Reservoir Simulation Publications

This work highlights recent advancements in ECHELON to model CCS applications.

A workflow combining a GPU-based full physics reservoir simulator with a FNO-based machine learning model on the cloud performed 50 times faster. ML model demonstrated reasonable agreement with HPC full physics simulation in both the well placement optimization and scenarios with geological uncertainties. The developed workflow provides a foundation for building advanced subsurface applications aided by machine learning and AI approaches.

This paper shows the first steps towards the integration of RTM capabilities in ECHELON

This paper introduces the architecture of an intelligent reservoir simulation assistant aimed at increasing the productivity of reservoir engineers, reducing repetitive tasks, and supporting less experienced engineers in honing their skills.

We are developing a generative AI assistant to lower the barrier for building and deploying numerical simulation models on the GPU-based reservoir simulation platform ECHELON.

Accurately reproducing the coupling of fluid flow in porous medium and rock mechanics is crucial for the modelling of CO2 geological storage in order to properly evaluate and prevent the risk of inducing fault instability during injection operations. As an alternative to using monolithic flow/mechanical suites, the process can be modelled by linking individual codes, i.e., a reservoir simulator for flow and a geomechanical package to account for fluid induced stress change, which tackle the two problems sequentially. We developed a flexible numerical framework where different coupling logics can be selected, i.e., one-way coupling, two-way coupling, and explicit coupling, which are characterized by different levels of accuracy and computational costs.

Decision making workflows in the energy industry require the capability to simulate hundreds if not thousands of models in a practical timeframe. In this paper we show how these capabilities can be achieved by exploiting top-class high performance computing resources available on premise.

We present the development of ECHELON coupling the model of Zohr, a super giant deepwater gas reservoir requiring a multi-million active cells grid with dual porosity/dual permeability formulation, to its gathering network.

ECHELON can be used seamlessly to build and run coupled multi-reservoir models and has been applied in contexts ranging from deep offshore to unconventionals. This paper, written with Eni, was presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition in Abu Dhabi.

GPU-based simulation reduces run times of dynamic models and in addition, economizes the hardware requirements offering a single HPC solution. This paper, presented during ADIPEC 2022, summarizes the various considerations that led to the selection of the appropriate HPC hardware and illustrates the benefits in terms of simulation speed-up of using a full-GPU reservoir simulator.

A key requirement for simulating the capture and sequestration of CO2 in saline aquifers or depleted hydrocarbon fields is the ability to model its solubility in brine. This poster, presented at the 16th GHGT held in Lyon, France in 2022, illustrates how our GPU reservoir simulator ECHELON can account for said solubility using an advanced equation of state with close to zero computational cost.

Here we present the results of a comparative study between a newly developed EHM tool that aims at leveraging the GPU parallelism, and a commercial third-party EHM software as a benchmark. Both are tested on a real case.

We prove that the benefits of GPU-based reservoir simulation persist with a complex fluid description through the extension of a commercial GPU-based black-oil simulator to include a compositional description based on standard cubic equations of state (EOSs).

Surface facility network solvers improve reservoir simulation models by providing crucial back-pressure information for wells to the simulator. The integration of optimization takes the approach one step further by enabling well allocation to be based on arbitrary well and network constraints. This paper was prepared for presentation at the SPE.

ECHELON software, using NVIDIA's multi-processing service and multi-GPU, improves the performance of reservoir simulations.

Geological models are preferably built with very fine grids in a 3D dimensional geometry, but simulating such detailed models with sizes in the 100's of millions of cells is a huge challenge for current commercial CPU simulators.

This paper highlights the principles of a full-GPU (Graphics Processing Unit) reservoir simulator, currently approaching feature parity with traditional CPU-based codes. The approach exhibits fine-grained parallelism beyond that of CPU-based and hybrid CPU-GPU solutions; consequent performance improvements enable modeling of giant carbonate fields with limited computing resources. Additionally, large black-oil models are memory-bound, and GPU bandwidth has shown significant progress with every generational release of new hardware.

Equation-of-state (EOS) based compositional simulations accurately capture the dynamics of reservoirs with strong compositional effects.

This paper discusses the application of massively-parallel GPU flow-simulation to rapidly compare alternate reservoir development cases in the Bakken/Three Forks using vertically offset stacks of horizontal wells.

This paper discusses application of massively-parallel GPU flow-simulation for the Bakken reservoir. Compared to CPU simulation, GPUs allow larger, faster numerical models (10+ million cells).

Demands for higher fidelity and predictive capability from reservoir simulation have resulted in the development of reservoir simulation models in the hundreds of millions of cells. When combined with the need to quickly assess many realizations of such models, the cluster size and power requirements can become impractical. GPUs provide an extremely dense and efficient computational platform that can help reduce the required hardware footprint and power envelope.

There are numerous complex characteristics that impact the long-term decline behavior of wells in tight oil and gas reservoirs. We present our efforts to help eliminate this tradeoff by building a fully-implicit black-oil simulator that combines recent advances in simulation algorithms with the high performance of GPUs.

Higher stakes from deep-ocean drilling, increasing complexity from unconventional reservoirs, and an overarching desire for a higher-fidelity subsurface description have led to a demand for reservoir simulators capable of modelling many millions of cells in minutes.

We present an efficient, robust and fully GPU-accelerated aggregation-based algebraic multigrid preconditioning technique for the solution of large sparse linear systems.

In reservoir simulation, the elliptic character of the pressure subsystem and the inhomogeneous permeability field result in extremely slow convergence for conventional iterative solvers.

Kirchhoff Time Migration (KTM) is a traditional seismic imaging technique used by geophysicists, a numerical method related to the Greens function solution of the scalar wave equation.

Our goal here is to evaluate the various methodologies for implementation in a tool that should be fast, accurate, scalable and extensible to the most general specification of velocity field and non-Cartesian grids.

Over the past several decades, reservoir simulation has progressed in capability from coarse two-dimensional models to inhomogeneous and detailed three-dimensional models, providing a high degree of fidelity to empirical production rates.