Reservoir simulation
This study assesses the advantages, constraints, and necessary enhancements of both passive and active electromagnetic techniques in the context of carbon capture and storage.
This paper presents a novel workflow with multiobjective optimization techniques to assess the integration of pressure-management methodologies for permanent geological carbon dioxide storage in saline aquifers.
The authors present an efficient workflow using an embedded discrete fracture model to simulate carbon-dioxide flow by use of conductive faults.
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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. -
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.
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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).
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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?
