Reservoir characterization
Rock sample characterization, widely used in hydrocarbon exploration and production, is emerging as a critical tool for understanding and optimizing subsurface mineral extraction through in-situ leaching.
This paper presents laboratory-testing methods and evaluation criteria designed to improve understanding of both the origin and extent of formation damage associated with CO2 injection.
This work presents an evaluation for carbon capture, utilization, and storage projects in the UAE for two wells in a saline water aquifer with respect to caprock integrity and water sampling.
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Large geological models are needed for modeling the subsurface processes in geothermal, carbon-storage, and hydrocarbon reservoirs. The size of these models contributes to the computational cost of history matching, engineering optimization, and forecasting. To reduce this cost, low-dimensional representations need to be extracted. Deep-learning tools, such as autoenc… -
This paper presents agile technologies that integrate data management, data-quality assessment, and predictive machine learning to maximize asset value using underused legacy core data.
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The authors of this paper use a pattern-review technique in a complex brownfield as a tool to understand reservoir connectivity and dynamic fluid movements across the field.
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The authors of this paper describe a fiber-optic diagnostic method based on Rayleigh frequency shift that provides new insights about the characteristics of the near-wellbore region during production.
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The authors of this paper describe a project that integrates carbonate stratigraphic forward models of outcropping formations and reservoir-modeling work flows.
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This paper presents a work flow that has been applied to crossdipole sonic data acquired in a vertical pilot well drilled in the Permian Basin.
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As we continue to advance the capability in the laboratory environment to test downhole condition measurements experimentally, the tools we are using appear to be bridging the subsurface characterization with the production results. Continued focus on unconventionals is complemented with a renewed focus on conventional research as well.
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The paper describes a method to match reaction kinetics from coreflooding experiments.
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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.
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The paper presents key lessons learned in efficiently designing pressure-buildup tests in tight sandstone reservoirs.
