Reservoir characterization
This paper presents a novel methodology for assessing the rapid mineral carbonation of carbon dioxide through geochemical interactions with carbon-, magnesium-, and iron-rich minerals abundant in geological formations.
This study integrates physics-based constraints into machine-learning models, thereby improving their predictive accuracy and robustness.
This paper introduces a machine-learning approach that integrates well-logging data to enhance depth selection, thereby increasing the likelihood of obtaining accurate and valuable formation-pressure results.
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This paper describes an approach that combines rock typing and machine-learning neural-network techniques to predict the permeability of heterogeneous carbonate formations accurately. -
This study compares seven imputation techniques for predicting missing core-measured horizontal and vertical permeability and porosity data in two wells drilled in the North Rumaila oil field in southern Iraq.
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This study describes the performance of machine-learning models generated by the self-organizing-map technique to predict electrical rock properties in the Saman field in northern Colombia.
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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.
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The aim of this study is to address and discuss the reservoir engineering aspects of geological hydrogen storage.
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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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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.
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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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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 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.
