Reservoir
This paper discusses waterflooding and field-development implications of subsea seawater treatment for improved oil recovery.
This paper presents laboratory-testing methods and evaluation criteria designed to improve understanding of both the origin and extent of formation damage associated with CO2 injection.
This work presents an evaluation for carbon capture, utilization, and storage projects in the UAE for two wells in a saline water aquifer with respect to caprock integrity and water sampling.
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This paper introduces a novel steam-sensitive flow-control device designed to restrict the production of steam and low-subcool liquids while allowing higher mobility of oil-phase fluids. -
The authors propose a deep-learning-based approach enabling near-real-time CO2-plume visualization and rapid data assimilation incorporating multiple geological realizations for predicting future CO2 plume evolution and area-of-review determination.
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This paper demonstrates how the integration of multiphysics downhole imaging with machine-learning techniques provides a significant advance in perforation-erosion analysis.
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This study integrates laboratory testing with reservoir simulation to evaluate the effectiveness of autonomous inflow-control valves in managing late-life steam-assisted gravity-drainage production challenges.
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Steam-assisted gravity drainage (SAGD) technology continues to advance rapidly, driven by improvements in numerical simulation, inflow-control technologies, fiber-optic monitoring, and real-time production optimization.
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The three featured papers illustrate how emerging computational methods—ranging from gradient-based optimization to data-driven proxies—are reshaping reservoir characterization, uncertainty assessment, and real-time decision support across diverse subsurface applications.
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In this study, forward simulation is executed by a commercial reservoir simulator while external code is developed for backward calculations.
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This paper presents a workflow that leverages a multiagent conversational system to integrate data, analytics, and domain expertise for improved completion strategies.
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Findings from two new SPE papers argue that the tight-rock sector needs to rethink longstanding assumptions about how hydraulic fractures form underground.
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In this study, the authors propose the use of a deep-learning reduced-order surrogate model that can lower computational costs significantly while still maintaining high accuracy for data assimilation or history-matching problems.
