Reservoir
This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.
This paper presents a novel reservoir engineering/reservoir simulation approach—a data-driven interwell-connectivity model augmented as a digital twin—to predict reservoir dynamics and optimize operations in the Changqing oil field of China.
This work uses a novel pseudosteady-state-based simulation to reduce training-data-generation cost while maintaining high-performance predictions of data-driven proxy models for carbon-sequestration projects.
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The Norwegian data company has launched a 3D seismic survey in the Equatorial Margin area. -
This paper presents a unique investigation into determining the sufficient concentration of hardness ions required to significantly reduce the adsorption of acrylamide-tertiary-butyl-sulfonate-based polymer with a focus on mitigating polymer retention in carbonate formations using softened brine.
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The deal comes only weeks after the private equity firm purchased a natural gas-fired plant operator.
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India’s state oil company is accepting proposals from potential technical service partners until 15 September for EOR projects in the Arabian Sea.
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The SPE IOR-EOR Terminology Review Committee has opened a period for public comments on a draft technical report.
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This paper describes a data-driven approach for liquid-loading detection and prediction that harnesses high-frequency gas-rate and tubinghead-pressure measurements to identify the onset of liquid loading and correct critical rates computed by empirical methods.
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This paper outlines the importance of numerical rate transient analysis for dry gas wells, describing a simple, fully penetrating planar fracture model.
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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 paper describes an approach that combines rock typing and machine-learning neural-network techniques to predict the permeability of heterogeneous carbonate formations accurately.
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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.
