Reservoir characterization
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.
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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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.
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This paper sheds light on newer frontiers of tracer applications with unconventional uses to gain flow insights from an oil and gas reservoir.
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This paper presents a comprehensive technical review of applications of distributed acoustic sensing.
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The authors of this paper describe a technology built on a causation-based artificial intelligence framework designed to forewarn complex, hard-to-detect state changes in chemical, biological, and geological systems.
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Decades of experience injecting fluids into the ground has revealed a fundamental truth: No two injection sites are the same. A thorough understanding of site-specific conditions is essential to ensure safe and secure long-term subsurface disposal of carbon dioxide.