Reservoir
This case study describes how edge computing and industrial internet of things platforms were deployed to automate and optimize production operations across four distinct basins.
This case study presents a procedure in which the operator compared production from wells with adjusted wettability to a control group, finding that the adjustments resulted in significant improvements in production and reductions in produced water.
This year’s selected papers showcase meaningful advances across condensate‑rich tight gas, tight sandstones, and coalbed methane reservoirs, each contributing new tools for improving predictability and field-development efficiency.
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New strategies for protecting metal infrastructure emerge as operators fine-tune a corrosion threat screening process and develop a new method for tracking inhibitor effectiveness. -
Interfacial tension keeps oil and water separate by resisting the mixing of their molecules at the surface. Learn how industry experts measure this force to diagnose fluid behavior.
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The SPE Reservoir Advisory Committee has updated its "state of the reservoir technical discipline" document. The new edition is available for free download.
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The operator’s deepwater discovery in the Gulf of Mexico is potentially commercial, and government analysis indicates the gulf holds 1.3 billion BOE more reserves than estimated.
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The test marks a milestone in the Poseidon CCS project, which aims to store carbon dioxide in the depleted gas reservoir below the Leman development in the southern North Sea.
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The company credited its theory of shale oil enrichment for the significant increase in the quantity of proven reserves at the field.
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In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.
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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.
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This study presents a production-optimization method that uses a deep-learning-based proxy model for the prediction of state variables and well outputs to solve nonlinearly constrained optimization with geological uncertainty.
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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.
