Reservoir simulation
This study applies Monte Carlo simulation and an XGBoost regression model to assess the influence of various formations, geologic provinces, tectonic-plate types, and boundary conditions on hydrogen concentrations.
This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.
This paper presents a novel reservoir engineering/reservoir simulation approach—a data-driven interwell-connectivity model augmented as a digital twin—to predict reservoir dynamics and optimize operations in the Changqing oil field of China.
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A critical step in proper design and optimization of any chemical-enhanced-oil-recovery (CEOR) process is appropriate and precise numerical simulations. -
This study explores the mechanisms contributing to oil recovery with numerical modeling of experimental work and investigates the effects of various parameters on oil recovery.
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This paper describes the first job in southeast Asia in developing horizontal-well placement in a turbidite environment.
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Producers face a number of decision-making challenges. Specifically, they must optimize field development and operational decisions in light of the complex interplay of fiscal, market, and reservoir variables.
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The story of unconventional oil and gas technology development has been focused on fractures. The formula has been more stages, more sand, and more water, targeting the most productive spots.
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The optimization algorithm used in this work is a hybrid genetic algorithm (HGA), which is the combination of GAs with artificial neural networks (ANNs) and evolution strategies (ESs).
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Reservoir-simulation-model inputs are numerous, and uncertainty is pervasive—before, during, and after development. With the pressure to deliver results quickly, how do we find the right balance?
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In upstream oil and gas, cloud computing is very immature because the industry has always been challenged by storage and computational capability. However, high-performance cloud computing may create an opportunity for smaller companies lacking infrastructure for scientific applications.
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Computational advances in reservoir simulation have made possible the simulation of thousands of reservoir cases in a practical time frame. This enables exhaustive exploration of subsurface uncertainty and development/depletion options.
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Because the uncertainty analysis is complex and time consuming, in this paper, a stochastic representation of the computer model, called an emulator, was constructed to quantify the reduction in the parameter input space.
