Reservoir characterization
This work investigates the root cause of strong oil/water emulsion and if sludge formation is occurring within the reservoir using a robust integrated approach.
In this work, a perturbed-chain statistical associating fluid theory equation of state has been developed to characterize heavy-oil-associated systems containing polar components and nonpolar components with respect to phase behavior and physical properties.
The paper describes a parameter inversion of reservoirs based on featured points, using a semi-iterative well-test-curve-matching approach that addresses problems of imbalanced inversion accuracy and efficiency.
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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. -
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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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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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.
