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Title: Accelerating Compositional Reservoir Simulation with GPUs and Advanced Solvers

Abstract

The safe and efficient recovery of hydrocarbons requires a detailed understanding of the dynamics of the multiphase flow of oil, water and gas through subsurface rock, as well as the coupling of this flow to injection and recovery wells. Reservoir simulation, which solves the nonlinear partial differential equations governing this flow, plays a crucial role in the oil and gas industry. It enables engineers to predict recovery and to determine the optimal locations for new wells, as well as the ideal control parameters for existing ones. Since hydrocarbon reservoirs are typically obscured by thousands of feet of rock, empirical data is limited and indirect, no competitive alternative to numerical reservoir simulation exists. The technology has thus been employed for several decades in the energy industry. Over the years, simulation models have evolved from a few tens of cells yielding only crude qualitative predictions to higher resolution models with hundreds of thousands of cells that can provide greater accuracy. However, the poor computational performance provided by market leading simulators has prevented the industry from simulating models large enough to accurately match the observed productions. This project addressed the implementation of a compositional treatment of hydrrocarbons for reservoir simulation using GPUs. Themore » purpose is to show step-out performance capability and enable new and more advanced simulation methodologies.« less

Authors:
 [1];  [1]
  1. Stone Ridge Technology, Bel Air, MD (United States)
Publication Date:
Research Org.:
Stone Ridge Technology, Bel Air, MD (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1481726
Report Number(s):
DOE-SRT-SC0015214
DOE Contract Number:  
SC0015214
Type / Phase:
SBIR (Phase II)
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
02 PETROLEUM; 97 MATHEMATICS AND COMPUTING; gpu; reservoir simulation; compositional; petroleum; eos

Citation Formats

Esler, Kenneth, and Natoli, Vincent. Accelerating Compositional Reservoir Simulation with GPUs and Advanced Solvers. United States: N. p., 2018. Web.
Esler, Kenneth, & Natoli, Vincent. Accelerating Compositional Reservoir Simulation with GPUs and Advanced Solvers. United States.
Esler, Kenneth, and Natoli, Vincent. Sun . "Accelerating Compositional Reservoir Simulation with GPUs and Advanced Solvers". United States.
@article{osti_1481726,
title = {Accelerating Compositional Reservoir Simulation with GPUs and Advanced Solvers},
author = {Esler, Kenneth and Natoli, Vincent},
abstractNote = {The safe and efficient recovery of hydrocarbons requires a detailed understanding of the dynamics of the multiphase flow of oil, water and gas through subsurface rock, as well as the coupling of this flow to injection and recovery wells. Reservoir simulation, which solves the nonlinear partial differential equations governing this flow, plays a crucial role in the oil and gas industry. It enables engineers to predict recovery and to determine the optimal locations for new wells, as well as the ideal control parameters for existing ones. Since hydrocarbon reservoirs are typically obscured by thousands of feet of rock, empirical data is limited and indirect, no competitive alternative to numerical reservoir simulation exists. The technology has thus been employed for several decades in the energy industry. Over the years, simulation models have evolved from a few tens of cells yielding only crude qualitative predictions to higher resolution models with hundreds of thousands of cells that can provide greater accuracy. However, the poor computational performance provided by market leading simulators has prevented the industry from simulating models large enough to accurately match the observed productions. This project addressed the implementation of a compositional treatment of hydrrocarbons for reservoir simulation using GPUs. The purpose is to show step-out performance capability and enable new and more advanced simulation methodologies.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {11}
}

Technical Report:
This technical report may be released as soon as November 11, 2022
Other availability
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