JutulDarcyRe-thinking reservoir simulation in Julia

What is this? ​

JutulDarcy.jl is a general high-performance purpose porous media simulator toolbox (CO2 sequestration, gas/H2 storage, oil/gas fields) written in Julia based on Jutul.jl, developed by the Applied Computational Science group at SINTEF Digital.

A few highlights:

• Immiscible, black-oil, compositional, CO2-brine and geothermal systems

• Fully differentiable through adjoint method (history matching of parameters, optimization of well controls)

• High performance, with optional support for compiling MPI parallel binaries

• Consistent discretizations

• Industry standard input formats - or make your own model as a script

• 3D visualization and tools for post-processing of simulation results

Quick start guide ​

Getting started is the main setup guide that includes the basics of installing Julia and creating a Julia environment for JutulDarcy.jl, written for users who may not already be familiar with Julia package management.

If you want to get started quickly: Install Julia and add the following packages together with a Makie backend library to your environment of choice using Julia's package manager Pkg:

using Pkg
Pkg.add("GLMakie")    # Plotting
Pkg.add("Jutul")      # Base package
Pkg.add("JutulDarcy") # Reservoir simulator

To verify that everything is working, we have a minimal example that runs a industry standard input file and produces interactive plots. Note that interactive plotting requires GLMakie, which may not work if you are running Julia over SSH.

using JutulDarcy, GLMakie
spe9_dir = JutulDarcy.GeoEnergyIO.test_input_file_path("SPE9")
file_path = joinpath(spe9_dir, "SPE9.DATA")
case = setup_case_from_data_file(file_path)
result = simulate_reservoir(case)
plot_reservoir_simulation_result(case.model, result)

To get access to all the examples, you can generate a folder that contains the examples locally, you can run the following code to create a folder jutuldarcy_examples in your current working directory:

using JutulDarcy
generate_jutuldarcy_examples()

These examples can then be run using include("jutuldarcy_examples/example_name.jl") or opened in an editor to be run line by line. Alternatively, you can download all examples as Jupyter Notebooks.

Citing JutulDarcy ​

If you use JutulDarcy for a scientific publication, please cite the main paper the following way:

O. Møyner, (2024). JutulDarcy.jl - a Fully Differentiable High-Performance Reservoir Simulator Based on Automatic Differentiation. ECMOR 2024, https://doi.org/10.3997/2214-4609.202437111

@article{jutuldarcy_ecmor_2024,
   author = "M{\o}yner, O.",
   title = "JutulDarcy.jl - a Fully Differentiable High-Performance Reservoir Simulator Based on Automatic Differentiation", 
   year = "2024",
   volume = "2024",
   number = "1",
   pages = "1-9",
   doi = "https://doi.org/10.3997/2214-4609.202437111",
   publisher = "European Association of Geoscientists \& Engineers",
   issn = "2214-4609",
}

A few of the packages used by JutulDarcy ​

JutulDarcy.jl builds upon many of the excellent packages in the Julia ecosystem. Here are a few of them, and what they are used for:

• ForwardDiff.jl implements the Dual number class used throughout the code

• SparsityTracing.jl provides sparsity detection inside Jutul

• Krylov.jl provides the iterative linear solvers

• ILUZero.jl for ILU(0) preconditioners

• AlgebraicMultigrid.jl for AMG preconditioners

• HYPRE.jl for robust AMG preconditioners with MPI support

• PartitionedArrays.jl for MPI assembly and linear solve

• CUDA.jl for CUDA-GPU support

• AMGX.jl for AMG on CUDA GPUs

• Tullio.jl for automatically optimized loops and Polyester.jl for lightweight threads

• TimerOutputs.jl and ProgressMeter.jl gives nice output to terminal.

• Makie.jl is used for the visualization features

• MultiComponentFlash.jl provides many of the compositional features

...and many more directly, and indirectly - see the Project.toml and Manifest files for a full list!