Reservoir characterization
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.
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.
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 propose an artificial-intelligence-assisted work flow that uses machine-learning techniques to identify sweet spots in carbonate reservoirs. -
This paper describes an effort to use multiple technologies to better understand an Arkoma Basin reservoir and the interdisciplinary relationship between the reservoir’s subsurface hazards and a stimulation treatment.
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The main objective of this paper is to investigate the relationship between strain change and pressure change under various fractured reservoir conditions to better estimate conductive fractures and pressure profiles.
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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.
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Development and study of a new downhole bubblepoint pressure measurement technique, suitable for black oils and volatile oils, to augment downhole fluid analysis using optical spectroscopy.
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The authors of this paper describe an approach in which all available technologies are combined to improve understanding of reservoir depositional environments.
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The authors of this paper describe a project aimed at automating the task of cuttings descriptions with machine-learning and artificial-intelligence techniques, in terms of both lithology identification and quantitative estimation of lithology abundances.
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