Reservoir
In a study that applied alternative carbon carrier technology to enhanced oil recovery (EOR) scenarios, researchers at The University of Texas at Austin found that the new method recovered up to 19.5% more oil and stored up to 17.5% more carbon than conventional EOR methods.
This study assesses the advantages, constraints, and necessary enhancements of both passive and active electromagnetic techniques in the context of carbon capture and storage.
This paper presents a novel workflow with multiobjective optimization techniques to assess the integration of pressure-management methodologies for permanent geological carbon dioxide storage in saline aquifers.
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Researchers from Skoltech have trained a neural network to recognize rock samples in core box images efficiently. The process has sped up analysis by up to 20 times and made it possible to automate the description of rock samples. -
Chevron assets in Texas and Colorado have earned high marks for control of methane emissions under a pilot program with Project Canary, paving the way for its sale of responsibly sourced gas.
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The paper investigates estimation of optimal design variables that maximize net present value for life-cycle production optimization during a single-well CO2 huff ‘n’ puff process in unconventional oil reservoirs.
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Unconventional resource exploitation has three key technology and application focus areas—increasing implementation of fracture diagnostics, enhancing production and recovery from new well completions as well as existing wells through improved stimulation and restimulation design and execution quality, and developing enhanced oil recovery methods.
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The paper presents key lessons learned in efficiently designing pressure-buildup tests in tight sandstone reservoirs.
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This paper discusses the challenges presented by factors such as infrastructure, types of primary energy, and investment.
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The paper describes an approach to history matching and forecasting that does not require a reservoir simulation model, is data driven, and includes a physics model based on material balance.
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This paper presents a physics-informed neural network technique able to use information from fluid-flow physics as well as observed data to model the Buckley-Leverett problem.
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This paper illustrates efforts undertaken to evaluate high-viscosity friction reducers and crosslinked gel products, their successful field application, and the importance of maintaining an effective field/laboratory/field cycle.
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Whether it be Derrick Turk asking us to “resist the temptation to accrue vocabulary rather than understanding” or Mark Bentley telling us that “if you can sketch it you can model it,” there does seem to be a growing pushback against the notions that the modeling/simulation process can be successfully shrink-wrapped and that fundamental understanding is increasingly a …
