Reservoir characterization
Rock sample characterization, widely used in hydrocarbon exploration and production, is emerging as a critical tool for understanding and optimizing subsurface mineral extraction through in-situ leaching.
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 study describes the performance of machine-learning models generated by the self-organizing-map technique to predict electrical rock properties in the Saman field in northern Colombia. -
Fundamental research conducted to derive a transport model for ideal and partitioning tracers in porous media with two-phase flow that will allow fast and efficient characterization and selection of the correct tracer to be used in field applications.
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The aim of this study is to address and discuss the reservoir engineering aspects of geological hydrogen storage.
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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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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 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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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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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.
