSRwsJC2WeffeT//u//aj323nvvlb322ktycnKkd+/ecvHFF8thhx0mniPKBAAANF1xL5ySGzK4B3PBggVy5ZVXyvnnn195X9++fWXcuHEyfvx46d+/v7lv5cqVMmHCBLn88svl73//u7lv3333NY995pln5Kijjgp0u9o8utLavmp026TXHbdkxNUnJzOVXBmbflPYXBmf1mXjXmrzQV35o5a3JZ5jXzbmyLl0/R5z5WTasi5dOZeu9ljI/txhf/Gkklee/Lb7jmVN5Rh+V+Zh2J41GXdkdMYsvycKJd+67HopED9KpMjaHgklfvHNQ2WOtdv3S469WcJeKKVZlH7W7cqxjDheW15x4uULP9zVumzzXT+0rxwIUFqU5ieccEK14lLtuusfH5TZs2dX3vfqq6+a3s2xY8dW3rftttvKwIED5cknn2zALQYAAOnE88LieZGAb2lRJmWktNhzbdvW7gl89913zb9bb7115X3ffPONtGnTRjp37lztsVpkfvvttw2wpQAAAMiIU+Q1/frrrzJx4kQZMWKEbL/99pX36ylyLTBr0vu0zWbt2rXmVmHx4sUBbzUAAGgsmoEZeA5mwOvLJmlXYK5Zs0YOOuggKSgokKlTp1Zr05ni8XjtAS56XyRiPwgmT54skyZNCnx7AQBAupwiD/bELKfIM/wUeQWNHdLi8vfff5e33npLunTpUq29U6dOJs6opmXLlsmmm25qXbdODlq0aFHlbfr06YFvPwAAANKoB7O0tFQOP/xw+fHHH834yy233LLWYwYNGmSK0FmzZsl2221Xef9nn31mootsWrVqZW4AAKDp8eJ6ljPgHsyA15dN0qLAjMViMmrUKFMovvPOO7LNNtvU+TjNxtS8zBtvvFH++c9/VmZozp07Vx5++OEG3uoAInF8xBi5+Ik58lyZM424X0OufR71+eRhR0xRNPF+jTk+Ta720qj9tRXlJR9T5Jff37FRR5ZQ2HbMOT4KjtQXiTj2i2u3+YpBcgzfijnidFxKLL/CV3itrcsuj9cez15VuWc/YDtEVlnbm4eKE7bFZIN12bAj1zjiOA0a8fHr0xUj5IohynEcMJFye3u01L7+aFHi5XNKyYNG+kiLAvOWW26R559/3vRQ6ljJqvQqPZp1qfLy8uSJJ56QQw891FzBR0+Lv/HGG3LdddfJkCFDGmnrAQBAY/Pkj5iioNeJDC4whw8fXtkjWVO3bt2q/ayF5fz5801PZ1FRkSlOe/bs2UBbCgAAgIwoMAcMGGBu9dWiRQs5+OCDU7pNAAAgg+jQiaCD0QlaTxp9vwAAAGh6PZgAAAB+Z3wHPeubWeTJowcTAAAAgaIHEwAAZD7tvQz60o7kYCaNAtOHWFnyYWthR1ZaKOKv3ZWjacvJ9JzBgP7aXfmhvpIBU5hN6lLuyKksybO/J+ua29s35PrLRrWJuLL/fO5W1/K2z4Mz59L53F7yGZwmX1SS5vwoRf29pxskP+mcy7nFW1jbf1trT+fo0/Yba3v7/DUJ27qKPUPTxZlF6cia9LVux+/uaKm9PcfRnltkf/6IJesyXGLf+NiLg+3rPjTDr3DHJJ+0wilyAAAABIoeTAAAkPn09Hjgp8gDXl8WoQcTAAAAgaIHEwAAZD69TGTQPY4BX3oym9CDCQAAgEDRgwkAADKfRk4EHSvkirFAQvRgAgAAIFD0YPrgxZJfNubKgox7Kc3RtC7rWLcXi/vKuRTH8n649pszztG1vOUrWczxaSossL/uNfmudvv3wahlv0cdryu/3N4edRwTzpxLx1se8RKvPxbyF8IZtqz7D6nLTo07dkxurPF+Qy9Z393avt2UPazt359t3/gd82clbIs49nmzMmuzFJSEfB1vYUuvlOtYduVcRsrFX85lsSPD2JJ1GS52HFA+spszQSgeMbeg14nk0IMJAACAQNGDCQAAMl7Ii5hb0OtEcigwAQBA5mOST1rhFDkAAAACRQ8mAABoGqfIg57kwynypNGDCQAAgEDRgwkAADJfLCKhWMCTcoJeXxahwPTBc2QL+mPPWou7svt8bFs4Yu/Yjpfas9RCpfYstrCf5XN9drqHHfmhseRzMMty7csW5tvbf29m/zj+GN3E2t49vjJh26bFJdZlo46syai11Z076MzRtCwfdrxnLv4/pl7KnjvPsd+bxeyhim3D6xO2dQqvsC7bu/V31vZZf7b/Yd2x3Qxrew9ZmrCtTak96LJFsf1znlNuPyZyXDnBtuMt7i/nMlrk2LZCx+/HInt7yJJ1GXLlXDp+98Zv2ca+bed+b18/UAWnyAEAQMYLx8MpuW2sSZMmSX5+vrm1atVK+vbtK1dccYUUFdVO2X/yySdlyJAh0r59e9l6663lxhtvFM+r35daP8s2BHowAQAAAjJx4kT529/+Zv6/uLhYPvroIxk7dqzMmTNHHn744crHPfDAA3L66afLvffeKwcffLB89dVXcvzxx0thYaFcdtll1ufws2xDoQcTAABkvFA8XHm5yOBuG18m5eTkVPZgtmnTRg444AA599xzTY9jScn/hitdffXVcvTRR8u4ceOkQ4cOsvfee5vi9Nprr5W1a9dan8PPsg2FAhMAACCF8vPzJRaLSTz+xzhY/f+ff/5Zttmm+rhXPdWtvZ7vvvtuwnX5WbYhcYocAABkvJCnPZjhwNepFi9eXO1+HVupN5d4PC5ffvml3H777XLWWWdJQUGBuT8SiUiXLl1k7ty51R4/b94882/N+6vys2xDogcTAABkvHA8kpKbGjx4sHTv3r3yNnnyZOf2dOjQQXJzc2XgwIHSr18/ueaaa6q1n3HGGfL444/L888/L2VlZfL5559XPkbHUtr4Wbah0IMJAABgMX36dOncuXPlz/Xpvfz111/NKeuZM2fK2WefLXvuuaeZ8BON/hH8phOBtDdywoQJctRRR5nC9frrr5dRo0Y51+9n2YZCgelD3BFy5zkyFW1CEVduoCPnzZEd6IUTb1uszJHT5sqidGVJOnIyrcv72KcqFHFsm2t5S4yc4y2RDY6czF+ira3td8242do+ftAfsxbr0i6y0L5xZf72a8Tzl0sYscXzuQ6XsL/PqStzNpXCjkiReMj+4uOhdYkbc361Lts+f421fcfOs5LOuVRdyhOvv3WJ/XU3L7JncEbL/L1ntt+frpzLnBL7c0c32LMmI+vtTxDa4NgAS06m83erIwfTuXyaC8VDwZ8i///HihaX3bp126hl8/LyzG333XeXu+++W4YOHSrTpk2TkSNHmnYtELVQ1JueSg+Hw/LZZ5+Zth122MG6bj/LNhROkQMAADSA+P+f5FOTFohq6tSpstlmm8luu+1W73X6WTaVKDABAEDG08tEhgO+JXPpyfHjx8tbb71l4oJ0fOSMGTPkz3/+s/Tq1Uv22muvyse98sorcuedd8rq1avNuMnbbrtN7rvvPrnnnntMD2UFzc7UWejffffdRi/bmCgwAQAAAnLmmWfKlClTZPPNN5cWLVqYvEo9Tf7BBx9UziJXOiZTw9d79+4t7dq1k6efflpef/31ylPoVWOJND+zau9nfZdtTIzBBAAAGS/sJXdpR9c6N5bOGn/++efN/+ulG0OhusftavF58803m5vtcWPGjDGTd3Q858Yu25goMAEAAFIgVM/Cz/Y4PeVtO+2djsWlosAEAABN5FKRQc8iZyRhsthzAAAACBQ9mD50fnW9tX3RiGZJr9uWt+jKsaxPjqbtu4UrKzLuyFKLR+1ZaqFS+/easC2LzbGsROztzhMJ0XjS70vEESHXrDSUdG6gOnfQWdb2rrHEyzcrs7+u/HL7tuU52nMduYRRR7SfK9fVzy8xVweE46OUUlFnPqh948Je4h0bzV1rXbZDxHW1j9+trfkx+wHf2vI5brc+4ut4ynEcjy62z3HUkXOZU+r5y7lcV2Zt99aXJZ1l6cqxjBc5PoiOnOH0mJucWDgWNjO/g14nkkOBCQAAMp5O8Al8kg+nyJNGaQ4AAIBA0YMJAAAynk7ICfqUNpN8kkcPJgAAAAJFDyYAAMh4xBSlF3owAQAAECh6MFOovDz5/JOwIzvFkYgj4bA9asNzxFHYhBx5POEyRwyR47lt2xZybrcj38kRC+OK6QgXJX7tzdbZP07tCuz7JRa2R4h0iC6ztudbFi9wRCS54nLyfMbGuNpTGRUUd1zlIuyIAmpMOY5UmRzLRzG/3P45LXPlaom/2K02hYnjYpoX2j8LUce6w46PuUvEcjxHi+3HQ06h/cnDq0ut7d7KkpRFCbmWjTtijFwxR1FJb+FYyNyCXieSQw8mAAAAAkUPJgAAyHhhLxR8DqZHD2ay6MEEAABAoOjBBAAAGU8vOevnsrOJ1onkUGACAICMF4qlIGida5EnjVPkAAAACBQ9mAAAIOOFYiFzC3qdSA4FZgrFfORg+kuoU/bnjuYm/tCEIvYPVMSRURePurIofeQOupZ1ZWw6Vu967eHixO9MtDhx7p9quS7iKyuyNNdHrmrc3zgjvzmWrlxDm5DPzEOXstzk913AE1br4MovTbzjC+xxjFIeceQ9Ov6wNt9gf/F5JYmXz3HlXPr8Beg6ZiKWuMhIkSPnco0j59LRHl9fZm/3kWXpWjZm+f1l2h05mAXWVqA6CkwAANBEYooCnuRDTFHSGIMJAACAQNGDCQAAMh5jMNMLPZgAAAAIFD2YAAAg4xG0nl4oMAEAQMYLxf64Bb1OJIdT5AAAAAgUPZgpVG7JwfTiPrIgNe8s7q+93JFraBPJsX8vicTsT+452m1Zlp4jp00i9m0LlTqe25U1WRZPOj8vL9eegxlyhCrmlUjKuHIyXWy5gioUT23uoR9+MkJjOf4+x37llqUuBDri+B0RdeRs5lhyMFOdfep6TyPFiR8QLnIcjKvsLzy2stje7jMH05ZlWbrWsW1l9uO1vMT+2tcMS5yEubi4cT8LKpSCmCJdJ5JDDyYAAAACRQ8mAADIeIzBTC/0YAIAACBQ9GACAICMp2N3/Y7frWudSA49mAAAAGi6PZiLFi2Sxx9/XJYuXSoXXnihtG/fvs7H/fDDD/Liiy/Khg0bZOjQobLffvs1+LYCAID0EYp7ErKkkCS7TmR4D+aBBx4ou+++u0ybNk1uuukmWbVqVZ2Pe+qpp6R///4yd+5cicViMnbsWHMDAABAekibHswJEybIsGHD5LnnnpP//Oc/dT5m7dq1ctppp8kFF1wgV155pbnvsMMOk0GDBslRRx0lBx10kKSTrT9NHFz4zYBc67Jxz/6tqaTU3u760pUXSz7bK5prz0qLNrfnPXqOb5jWrMuIfbtD4sjgdH2ncuRshgoTP38kal93rqPdlTtYnpu+Y4Vcz+3KJbT1Enjh1ObQuTI69ahJxJW554g29Z0v6ie/1PW6I2Wpfc9T2WsUdu23EksO5jpHTuWaEl85l852S86lKl6dOOuypND+wsscOcCu/GTruh0Zmw2BMZjpJW16MEeMGCHhsH1zXnvtNVm9erWccsoplfcNHDjQ9Gg++uijDbCVAAAgHenp8VTckOEFZn189dVX0qpVK+nRo0e1+/v16ydff/11o20XAAAA0vAUeX2sWLFC2rVrV+t+vU/bbPT0ut4qLF68OCXbCAAAGoEOrQh6Ug6TfLKjwFReHWMT9b5QyD6eaPLkyTJp0qQUbhkAAAAy7hT5JptsUmdPpd7XsWNH5yQijUGquE2fPj2FWwoAABojpijQGz2Y2dGDudNOO8n69etlzpw50rt378r7v/jiCxk8eLB1WR27qTcAAACkVkb1YI4cOVI23XRTuf322yvv09zM77//nixMAACyWcUYzKBvyOwezLvuukvmzZsns2fPNj9fe+210qZNG9lrr70q8y2bNWsmU6dOlT/96U/y008/mWLzySefNLmYmqGZScod0Qdl5fb2wmLH8o7150UTj1nNcWRNxhzb5sy5dLTHLVmUkVxHxqa1VTfenjHnFEn8nSy0wZ5BF3Hs13Ce/bVF8uzfB71G/LroO4PT+kvc8a76zMl0bbstLzKUZ1/WC/v74+TKqgzHfGRB+sgmTTX3e5J8zqVpt3xWvfX2HRd35FiWr02cU6lKHe1lG+y/ozasS7x9JcVxX7+7/eRgFjuymZF90qbA1JnghYWF0qlTJ9ljjz0q72/ZsmW1x+27777mFLlmYhYVFcmf//xnGTBgQCNsMQAASBux+B+3oNeJzC4wjznmmHo/VovQcePGpXR7AAAAkOEFJgAAQNL0HL+f8/yJ1omkUGACAIDMp+OGgz6lzSSf7JhFDgAAgPRHDyYAAMh4oXjc3IJeJ5JDgdlIBsy0R128u6U9smZ1kT0S4rf19uffok3i5Vs1c0VZ+Iwp8rG85zj9EXJ1yjuigpynV4osESauGCL7mp37LVTmiClyvbYURv04TyO51p/K01CO5w7ZP4oSssRDhR0xQrGc1EYo2aKGXDFDvqOlfHA+t2Pb/cQQmedflTgqKLay2LpsmaPdFUNU4ohBKi2yxxQVW2KMSsvs+62oxN/vdptCx3Mj+1BgAgCAzEdMUVphDCYAAAACRQ8mAADIfF4KYop0nUgKPZgAAAAIFD2YAACgiYzBjAW/TiSFHkwAAAAEih5MAACQ+dLkUpFLly41N5Wbmyvdu3eXgoKChI8vLy+XRYsWSbt27aR169Yb9Vy67C+//CItW7aU9u3bSzqhwExTe861d/M/39ne+dypmX39zfIS5/PluPIcfWYmuvIexdbuWtYeH+pc3pnkZlu+1P6eufaac69G7e95yPa++c25TOfLqfk9Hl371ZY1GXV8Vhw5mF648bImGzUHs8yRZ1vuyMEsdnzW1trDTeNrEmdVxtfbl405nrvMklOpyh0ZniXF9vbCosTt6zbY91uxI6sy4uN4LHS8Zw1CT48Hfop849f35JNPyj333GP+v7i42BSPI0eOlLvuuku6du1a+bjS0lKZOHGiTJkyRTbZZBNTlB5yyCHywAMPSPPmzZ3Pc/HFF8vNN98sHTp0kNWrV0uPHj3k3nvvlaFDh0o64BQ5AABAQM4++2z59ttvzW3u3Lkyf/58mTdvnhx77LHVHjdp0iRTiH788cfy888/m9usWbPk9NNPdz6HFqHXXHONPPfcc7JgwQJTnPbp00cOPfRQKSwsTIv3kgITAABkPC8eFy8WC/YWwCn3rl27yimnnCLvv/++rF//v8vsPfjggzJmzBjZYYcdzM/ai3nBBRfIo48+Kr/99pt1ndOmTZPtttvO9IyqvLw8U9guW7ZMvvnmG0kHFJgAAAAptHLlSmnWrJnk5+ebn2OxmCkGO3XqVO1x+rPneaYYtdHT4qtWrZJ4lQJ4+fLllW3pgDGYAAAg88Vjf9yCXqeILF68uNrdrVq1Mjeb77//XoqKimTGjBly++23y0033SQ5OX+UXZFIRHr37m3aqqr4eeHChdZ1T5gwQV588UU59dRT5eSTT5YlS5aY3s9zzjlHttxyS0kHFJgAAAAWgwcPrvbzZZddJpdffrl1n40dO9b0MuoknxEjRlSezq6gy48ePdpM9Dn44IPl66+/ljvvvFNCoZCZAGSjE3rOO+88s+zbb78ta9askV69esn48ePT5n2kwAQAAJkvhbPIp0+fLp07d66829V7WbGM0hneJ510kgwZMkR++OEHadOmjbn/mGOOMaez7777bjN+smfPnvL888/Lzjvv7DzN/Ze//EUefvhh8xzbbrutOeWuRa8+x1dffSVbbbWVNDbGYAIAAFhocdmtW7fKW30KzApaUGpv5e+//y7vvfeeVKU9m08//bQ5Na7/rlu3rs4e05oeeeQRU6BqcVlxyv2SSy6RkpISM7M8HdCDmaEOX2yf2fZWT/t3h4JcSw6m46gIOXIHrXmM9eBZLs3lztB05Ov5CXpzrb/UkXnoWLUzJ9OZAZrirEub0hSGKrpel6Pdcx2vjkxGyU8crhqO248nLxbylcHpJyfTd86lz2xT2/HqO+dyQ7m13XNkWXpFiZePW9pUebG9PeY4nsocn5UiS86lWrkucfviP2qThHIdOcG5juMtalm+JOCOw6R4GrQeC36dG0lPb+fm5la7r2JMZUXvpSorK5NoNFrtcXfccYcpLgcMGFB5n/aAapi6jtnU2eJKQ9krwtyrTvLRST/alg4oMAEAAAJy4IEHmp5JLRR15viXX34pV1xxhblvt912q3zcyy+/LC+99JIcd9xxptC8//775YMPPpB333232vpeeOEFGTdunIkf6tu3r7lPT6mfddZZpgdzv/32M7PUNVezS5cucvjhh6fFe0mBCQAAMl+s/I9b0OvcSHqqe8qUKXLttdfK2rVrzaUib7jhBnNKOxz+XzexFoIbNmyQ66+/3jyuYvxk1av9qLZt25rMy4qII3XmmWeaiT5Tp06VV1991Vz5R4tXnfjTsWNHSQchTwOXspB2N+ubrrO7dDxFU+M6Rd6qWeL2ls3ty7ZsU71Lv6Zmbe3t0eb29pzmib/3RFpUP+1QU8hxjsd5ijyS/On/kM/zT36X5xR5cqfInafYLafInae4XZeK5BR5ak6RryxJ+lKR5SuLrcsWLd1gb19pn/1b7LiU5Lp19vbfV8XS8hT58pgnJy2VRvmbWvH3fPc/vSn5zavnSvpVXLhE3n92nyZbK6QSk3wAAAAQKE6RAwCAJhK0HvAp8qAnDWURejABAAAQKHowAQBA5ounYJJP0D2iWYQCs4na+2d7dtfX/RNPtIk6RnpH81wTaRoxj9EvR9ak52PZkCP/zp3xGUl+v7veE9frLnWcJnJte0pzMH0ej66ZDbY8R8sEoPpM4nEm7KXxZ8mVy2rLunRN4hFXe5G93XN+1mxZu/4+p66cy5JiRw5miX39q4sSt81eZz9euuTb113gqAjGr4pbJ9pI9+72FSCrUGACAICM58Vi4gXcg6nrRHIYgwkAAIBA0YMJAAAyn46XDHwWOWMwk0UPJgAAAAJFDyYAAMh8mlkZ+CxyxmAmiwITAABkPC9WZm5BrxPJ4RQ5AAAAAkUPZpba4avkv5Wt+FNra3soHGq6OZk2rvw8x/c5515xZFFaE+5852A6Eht9Zgf6Ol4cOZjunEtHvqhl212vKhT3svOz4MrJLLMfLyFHuzuX1bW8LQfT/p7FHNsWd3xU3O32549aDtfuBf7yaB27Le1pRFHwPZhM8kkWPZgAAAAIFD2YAAAg8xFTlFbowQQAAECg6MEEAAAZz4unYAwmQetJowcTAAAAgaIHEwAAZD7tvQw6t5IczKRRYGKjtX92ja+9tuGMTtb2kCt2pjH5iawRfzFGrighX3zEupj2FOab+H3VruMpVOD4NViQOBcm5HPbw2F73E483x6h5OU0XsxRyM9b7jqWnbFYjlgtH5+VuCOGyCUc9tfukms5JFrn2ZcdvSzDc4iQUSgwAQBAxiMHM71QYAIAgMznlYoXLw18nUhOGp+LBAAAQCaiBxMAAGQ+vaxj4JN8uFRksujBBAAAQKDowQQAABlPQ9a9WLBjJoMObs8m9GACAAAgUPRgosE1u2tJ0suWTewpmcqVzefKyUzlc7tzBVObS+gnRzO23t7DELYFB4pIpHWufXmxt9uEIvacSi/XkdHpet/CjZeDKXHPX3sKOfd7CnNbw473JOp4z3f5kVnLydIZ5IH3YAY9Kz2L0IMJAACAQNGDCQAAMh5B6+mFHkwAAAAEih5MAACQ8XT8ZfCzyBmDmSwKTAAAkPE8c6nIksDXieRwihwAAACBogcTAAA0jaD1MEHr6YICExkletXPvpYvn9RLUsaVWejM5vOZG+hjeb85l/HSmGP9PrbNse5lP6yxtrft3tzanufKorTkaLryFqXUsV+jrvZw8lmTjZmR6eLabykWiiTer5F8e25qx+fXpmCLgKaHAhMAAGQ8ejDTC2MwAQAAECh6MAEAQMbTGeReLBr4OpEcejABAAAQKHowAQBA5tOg9VDAPY4ErWdXgfnbb7/Ja6+9Jhs2bJChQ4fKwIEDG3uTAABAY1/JJxRsWcOVfLLoFPnrr78uW221lbz88ssya9YsGTFihJx//vmNvVkAAADIxB5M7bEcO3asnHLKKXLzzTeb+0aNGiXDhg2TAw88UIYPH97Ym4g0l3PZvEZ77vJ/bNloz+0359J3RqeLZf2u527dpcDaHnbkXDpfm49tE0eGZ6jAnrnoa6/bMjIbIifTsv7w6C98rdrvltv3OjKVF9drkUcCXyeyoAdTey+XLl0qZ511VuV9e+21l2y77bby0EMPNeq2AQAAIAN7MD///HNp0aKF9OpV/WosAwYMkC++8PeNGAAAZC4vViJewP1muk5kQYG5bNkyad++fa379T5ts1m7dq25VVi8eHFKthEAACDbZVSBqUKhUJ33eZ59vNHkyZNl0qRJKdwyAADQqLPIA+/BZAxmVozB1J7KlStX1rpf7+vQoYN12QkTJsiiRYsqb9OnT0/hlgIAAGSvjOrB7N+/vznNvWDBAtlss80q7585c6bssMMO1mVbtWplbgAAoOmJx0sknoJ1IgsKzAMOOEDatm0r9957r1x11VXmPu2J/Prrr+Xqq6/eqHWVl5ebfxmLiYZSvuaPYy5lbHE68dTGFLnW78V8bHuZfeHSDfbnzonbl4+E7O05kvh9C5XY90uo2L5tjqeWuGP9XjTceOevHIdUyPKeRn75xeeTI91U/C2t+NvaGMpTECmUinVmi4wqMFu2bGmKy+OPP95czWfTTTeVBx54QMaPH2+Kz41RMSlo8ODBKdpaAEDdurNjmij929qzZ88GfU5Nl2nXrp3MXjkzJevXdetzYOOEPNfsmDQ0Z84ceeGFF6SoqMhcKnKfffbZ6HUUFxfLN998Ix07dpScnBzz7UuLTe0R7dy5c0q2uyliv7HfON7SH59T9luqac+lFpf9+vWT/Px8aWirV6+W9evXp2TdWly2adMmJetuyjKqB7NC79695YILLvC1Dv0ADBo0qNb9Wlx269bN17qzEfuN/cbxlv74nLLfUqmhey6r0gKQIjC9ZNQscgAAAKQ/CkwAAAAEigLz/9MIo8suu4woo43EfksO+4391pA43thvQEPLyEk+AAAASF/0YAIAACBQFJgAAAAIFAUmAAAAApWROZipUFJSIrm5uRIKhRp7U9KWhtjqkF29opJrX0ajUQmH+f6iSktLzbFVn8dFIhFzy1ZlZWVSWFho/l/3g+tYq1hGP7d6wQT87zOoF6IoKCiQvLy8OndLLBaTeDxuPqvZTAO6a9JjKdGVW3Sfaah4fT7TQDbL+grgrbfekm233dbMstRfKKeeeqr5xYz/XfHo4YcfluHDh0vr1q1l9913T7hrPvnkE9lxxx3NvmzevLm5pOeaNWuyclfq1abOOOMM6dKliymSOnXqJH/7298qi6eq9IpSu+66q3mc7rfDDz9cfv/9d8lGb7zxhglr1pvuOz2WdH/MnTu31mPnzZsne++9t/nc6n7bb7/9ZMGCBZLttPjRz2nbtm3lrrvuqtWul9k95JBDpFmzZmbf7bHHHvL9999LttL91LVr18rjTm8HH3xwrcetXLlSRo0aZY41/azqhTo+//zzRtlmIBNkdYGpv1T1F8no0aNlw4YN5g+9FpxaGOAPH374ofmjf/HFF8uRRx6ZcLf8+uuvMnLkSNlzzz1NT6f+8Z85c6Ycd9xxWbkr9UpT+vpff/1105v073//2xTqY8eOrdV7opc63WqrrUwxrvtx6dKlpqjKxoCHAw880OwTva1bt06+/fZbWb58uSke9ctOBf1/Pd60QFqxYoW5RJ32mO+///6mRzObXXXVVQmPHe190995um+XLFkiq1atMlcu23fffVN2mb1M8MADD1Qed3p75513aj3m6KOPNl8cf/75Z7P/tMDU/aafVwB18LLYySef7G255ZZePB6vvG/q1KleKBTyfvnll0bdtnR0zDHHeDvssEOdbX//+9+9Dh06eKWlpZX3vfrqq/pXzvv666+9bHPPPfd4JSUl1e674YYbvHA47K1Zs6byvhtvvNErKCiodt/06dPNfnv77bcbdJvT1bRp08z++PDDDyvv+9e//mX2ZdXP6bx588zjnnzySS9bffbZZ16LFi28r776yuyLm2++uVr7yy+/bO6fOXNm5X3Lly/3otGoN2XKFC8b6f54/PHHrY/55JNPzOP0WKywYcMGr3Xr1t6kSZMaYCuBzJPVPZj6LXXYsGHVxl3qqWD9nfPf//63UbctE/elnparOp5rr732Mvu2rt6Apk6HWtQco6U9bHpsVT3edN8MHDiwWsC//qyn4LJxvyU65auq7k/dN3369DGnNitsscUW5vRmtu437dU94YQTZOLEiWbf1EX3TceOHaVfv36V97Vv31522GGHrN1vFWxDo3Tf6PG32267Vd6n41uHDh2a9fsNSCSrC0w91dG9e/dq9+m4Ly0EtA3+9qX+Au7QoQP78v9Purj33nsri0fbftMCVE9bZvMxqKdrdczbxx9/bMau6phB3Xe2/ab0vmzdbxdddJGZ0POXv/wl4WPYb3U78cQTzRhzHY95xBFH1Brzq/tNx1HXnBCVzccb4JK1BaaO09JZlPn5+bX+uOsvaSb6bBzdXzX3pdL72Jcip59+usyfP1/uuOMO9ls96GSKzTbbTHbZZRfTg3n99dez3yzeffddmTJlijz44IPW2fR8TmvTMfffffed+RKoExV1zO+QIUPMZCj2G5C8rC0w9Zuo3nRyT1VadOovmkQRFaibzqysuS+VzprO9n154YUXytSpU+WRRx6RwYMHV2tjv9Xt5ZdfNhMpdAKFTqbQ3kudYMF+q9tJJ51kvsToEAGdpFKR3qAFZdUYHo632u68804zvEI7F3RowVNPPWV6zx966CH2G+BD1haYasstt5Sffvqp2n0LFy40My179erVaNvVVPbl2rVrzS/qbN6X1157rVx33XVy//33m1mo9dlv+iXnl19+yer9VkHHC959993mjMNjjz1m3W9KT1dm437TIQVaEFXE7FSMwbziiivMz1X3m+6jmrPMs3W/1UVPhbdr1858Bqvut8WLF1dLMlDsNyCxrC4wNWJCY4kqJhGo1157zfRs6uQfbNy+fP/996vlPGpEj/YKaFu29ozouDj9d9y4cXU+RvfNF198US33Uk93am+wxvBAzL7Qz2jVwHDdbxqFNXv27Mr7NBZKT2tm437TyKGqMTv6s7rmmmuq9WDqftOf9VRwBS2kNA4qG/dbXXT8pUZfVS24db/pF78333yz8j7djzpGmP0GJOBlsYULF3pt27b1Tj31VG/RokUmgqJjx47eBRdc0NibljY0wmnVqlXmdsQRR3h9+/at/Lm8vLzycStXrvS6dOlioox+/vln7+OPP/Z69OjhjRs3zstGDz/8sIm7uuyyyyr3V137TaNOevfu7e23337e3LlzvS+++MLbZpttvIMPPtjLRuedd543efJkE221ePFi77333vOGDRvmtW/f3nxGK+g+3Gmnnbxdd93V+/77771Zs2Z5gwYN8nbZZZdqsWPZqqioqM6YIrX33nt722+/vdnHs2fP9kaMGOFtt912tWK1ssFtt91mItY0GuzXX3/1Xn/9dW/bbbf1NttsM/M7rapRo0Z5vXr1Mo/96aefvMMPP9z8jlu7dm2jbT+QzrK6wFT6S3b//ff3Nt10U69Pnz7eVVddVa0AyHYrVqwwWW913WbMmFHtsXPmzDG/dDt37mx+EV900UVecXGxl420YEy036pmECotnEaPHm0K9J49e3rnnHOOt27dOi8b6R/1Sy+91Ntxxx29Tp06eQMGDDBFp34ZrGnZsmXe+PHjvW7dunndu3c3XxT1eIVnPnd6rN155521dodmrp511lmmiOratat3/PHHe7/99lvW7qdbbrnFGzJkiPm9pV9a/va3v5ls0Jr0y+Bf//pXb4sttjCPPfLII7358+c3ynYDmSCk/0nUuwkAAABsrKwegwkAAIDgUWACAAAgUBSYAAAACBQFJgAAAAJFgQkAAIBAUWACAAAgUBSYAAAACBQFJgAAAAJFgQkAAIBAUWAC8OWBBx6QU045xfqY2267Ta666qpA9vQPP/wgRx55pKxcuTKQ9QEAgkeBCcCXL7/8Ul566SXrY6ZPny7vv/9+IHt6+fLl8uyzz8qGDRsCWR8AIHg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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "layer = nz // 2\n", + "fig, ax = plt.subplots(figsize=(6.5, 5.2))\n", + "im = ax.imshow(final_pressure_grid[layer] / 1e6, origin='lower', cmap='turbo')\n", + "ax.set_title(f'Final pressure on K layer {layer}')\n", + "ax.set_xlabel('I index')\n", + "ax.set_ylabel('J index')\n", + "fig.colorbar(im, ax=ax, label='Pressure, MPa')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "686baff5", + "metadata": {}, + "source": [ + "## 3D pressure field\n", + "\n", + "The 2D `K` slice above is easy to test; a 3D scatter of the active cells gives spatial intuition for the whole grid. Below, every active cell is drawn at its `(I, J, K)` position and coloured by its final-timestep pressure." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fa7a7f35", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:42:20.656238Z", + "iopub.status.busy": "2026-07-22T10:42:20.656102Z", + "iopub.status.idle": "2026-07-22T10:42:21.193088Z", + "shell.execute_reply": "2026-07-22T10:42:21.192208Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D # noqa: F401 (registers 3D projection)\n", + "\n", + "kk, jj, ii = np.where(np.isfinite(final_pressure_grid))\n", + "vals = final_pressure_grid[kk, jj, ii] / 1e6\n", + "\n", + "fig = plt.figure(figsize=(8, 6))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "sc = ax.scatter(ii, jj, kk, c=vals, cmap='turbo', marker='s', s=6, depthshade=False)\n", + "ax.set_xlabel('I index')\n", + "ax.set_ylabel('J index')\n", + "ax.set_zlabel('K layer')\n", + "ax.set_zlim(nz - 1, 0) # deeper layers point downward\n", + "ax.set_title('Final pressure field over active cells')\n", + "fig.colorbar(sc, ax=ax, label='Pressure, MPa', shrink=0.6)\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "75ef6ed3", + "metadata": {}, + "source": [ + "## Inspect well results\n", + "\n", + "The `wells.h5` file contains a top-level `wells` group. Each child dataset is one well and uses the column order listed in `manifest['wells']['columns']`.\n", + "\n", + "The group access is important: use the `wells` group first, then index the selected well. Looking for `PROD1` at the HDF5 root would raise a `KeyError` because it is stored below `wells/`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f3d48847", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:42:21.195091Z", + "iopub.status.busy": "2026-07-22T10:42:21.194823Z", + "iopub.status.idle": "2026-07-22T10:42:21.205102Z", + "shell.execute_reply": "2026-07-22T10:42:21.204382Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(['INJECT1', 'INJECT2', 'INJECT3', 'INJECT4', 'INJECT5'],\n", + " time_days WBHP WOPR WWPR WGPR WWIR WGIR\n", + " 0 30.0 395.0 132.174435 1.684508e-09 0.0 0.0 0.0\n", + " 1 60.0 395.0 132.949010 3.882380e-15 0.0 0.0 0.0\n", + " 2 90.0 395.0 133.414487 2.987594e-19 0.0 0.0 0.0\n", + " 3 120.0 395.0 133.960671 2.265188e-23 0.0 0.0 0.0\n", + " 4 150.0 395.0 137.677850 1.697437e-27 0.0 0.0 0.0)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "well_name = 'PROD1'\n", + "well_columns = manifest['wells']['columns']\n", + "\n", + "with h5py.File(run_dir / 'wells.h5') as h5:\n", + " wells_group = h5['wells']\n", + " well_names = sorted(wells_group.keys())\n", + " prod1 = pd.DataFrame(wells_group[well_name][:], columns=well_columns)\n", + "\n", + "well_names[:5], prod1.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "911a27eb", + "metadata": {}, + "source": [ + "### Example: producer rates and BHP\n", + "\n", + "`PROD1` is useful for a compact example because the oil and water rates change visibly over the history. BHP uses a second axis because it has different units from rates.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c08653fe", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:42:21.206728Z", + "iopub.status.busy": "2026-07-22T10:42:21.206541Z", + "iopub.status.idle": "2026-07-22T10:42:21.531003Z", + "shell.execute_reply": "2026-07-22T10:42:21.530048Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax_rate = plt.subplots(figsize=(9, 4.8))\n", + "ax_bhp = ax_rate.twinx()\n", + "\n", + "ax_rate.plot(prod1['time_days'], prod1['WOPR'], label='Oil rate WOPR', color='tab:blue')\n", + "ax_rate.plot(prod1['time_days'], prod1['WWPR'], label='Water rate WWPR', color='tab:red')\n", + "ax_bhp.plot(prod1['time_days'], prod1['WBHP'], label='BHP', color='tab:green', alpha=0.75)\n", + "\n", + "ax_rate.set_title(f'{well_name}: simulated rates and bottom-hole pressure')\n", + "ax_rate.set_xlabel('Time, days')\n", + "ax_rate.set_ylabel('Rate, sm³/day')\n", + "ax_bhp.set_ylabel('BHP, bar')\n", + "ax_rate.grid(True, alpha=0.3)\n", + "\n", + "lines = ax_rate.get_lines() + ax_bhp.get_lines()\n", + "ax_rate.legend(\n", + " lines, [line.get_label() for line in lines],\n", + " loc='center right', bbox_to_anchor=(0.99, 0.68), frameon=True)\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "02f684bd", + "metadata": {}, + "source": [ + "## Run a three-month BHP optimization\n", + "\n", + "The optimization driver starts from the same deck and writes:\n", + "\n", + "* `summary.json` — objective values, convergence, economic settings and optimizer metadata;\n", + "* `optimal_bhp.csv` — optimized bottom-hole pressure by month and well;\n", + "* `production.csv` — base-vs-optimized rates by month and well.\n", + "\n", + "We intentionally optimize **3 forecast months**, not a one-month toy case, so controls and production response have a visible trajectory across decision periods.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "42f59b0d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:42:21.532634Z", + "iopub.status.busy": "2026-07-22T10:42:21.532508Z", + "iopub.status.idle": "2026-07-22T10:49:52.789424Z", + "shell.execute_reply": "2026-07-22T10:49:52.788614Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "======================================================================\n", + "Forecast BHP optimization: Egg_Model_ECL.DATA\n", + "months=3\n", + "======================================================================\n", + "[1/5] Loading cached history ...\n", + "Jutul: Simulating 9 years, 44.69 weeks as 120 report steps\n", + "╭────────────────┬───────────┬───────────────┬──────────╮\n", + "│ Iteration type │ Avg/step │ Avg/ministep │ Total │\n", + "│ │ 120 steps │ 154 ministeps │ (wasted) │\n", + "├────────────────┼───────────┼───────────────┼──────────┤\n", + "│ Newton │ 4.31667 │ 3.36364 │ 518 (0) │\n", + "│ Linearization │ 5.6 │ 4.36364 │ 672 (0) │\n", + "│ Linear solver │ 21.8083 │ 16.9935 │ 2617 (0) │\n", + "│ Precond apply │ 43.6167 │ 33.987 │ 5234 (0) │\n", + "╰────────────────┴───────────┴───────────────┴──────────╯\n", + "╭───────────────┬─────────┬────────────┬─────────╮\n", + "│ Timing type │ Each │ Relative │ Total │\n", + "│ │ ms │ Percentage │ s │\n", + "├───────────────┼─────────┼────────────┼─────────┤\n", + "│ Properties │ 0.4708 │ 0.49 % │ 0.2439 │\n", + "│ Equations │ 4.7673 │ 6.47 % │ 3.2036 │\n", + "│ Assembly │ 2.4749 │ 3.36 % │ 1.6631 │\n", + "│ Linear solve │ 2.5000 │ 2.61 % │ 1.2950 │\n", + "│ Linear setup │ 60.9760 │ 63.78 % │ 31.5855 │\n", + "│ Precond apply │ 1.2374 │ 13.08 % │ 6.4765 │\n", + "│ Update │ 2.1805 │ 2.28 % │ 1.1295 │\n", + "│ Convergence │ 2.5715 │ 3.49 % │ 1.7281 │\n", + "│ Input/Output │ 0.0000 │ 0.00 % │ 0.0000 │\n", + "│ Other │ 4.2487 │ 4.44 % │ 2.2008 │\n", + "├───────────────┼─────────┼────────────┼─────────┤\n", + "│ Total │ 95.6101 │ 100.00 % │ 49.5260 │\n", + "╰───────────────┴─────────┴────────────┴─────────╯\n", + "Producers: [:PROD2, :PROD1, :PROD4, :PROD3]\n", + "Injectors: [:INJECT5, :INJECT4, :INJECT2, :INJECT1, :INJECT6, :INJECT7, :INJECT3, :INJECT8]\n", + "[2/5] Building 3-month forecast ...\n", + "Variables: 36 (12 controls x 3 periods)\n", + "[3/5] Simulating base and optimizing (L-BFGS + adjoint) ...\n", + "Jutul: Solving 6 adjoint steps\n", + "It. | Objective | Proj. grad | Linesearch-its\n", + "-----------------------------------------------\n", + "0 | 3.7737e+05 | 1.8832e+07 | -\n", + "Jutul: Solving 6 adjoint steps\n", + "1 | 8.5086e+05 | 8.3530e+06 | 1\n", + "Jutul: Solving 11 adjoint steps\n", + "2 | 3.0080e+06 | 7.2176e+06 | 1\n", + "LBFGS: Problematic constraint handling, relative step length: 0.8868669791246965\n", + "Jutul: Solving 10 adjoint steps\n", + "3 | 3.4788e+06 | 1.0683e+07 | 1\n", + "Jutul: Solving 10 adjoint steps\n", + "4 | 4.6503e+06 | 1.5600e+06 | 2\n", + "LBFGS: Problematic constraint handling, relative step length: 0.12713676541686328\n", + "Jutul: Solving 11 adjoint steps\n", + "LBFGS: Line search at max step size, Wolfe conditions not satisfied for this step\n", + "LBFGS: Hessian not updated during iteration 5\n", + "5 | 4.9562e+06 | 1.5176e+06 | 1\n", + "Jutul: Solving 12 adjoint steps\n", + "LBFGS: Line search at max step size, Wolfe conditions not satisfied for this step\n", + "LBFGS: Hessian not updated during iteration 6\n", + "6 | 5.7893e+06 | 1.6629e+06 | 1\n", + "LBFGS: Problematic constraint handling, relative step length: 0.06737814104303683\n", + "Jutul: Solving 22 adjoint steps\n", + "LBFGS: Line search at max step size, Wolfe conditions not satisfied for this step\n", + "LBFGS: Hessian not updated during iteration 7\n", + "7 | 5.8078e+06 | 2.5004e+07 | 1\n", + "LBFGS: Problematic constraint handling, relative step length: 0.072341000718985\n", + "Jutul: Solving 21 adjoint steps\n", + "LBFGS: Line search at max step size, Wolfe conditions not satisfied for this step\n", + "8 | 5.8342e+06 | 2.5309e+07 | 1\n", + "Jutul: Solving 32 adjoint steps\n", + "LBFGS: Line search at max step size, Wolfe conditions not satisfied for this step\n", + "9 | 5.9436e+06 | 2.5228e+07 | 1\n", + "Jutul: Solving 26 adjoint steps\n", + "Jutul: Solving 27 adjoint steps\n", + "LBFGS: Line search at max step size, Wolfe conditions not satisfied for this step\n", + "LBFGS: Hessian not updated during iteration 10\n", + "10 | 6.0148e+06 | 2.0260e+06 | 2\n", + "LBFGS: Problematic constraint handling, relative step length: 0.009677449778054953\n", + "Jutul: Solving 33 adjoint steps\n", + "LBFGS: Line search at max step size, Wolfe conditions not satisfied for this step\n", + "LBFGS: Hessian not updated during iteration 11\n", + "11 | 6.0201e+06 | 6.4937e+07 | 1\n", + "Jutul: Solving 31 adjoint steps\n", + "LBFGS: Line search at max step size, Wolfe conditions not satisfied for this step\n", + "12 | 6.0273e+06 | 6.5066e+07 | 1\n", + "Jutul: Solving 27 adjoint steps\n", + "13 | 6.0278e+06 | 1.6343e+06 | 1\n", + "Jutul: Solving 27 adjoint steps\n", + "14 | 6.0583e+06 | 6.0238e+05 | 2\n", + "Jutul: Solving 31 adjoint steps\n", + "15 | 6.1456e+06 | 4.7220e+05 | 1\n", + "Jutul: Solving 31 adjoint steps\n", + "16 | 6.1554e+06 | 4.1120e+05 | 1\n", + "Jutul: Solving 29 adjoint steps\n", + "17 | 6.2008e+06 | 4.0025e+05 | 1\n", + "Jutul: Solving 32 adjoint steps\n", + "18 | 6.2009e+06 | 1.5616e+06 | 1\n", + "Jutul: Solving 27 adjoint steps\n", + "Jutul: Solving 30 adjoint steps\n", + "Jutul: Solving 32 adjoint steps\n", + "LBFGS: Line search unable to succeed in 5 iterations ...\n", + "Jutul: Solving 32 adjoint steps\n", + "LBFGS: Hessian not updated during iteration 19\n", + "19 | 6.2009e+06 | 3.4684e+05 | 5\n", + "base NPV=0.494 MM, opt NPV=6.201 MM (1154.7%)\n", + "[4/5] Simulating optimized forecast ...\n", + "[5/5] Writing CSV + summary ...\n", + "Done. Wrote production.csv, optimal_bhp.csv, summary.json to d:\\GitHub\\geo-kit\\GeoCode\\open_data\\egg\\optimization\n", + "jutul_optimize.jl completed; full output: d:\\GitHub\\geo-kit\\GeoCode\\open_data\\egg\\optimization\\julia.log" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "WindowsPath('open_data/egg/optimization')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimization_params = {\n", + " 'months': 3,\n", + " 'oil-price': 600.0,\n", + " 'gas-price': 0.12,\n", + " 'water-price': 3.0,\n", + " 'water-cost': 3.0,\n", + " 'gas-cost': 0.12,\n", + " 'discount-rate': 10,\n", + " 'max-it': 25,\n", + " 'bhp-prod-min': 200,\n", + " 'bhp-prod-max': 400,\n", + " 'bhp-inj-min': 200,\n", + " 'bhp-inj-max': 400,\n", + " 'history-cache': str(run_dir / 'jutul_state'), # reuse the JutulDarcy restart store from the simulation cell\n", + "}\n", + "\n", + "optimization_dir = execute_julia_optimize(case_path, params=optimization_params)\n", + "optimization_dir.relative_to(PROJECT_ROOT)" + ] + }, + { + "cell_type": "markdown", + "id": "16194fa4", + "metadata": {}, + "source": [ + "## Read optimization outputs\n", + "\n", + "Start with the summary. The checks below are deliberately explicit: the optimizer must converge, and the recorded forecast length must remain three months.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9ee14606", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:49:52.791565Z", + "iopub.status.busy": "2026-07-22T10:49:52.791419Z", + "iopub.status.idle": "2026-07-22T10:49:52.796969Z", + "shell.execute_reply": "2026-07-22T10:49:52.795955Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'converged': True,\n", + " 'iterations': 20,\n", + " 'max_iterations': 25,\n", + " 'months': 3,\n", + " 'variables': 36,\n", + " 'base_npv': 494190.83,\n", + " 'optimized_npv': 6200858.44,\n", + " 'improvement_pct': 1154.75}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "summary = json.loads((optimization_dir / 'summary.json').read_text())\n", + "assert summary['converged'], summary\n", + "assert summary['months'] == 3, summary\n", + "\n", + "{\n", + " 'converged': summary['converged'],\n", + " 'iterations': summary['iterations'],\n", + " 'max_iterations': summary.get('max_it'),\n", + " 'months': summary['months'],\n", + " 'variables': summary['n_variables'],\n", + " 'base_npv': round(summary['base_npv'], 2),\n", + " 'optimized_npv': round(summary['opt_npv'], 2),\n", + " 'improvement_pct': round(summary['improvement_pct'], 2),\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "eb7984d2", + "metadata": {}, + "source": [ + "## Optimization economics (NPV)\n", + "\n", + "The optimizer's headline numbers — base vs optimized NPV and the relative gain — summarise the economic result. Here they are as a compact bar chart." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "010aa569", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:49:52.798668Z", + "iopub.status.busy": "2026-07-22T10:49:52.798448Z", + "iopub.status.idle": "2026-07-22T10:49:52.956442Z", + "shell.execute_reply": "2026-07-22T10:49:52.955747Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "labels = ['Base', 'Optimized']\n", + "values = [summary['base_npv'], summary['opt_npv']]\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 4.5))\n", + "bars = ax.bar(labels, values, color=['tab:gray', 'tab:green'])\n", + "ax.set_title(f\"NPV: base vs optimized (+{summary['improvement_pct']:.1f}%)\")\n", + "ax.set_ylabel('NPV')\n", + "ax.grid(True, axis='y', alpha=0.3)\n", + "for bar, value in zip(bars, values):\n", + " ax.text(bar.get_x() + bar.get_width() / 2, value, f'{value:,.0f}',\n", + " ha='center', va='bottom')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "122a0a47", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:49:52.958352Z", + "iopub.status.busy": "2026-07-22T10:49:52.958201Z", + "iopub.status.idle": "2026-07-22T10:49:52.974898Z", + "shell.execute_reply": "2026-07-22T10:49:52.974251Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'optimal_bhp_rows': 36,\n", + " 'production_rows': 36,\n", + " 'periods': [1, 2, 3],\n", + " 'controls': 12,\n", + " 'wells_in_production': 12}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bhp = pd.read_csv(optimization_dir / 'optimal_bhp.csv', parse_dates=['start_date', 'end_date'])\n", + "production = pd.read_csv(optimization_dir / 'production.csv', parse_dates=['start_date', 'end_date'])\n", + "\n", + "{\n", + " 'optimal_bhp_rows': len(bhp),\n", + " 'production_rows': len(production),\n", + " 'periods': sorted(bhp['period'].unique().tolist()),\n", + " 'controls': bhp['control'].nunique(),\n", + " 'wells_in_production': production['well'].nunique() if 'well' in production else None,\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "1262196d", + "metadata": {}, + "source": [ + "## Optimized controls\n", + "\n", + "Each row below is a forecast month and each column is a well control. Producers and injectors are optimized within their BHP bounds. The annotation makes it easy to spot wells pushed to high or low pressure limits.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "8765d0f7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:49:52.976580Z", + "iopub.status.busy": "2026-07-22T10:49:52.976176Z", + "iopub.status.idle": "2026-07-22T10:49:53.217250Z", + "shell.execute_reply": "2026-07-22T10:49:53.216467Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "control_order = sorted(bhp['control'].unique(), key=lambda x: (not x.startswith('PROD'), x))\n", + "bhp_matrix = bhp.pivot(index='period', columns='control', values='bhp_bar').reindex(columns=control_order)\n", + "\n", + "fig, ax = plt.subplots(figsize=(11, 4.8))\n", + "im = ax.imshow(bhp_matrix, aspect='auto', cmap='viridis', vmin=200, vmax=400)\n", + "ax.set_title('Optimized BHP controls by period and well')\n", + "ax.set_xlabel('Well')\n", + "ax.set_ylabel('Forecast period')\n", + "ax.set_xticks(range(len(bhp_matrix.columns)), bhp_matrix.columns, rotation=45, ha='right')\n", + "ax.set_yticks(range(len(bhp_matrix.index)), bhp_matrix.index)\n", + "for row, period in enumerate(bhp_matrix.index):\n", + " for col, well in enumerate(bhp_matrix.columns):\n", + " value = bhp_matrix.loc[period, well]\n", + " ax.text(col, row, f'{value:.0f}', ha='center', va='center', color='white' if value < 300 else 'black', fontsize=8)\n", + "fig.colorbar(im, ax=ax, label='BHP, bar')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "969c1d0b", + "metadata": {}, + "source": [ + "## Production response\n", + "\n", + "`production.csv` compares the base and optimized forecast for every well. Aggregating by period gives a field-level view; grouping by well shows which producers contribute most to the oil-rate uplift.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "46c119b7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:49:53.218843Z", + "iopub.status.busy": "2026-07-22T10:49:53.218693Z", + "iopub.status.idle": "2026-07-22T10:49:53.230299Z", + "shell.execute_reply": "2026-07-22T10:49:53.229501Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "period", + "rawType": "int64", + "type": "integer" + }, + { + "name": "base_oil_rate_m3_day", + "rawType": "float64", + "type": "float" + }, + { + "name": "opt_oil_rate_m3_day", + "rawType": "float64", + "type": "float" + }, + { + "name": "base_gas_rate_m3_day", + "rawType": "float64", + "type": "float" + }, + { + "name": "opt_gas_rate_m3_day", + "rawType": "float64", + "type": "float" + }, + { + "name": "base_water_rate_m3_day", + "rawType": "float64", + "type": "float" + }, + { + "name": "opt_water_rate_m3_day", + "rawType": "float64", + "type": "float" + }, + { + "name": "base_water_inj_m3_day", + "rawType": "float64", + "type": "float" + }, + { + "name": "opt_water_inj_m3_day", + "rawType": "float64", + "type": "float" + } + ], + "ref": "3eaa3de5-07c1-49e4-832a-a40bafbacf95", + "rows": [ + [ + "0", + "1", + "15.738", + "279.065", + "0.0", + "0.0", + "620.27", + "12375.789999999999", + "636.0", + "12655.025" + ], + [ + "1", + "2", + "15.546000000000001", + "246.09199999999998", + "0.0", + "0.0", + "620.4590000000001", + "12989.984", + "636.0", + "13236.112000000001" + ], + [ + "2", + "3", + "15.366", + "215.966", + "0.0", + "0.0", + "620.639", + "13542.019", + "636.0", + "13757.838" + ] + ], + "shape": { + "columns": 9, + "rows": 3 + } + }, + "text/html": [ + "
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periodbase_oil_rate_m3_dayopt_oil_rate_m3_daybase_gas_rate_m3_dayopt_gas_rate_m3_daybase_water_rate_m3_dayopt_water_rate_m3_daybase_water_inj_m3_dayopt_water_inj_m3_day
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" + ], + "text/plain": [ + " period base_oil_rate_m3_day opt_oil_rate_m3_day base_gas_rate_m3_day \\\n", + "0 1 15.738 279.065 0.0 \n", + "1 2 15.546 246.092 0.0 \n", + "2 3 15.366 215.966 0.0 \n", + "\n", + " opt_gas_rate_m3_day base_water_rate_m3_day opt_water_rate_m3_day \\\n", + "0 0.0 620.270 12375.790 \n", + "1 0.0 620.459 12989.984 \n", + "2 0.0 620.639 13542.019 \n", + "\n", + " base_water_inj_m3_day opt_water_inj_m3_day \n", + "0 636.0 12655.025 \n", + "1 636.0 13236.112 \n", + "2 636.0 13757.838 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "period_rates = production.groupby('period', as_index=False).agg({\n", + " 'base_oil_rate_m3_day': 'sum',\n", + " 'opt_oil_rate_m3_day': 'sum',\n", + " 'base_gas_rate_m3_day': 'sum',\n", + " 'opt_gas_rate_m3_day': 'sum',\n", + " 'base_water_rate_m3_day': 'sum',\n", + " 'opt_water_rate_m3_day': 'sum',\n", + " 'base_water_inj_m3_day': 'sum',\n", + " 'opt_water_inj_m3_day': 'sum',\n", + "})\n", + "period_rates\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4038f55f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:49:53.231939Z", + "iopub.status.busy": "2026-07-22T10:49:53.231727Z", + "iopub.status.idle": "2026-07-22T10:49:53.753933Z", + "shell.execute_reply": "2026-07-22T10:49:53.752902Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n", + "periods = period_rates['period']\n", + "\n", + "axes[0, 0].plot(periods, period_rates['base_oil_rate_m3_day'], marker='o', label='base')\n", + "axes[0, 0].plot(periods, period_rates['opt_oil_rate_m3_day'], marker='o', label='optimized')\n", + "axes[0, 0].set_title('Field oil rate')\n", + "axes[0, 0].set_ylabel('m³/day')\n", + "axes[0, 0].legend()\n", + "\n", + "axes[0, 1].plot(periods, period_rates['base_water_rate_m3_day'], marker='o', label='base')\n", + "axes[0, 1].plot(periods, period_rates['opt_water_rate_m3_day'], marker='o', label='optimized')\n", + "axes[0, 1].set_title('Produced water rate')\n", + "axes[0, 1].legend()\n", + "\n", + "axes[1, 0].plot(periods, period_rates['base_water_inj_m3_day'], marker='o', label='base')\n", + "axes[1, 0].plot(periods, period_rates['opt_water_inj_m3_day'], marker='o', label='optimized')\n", + "axes[1, 0].set_title('Injected water rate')\n", + "axes[1, 0].set_xlabel('Period')\n", + "axes[1, 0].set_ylabel('m³/day')\n", + "axes[1, 0].legend()\n", + "\n", + "axes[1, 1].plot(periods, period_rates['opt_oil_rate_m3_day'] - period_rates['base_oil_rate_m3_day'], marker='o', color='tab:green')\n", + "axes[1, 1].axhline(0, color='black', linewidth=1)\n", + "axes[1, 1].set_title('Oil-rate uplift')\n", + "axes[1, 1].set_xlabel('Period')\n", + "axes[1, 1].set_ylabel('m³/day')\n", + "\n", + "for ax in axes.ravel():\n", + " ax.grid(True, alpha=0.3)\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "27139e25", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-22T10:49:53.755635Z", + "iopub.status.busy": "2026-07-22T10:49:53.755501Z", + "iopub.status.idle": "2026-07-22T10:49:53.895552Z", + "shell.execute_reply": "2026-07-22T10:49:53.894825Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "well_uplift = (\n", + " production.assign(oil_uplift=lambda df: df['opt_oil_rate_m3_day'] - df['base_oil_rate_m3_day'])\n", + " .groupby('well', as_index=False)['oil_uplift']\n", + " .sum()\n", + " .sort_values('oil_uplift', ascending=False)\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 4.5))\n", + "colors = np.where(well_uplift['oil_uplift'] >= 0, 'tab:green', 'tab:red')\n", + "ax.bar(well_uplift['well'], well_uplift['oil_uplift'], color=colors)\n", + "ax.axhline(0, color='black', linewidth=1)\n", + "ax.set_title('Cumulative oil-rate uplift by well over 3 months')\n", + "ax.set_xlabel('Well')\n", + "ax.set_ylabel('Σ optimized - base, m³/day')\n", + "ax.tick_params(axis='x', rotation=45)\n", + "ax.grid(True, axis='y', alpha=0.3)\n", + "fig.tight_layout()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geoview", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/setup.py b/setup.py index abf21e5..82a43d5 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ import re from setuptools import setup, find_packages -with open('deepfield/__init__.py', 'r') as f: +with open('geocode/__init__.py', 'r') as f: VERSION = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", f.read(), re.M) if not VERSION: raise RuntimeError("Unable to find version string.")