Reservoir characterization
This paper introduces new methods to perform reliable permeability and saturation-dependent relative permeability measurements in organic-rich mudrock core samples using a pressure-decay setup.
The authors of this paper present a workflow designed to achieve maximum integration between analytical and modeling activities in carbon capture and storage projects.
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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The aim of this study is to address and discuss the reservoir engineering aspects of geological hydrogen storage. -
In this paper, the authors propose polymer-assisted water-alternating-gas (WAG) injection as an alternative method to reduce gas mobility while reducing the mobility of the aqueous phase and, consequently, improving WAG performance.
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This paper describes development-plan optimization and a probabilistic uncertainty study using Latin hypercube experimental design constrained to production performance in a deepwater Gulf of Mexico field.
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The authors of this paper propose a hybrid approach that combines physics with data-driven approaches for efficient and accurate forecasting of the performance of unconventional wells under codevelopment.
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This paper develops a deep-learning work flow that can predict the changes in carbon dioxide mineralization over time and space in saline aquifers, offering a more-efficient approach compared with traditional physics-based simulations.
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The authors integrated azimuths and intensities recorded by fiber optics and compared them with post-flowback production-allocation and interference testing to identify areas of conductive fractures and offset-well communication.
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The authors of this paper describe a seismic reprocessing campaign for an Egyptian oil field that had yielded poor seismic data.
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This paper describes a method with multitiered analysis to leverage machine-learning techniques to process passive seismic monitoring data, pumping and injection pressure, and rate for fracture and fault analysis.
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Recent technical papers have further shown the steady increase in the application of advanced seismic techniques and machine learning to mature to production “stranded” and “advantaged” hydrocarbon-bearing accumulations ; improve carbon capture, storage, and leak detection; and analyze naturally and artificially fractured reservoirs.
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Uncertainty comes in all scales and dimensions. This challenges us to learn at all scales possible, from the fume hoods in the laboratory to magnificently exposed outcrops and through deep narrow boreholes that drill through subsurface reservoirs. The combined efforts often convert learnings to actionable intelligence.
