Reservoir simulation
In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.
This study presents a production-optimization method that uses a deep-learning-based proxy model for the prediction of state variables and well outputs to solve nonlinearly constrained optimization with geological uncertainty.
In this work, a perturbed-chain statistical associating fluid theory equation of state has been developed to characterize heavy-oil-associated systems containing polar components and nonpolar components with respect to phase behavior and physical properties.
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This paper addresses the challenges in modeling highly unstable waterflooding, using both a conventional Darcy-type simulator and an adaptive dynamic prenetwork model, by comparing the simulated results with experimental data including saturation maps. -
Conventional inflow-performance-relationship (IPR) models are used in coupled wellbore/reservoir transient simulations, even if bottomhole-pressure conditions are assumed to be constant on the derivation of such IPR models.
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Because of their heterogeneity, carbonate reservoirs are more difficult to model than clastic reservoirs. The main difficulty comes from the number of different pore types, compared with the typical interparticle pore type in clastics.
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A fast, integrated reservoir modeling tool used by Eni at Norway’s Goliat field generated an ensemble of models that helped confirm the location of previously identified infill drilling targets and identify several new infill locations.
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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.
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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).
