README
Purpose of these datasets is using them for testing using MRST and its derivative Proof of Concept (POC) in Pyhon using the machine learning library PyTorch.
Since PyTorch
has builtin functionality to work with Graphics Processing Units or GPUs, we expect demonstrating before embarking in the full porting of MRST, that PyTorch
GPUbased tensors could significantly reduce compute time during reservoir simulation.
Evaluation for Proof of Concept
The steps are the following:

Find the Partial Differential Equations (PDE) that constitute the code of the MRST solvers.
 Test the running times of the solvers using Matlab and Octave. Some code for testing is available in the latest book An Introduction to Reservoir Simulation Using MATLAB, Octave by KnutAndreas Lie. See Appendix.
 Code is being tested for performance under Matlab and Octave. Code will be published in a separate repository.

Replicate the functionality in Python using PyTorch for GPUs.
 Convert the Matlab code to PyTorch
 Measure the compute time of the original
MRST
solvers.
If the compute times are 10 to 100 faster in PyTorch
, we will proceed with converting more Matlab code to PyTorch tensor based calculations.
Datasets
 MRST (downloaded)
 SPE9
 SPE10
 CaseB4
 SAIGUP
 OPM
 SPE1
 SPE9
 SPE10: model1, model2
 PUNQS3
 SPE1
 SPE2
 SPE3
 SPE5
 SPE6
 SPE9
 SPE9petrofaq
 SPE10
 VOLVE
Notes
 SPE10 from MRST contains
.dat
and.mat
files  Not all datasets from
OPM
included here; only SPE, SPE9 and SPE10.  SPE10 does not download using MRST2018. It had to be downloaded from another source. MRST2019 does download SPE10 normally
 VOLVE and PUNQS3 available for reference at this time. Tests will mainly focused to datasets available in MRST first.