Parallel Compositional Reservoir Simulation on Clusters of PCs

Abate, Jason; Wang, Peng; Sepehrnoori, Kamy

doi:10.1177/109434200101500102

Cited by 20 publications

(18 citation statements)

References 4 publications

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“…Another alternative is to use an Adaptive implicit method (AIM), formulated to reduce the number of implicit unknowns required by FIM and to alleviate the time step restrictions associated with moving compositions explicitly throughout an entire reservoir (Thomas and Thurnau (1983)). The computation time required for simulation can of course also be reduced through parallelization (Abate et al (2001) and Wang et al (1999)), or by including adaptive mesh refinement (AMR) as in Sammon (2003). AMR focuses computational effort in regions near displacement fronts to accurately capture the local displacement efficiency.…”

Section: Existing Solversmentioning

confidence: 99%

Industrial Compositional Streamline Simulation for Efficient and Accurate Prediction of Gas Injection and WAG Processes

Gerritsen¹

2008

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Executive SummaryGas-injection processes are widely and increasingly used for enhanced oil recovery (EOR). In the United States, for example, EOR production by gas injection accounts for approximately 45% of total EOR production and has tripled since 1986. The understanding of the multiphase, multicomponent flow taking place in any displacement process is essential for successful design of gas-injection projects. Due to complex reservoir geometry, reservoir fluid properties and phase behavior, the design of accurate and efficient numerical simulations for the multiphase, multicomponent flow governing these processes is nontrivial. In this work, we developed, implemented and tested a streamline based solver for gas injection processes that is computationally very attractive: as compared to traditional Eulerian solvers in use by industry it computes solutions with a computational speed orders of magnitude higher and a comparable accuracy provided that cross-flow effects do not dominate. We contributed to the development of compositional streamline solvers in three significant ways: improvement of the overall framework allowing improved streamline coverage and partial streamline tracing, amongst others; parallelization of the streamline code, which significantly improves wall clock time; and development of new compositional solvers that can be implemented along streamlines as well as in existing Eulerian codes used by industry.We designed several novel ideas in the streamline framework. First, we developed an adaptive streamline coverage algorithm. Adding streamlines locally can reduce computational costs by concentrating computational efforts where needed, and reduce mapping errors. Adapting streamline coverage effectively controls mass balance errors that mostly result from the mapping from streamlines to pressure grid. We also introduced the concept of partial streamlines: streamlines that do not necessarily start and/or end at wells. This allows more efficient coverage and avoids the redundant work generally done in the near-well regions. We improved the accuracy of the streamline simulator with a higher order mapping from pressure grid to streamlines that significantly reduces smoothing errors, and a Kriging algorithm is used to map from the streamlines to the background grid. The higher accuracy of the Kriging mapping means that it is not essential for grid blocks to be crossed by one or more streamlines. The higher accuracy comes at the price of increased computational costs, but allows coarser coverage and so does not generally increase the overall costs of the computations. To reduce errors associated with fixing the pressure field between pressure updates, we developed a higher order global time-stepping method that allows the use of larger global time steps. Third-order ENO schemes are suggested to propagate components along streamlines. Both in the two-phase and three-phase experiments these ENO schemes outperform other (higher order) upwind schemes. Application of the third order ENO scheme leads to ii...

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Section: Existing Solversmentioning

confidence: 99%

Industrial Compositional Streamline Simulation for Efficient and Accurate Prediction of Gas Injection and WAG Processes

Gerritsen¹

2008

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“…The simulation domain comprises of 500 ft in the x direction, 500 ft in the y direction and 100 ft in the z direction. The injector well is located at (1,1,1) and the producer well is located at (5,5,1). Gas is injected into the reservoir and it pushes the oil towards the producer.…”

Section: Two Dimensional Quarter Five Spot Casementioning

confidence: 99%

“…The initial water saturation in the reservoir is 0.1. The only injector is located at gridblock (1,1,1) and the only producer is located at gridblock (40,1,1). The initial reservoir pressure is 1500 psia.…”

Section: Buckley Leverett 1-d Water Floodmentioning

confidence: 99%

A New Generation Chemical Flooding Simulator

Pope¹,

Sepehrnoori²,

Delshad³

2002

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“…Many previous test runs have been performed using the fully implicit EOS compositional model to simulate gas injection (Abate et al, 2001;Uetani et al, 2002).…”

Section: Permeability Reductionmentioning

confidence: 99%

A New Generation Chemical Flooding Simulator

Pope¹,

Sepehrnoori²,

Delshad³

2004

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The premise of this research is that a general-purpose reservoir simulator for several improved oil recovery processes can and should be developed so that highresolution simulations of a variety of very large and difficult problems can be achieved using state-of-the-art algorithms and computers. Such a simulator is not currently available to the industry. The goal of this proposed research is to develop a newgeneration chemical flooding simulator that is capable of efficiently and accurately simulating oil reservoirs with at least a million gridblocks in less than one day on massively parallel computers. Task 1 is the formulation and development of solution scheme, Task 2 is the implementation of the chemical module, and Task 3 is validation and application. We have made significant progress on all three tasks and we are on schedule on both technical and budget. In this report, we will detail our progress on Tasks 1 through 3 for the first half of the third year of the project.iv

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Parallel Compositional Reservoir Simulation on Clusters of PCs

Cited by 20 publications

References 4 publications

Industrial Compositional Streamline Simulation for Efficient and Accurate Prediction of Gas Injection and WAG Processes

Industrial Compositional Streamline Simulation for Efficient and Accurate Prediction of Gas Injection and WAG Processes

A New Generation Chemical Flooding Simulator

A New Generation Chemical Flooding Simulator

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