Simulating Eclipse/DATA input files
The DATA format is commonly used in reservoir simulation. JutulDarcy can set up cases on this format and includes a fully featured grid builder for corner-point grids. Once a case has been set up, it uses the same types as a regular JutulDarcy simulation, allowing modification and use of the case in differentiable workflows.
We begin by loading the SPE9 dataset via the GeoEnergyIO package. This package includes a set of open datasets that can be used for testing and benchmarking. The SPE9 dataset is a 3D model with a corner-point grid and a set of wells produced by the Society of Petroleum Engineers. The specific version of the file included here is taken from the OPM tests repository.
using JutulDarcy, GeoEnergyIO
pth = GeoEnergyIO.test_input_file_path("SPE9", "SPE9.DATA");Set up and run a simulation
If we do not need the case, we could also have done: ws, states = simulate_data_file(pth)
case = setup_case_from_data_file(pth)
ws, states = simulate_reservoir(case);[94;1mPVT:[0m Fixing table for low pressure conditions.
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Stats: 184 iterations in 11.04 s (59.98 ms each)[K
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Stats: 191 iterations in 11.31 s (59.21 ms each)[K
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Stats: 194 iterations in 11.43 s (58.92 ms each)[K
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Stats: 200 iterations in 11.67 s (58.37 ms each)[K
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Stats: 204 iterations in 11.83 s (57.98 ms each)[K
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Stats: 211 iterations in 12.15 s (57.60 ms each)[K
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Stats: 214 iterations in 12.27 s (57.34 ms each)[K
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Stats: 218 iterations in 12.42 s (56.98 ms each)[K
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Stats: 222 iterations in 12.59 s (56.72 ms each)[K
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Stats: 225 iterations in 12.71 s (56.51 ms each)[K
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Stats: 230 iterations in 12.92 s (56.16 ms each)[K
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Stats: 235 iterations in 13.12 s (55.82 ms each)[K
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Stats: 238 iterations in 13.23 s (55.61 ms each)[K
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Stats: 241 iterations in 13.35 s (55.40 ms each)[K
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Stats: 245 iterations in 13.52 s (55.18 ms each)[K
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Stats: 248 iterations in 13.64 s (54.98 ms each)[K
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Progress: Solving step 66/90 (73.33% of time interval complete)[K
Stats: 250 iterations in 13.75 s (55.01 ms each)[K
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Progress: Solving step 67/90 (74.44% of time interval complete)[K
Stats: 253 iterations in 13.87 s (54.81 ms each)[K
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Progress: Solving step 69/90 (76.67% of time interval complete)[K
Stats: 258 iterations in 14.06 s (54.50 ms each)[K
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Stats: 262 iterations in 14.22 s (54.26 ms each)[K
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Stats: 266 iterations in 14.38 s (54.07 ms each)[K
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Progress: Solving step 73/90 (81.11% of time interval complete)[K
Stats: 268 iterations in 14.49 s (54.07 ms each)[K
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Stats: 272 iterations in 14.65 s (53.88 ms each)[K
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Stats: 275 iterations in 14.79 s (53.77 ms each)[K
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Progress: Solving step 78/90 (86.67% of time interval complete)[K
Stats: 279 iterations in 14.95 s (53.59 ms each)[K
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Stats: 283 iterations in 15.12 s (53.41 ms each)[K
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Progress: Solving step 82/90 (91.11% of time interval complete)[K
Stats: 287 iterations in 15.27 s (53.22 ms each)[K
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Progress: Solving step 83/90 (92.22% of time interval complete)[K
Stats: 291 iterations in 15.42 s (52.99 ms each)[K
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Simulating 2 years, 24.22 weeks as 90 report steps 93%|▉| ETA: 0:00:01[K
Progress: Solving step 85/90 (94.44% of time interval complete)[K
Stats: 295 iterations in 15.58 s (52.81 ms each)[K
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Progress: Solving step 87/90 (96.67% of time interval complete)[K
Stats: 299 iterations in 15.74 s (52.64 ms each)[K
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Stats: 303 iterations in 15.91 s (52.52 ms each)[K
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Simulating 2 years, 24.22 weeks as 90 report steps 100%|█| Time: 0:00:16[K
Progress: Solved step 90/90[K
Stats: 307 iterations in 16.07 s (52.34 ms each)[K
╭────────────────┬──────────┬──────────────┬──────────╮
│ Iteration type │ Avg/step │ Avg/ministep │ Total │
│ │ 90 steps │ 96 ministeps │ (wasted) │
├────────────────┼──────────┼──────────────┼──────────┤
│ Newton │ 3.41111 │ 3.19792 │ 307 (0) │
│ Linearization │ 4.47778 │ 4.19792 │ 403 (0) │
│ Linear solver │ 10.8667 │ 10.1875 │ 978 (0) │
│ Precond apply │ 21.7333 │ 20.375 │ 1956 (0) │
╰────────────────┴──────────┴──────────────┴──────────╯
╭───────────────┬─────────┬────────────┬─────────╮
│ Timing type │ Each │ Relative │ Total │
│ │ ms │ Percentage │ s │
├───────────────┼─────────┼────────────┼─────────┤
│ Properties │ 2.6742 │ 5.11 % │ 0.8210 │
│ Equations │ 13.0228 │ 32.66 % │ 5.2482 │
│ Assembly │ 2.7994 │ 7.02 % │ 1.1282 │
│ Linear solve │ 2.4283 │ 4.64 % │ 0.7455 │
│ Linear setup │ 11.9643 │ 22.86 % │ 3.6730 │
│ Precond apply │ 0.9703 │ 11.81 % │ 1.8979 │
│ Update │ 1.1646 │ 2.23 % │ 0.3575 │
│ Convergence │ 1.6265 │ 4.08 % │ 0.6555 │
│ Input/Output │ 0.8849 │ 0.53 % │ 0.0849 │
│ Other │ 4.7436 │ 9.06 % │ 1.4563 │
├───────────────┼─────────┼────────────┼─────────┤
│ Total │ 52.3387 │ 100.00 % │ 16.0680 │
╰───────────────┴─────────┴────────────┴─────────╯Show the input data
The input data takes the form of a Dict:
case.input_dataDict{String, Any} with 6 entries:
"RUNSPEC" => OrderedDict{String, Any}("TITLE"=>"SPE 9", "DIMENS"=>[24, 25, 1…
"GRID" => OrderedDict{String, Any}("cartDims"=>(24, 25, 15), "CURRENT_BOX…
"PROPS" => OrderedDict{String, Any}("PVTW"=>Any[[2.48211e7, 1.0034, 4.3511…
"SUMMARY" => OrderedDict{String, Any}()
"SCHEDULE" => Dict{String, Any}("STEPS"=>OrderedDict{String, Any}[OrderedDict…
"SOLUTION" => OrderedDict{String, Any}("EQUIL"=>Any[[2753.87, 2.48211e7, 3032…We can also examine the for example RUNSPEC section, which is also represented as a Dict.
case.input_data["RUNSPEC"]OrderedCollections.OrderedDict{String, Any} with 13 entries:
"TITLE" => "SPE 9"
"DIMENS" => [24, 25, 15]
"OIL" => true
"WATER" => true
"GAS" => true
"DISGAS" => true
"FIELD" => true
"START" => DateTime("2015-01-01T00:00:00")
"WELLDIMS" => [26, 5, 1, 26, 5, 10, 5, 4, 3, 0, 1, 1, 10, 201]
"TABDIMS" => [1, 1, 40, 20, 1, 20, 20, 1, 1, -1 … -1, 10, 10, 10, -1, 5, 5…
"EQLDIMS" => [1, 100, 50, 1, 50]
"UNIFIN" => true
"UNIFOUT" => truePlot the simulation model
These plot are normally interactive, but if you are reading the published online documentation static screenshots will be inserted instead.
using GLMakie
plot_reservoir(case.model, states)
Plot the well responses
We can plot the well responses (rates and pressures) in an interactive viewer. Multiple wells can be plotted simultaneously, with options to select which units are to be used for plotting.
plot_well_results(ws)
Plot the field responses
Similar to the wells, we can also plot field-wide measurables. We plot the field gas production rate and the average pressure as the initial selection. If you are running this case interactively you can select which measurables to plot.
We observe that the field pressure steadily decreases over time, as a result of the gas production. The drop in pressure is not uniform, as during the period where little gas is produced, the decrease in field pressure is slower.
plot_reservoir_measurables(case, ws, states, left = :fgpr, right = :pres)
Example on GitHub
If you would like to run this example yourself, it can be downloaded from the JutulDarcy.jl GitHub repository as a script, or as a Jupyter Notebook
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