Reservoir simulation
This study explores the feasibility of implementing in-situ carbon dioxide recycling for sequestration as a fit-for-purpose developmental strategy for a Malaysian gas field characterized by an initial carbon-dioxide content of approximately 60%.
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.
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The authors present an efficient workflow using an embedded discrete fracture model to simulate carbon-dioxide flow by use of conductive faults. -
This paper provides guidelines for thermal modeling for carbon capture and storage projects in a depleted gas field.
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The authors of this paper present a workflow designed to achieve maximum integration between analytical and modeling activities in carbon capture and storage projects.
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The authors of this paper present an adaptive grid-coarsening approach based on constraints that honor reservoir structure and stratigraphy, preserve fluid volumes and contacts, and retain resolution near wells.
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The SPE Reservoir Technical Discipline and Advisory Committee invite their Reservoir members worldwide to participate in a new survey aimed at assessing the current state of reservoir engineering across industry and academia. Deadline is 21 July 2025.
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This paper presents a fundamental research study with the main objective of building a mechanistic numerical model that captures the important mechanisms of polymer flooding through various mechanistic equations using a combined reservoir flow and geochemical numerical simulator.
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The authors of this paper describe reservoir-fluid-geodynamics processes that explain the reasons behind varying oil compositions and properties within and across different reservoir compartments.
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In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.
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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.
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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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