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 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. -
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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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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AIQ, ADNOC, and SLB announced a new software suite that integrates artificial intelligence into reservoir analysis and field development projects.
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The project aims to reimage a 6,400-km2 seismic data set near the recently discovered Baleine field.
