Reservoir characterization
The objective of this study is to numerically investigate system behavior when storing H2/natural gas (CH4) mixtures in aquifer-related underground gas storage, and the effect of gas composition and salinity on energy-recovery efficiency.
The authors describe the effectiveness of an electromagnetic look-ahead service while drilling in terms of providing accurate formation profiles ahead of the bit to optimize geostopping efficiency.
In the past year, publications on CO2, natural gas, and hydrogen storage have increasingly focused on the design, evaluation, and optimization of storage plans. These efforts encompass a broad spectrum of challenges and innovations, including the expansion of storage reservoirs from depleted gas fields and saline aquifers to stratified carbonate formations and heavy-o…
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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. -
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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The authors of this paper propose an artificial-intelligence-assisted work flow that uses machine-learning techniques to identify sweet spots in carbonate reservoirs.
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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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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.
