Reservoir simulation
This paper reviews the simultaneous supercritical CO2/brine aquifer injection and water-alternating-gas methods for geologic carbon sequestration and proposes a novel integration with saltwater-disposal wells.
The objective of this study is to numerically investigate system behavior when storing H2/natural gas (CH4) mixtures in aquifer-related underground gas storage, and the effect of gas composition and salinity on energy-recovery efficiency.
This paper addresses the difficulty in adjusting late-stage production in waterflooded reservoirs and proposes an integrated well-network-design mode for carbon-dioxide enhanced oil recovery and storage.
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This study explores pitfalls experienced when using capacitance/resistance modeling as a plug-and-play technique for waterflood optimization and discusses workarounds and mitigations to improve its reliability. -
This paper analyzes several configurations of convolutional neural networks suited for predicting upscaled fracture permeabilities and shape factors required to close a dual porosity/dual permeability model.
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The authors write that simple and straightforward observations on outcrops can be used to build 3D models that mimic geological relationships accurately.
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This paper presents an integrated work flow to model mechanical properties at sufficiently high resolution to honor accurately rock fabric and its effects on height and complexity and, thus, production.
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This paper presents a comprehensive comparison of two modeling-based approaches of fluid tracking for condensate allocation and gas usage.
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The authors develop a representative geostatistically based 3D model that preserves geological elements and eliminates uncertainty of reservoir properties and volumetric estimates for a Libyan field.
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The authors examine a theory that low resistivity in a Chinese reservoir is caused by bound water trapped in clay minerals and develop an improved model for production prediction of offset wells.
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In reviewing the long list of papers this year, it has become apparent to me that the hot topic in reservoir simulation these days is the application of data analytics or machine learning to numerical simulation and with it quite often the promise of data-driven work flows—code for needing to think about the physics less.
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The authors describe the process of building multiple scenario-based models to optimize development planning in preparation for the upcoming production phase of the Ichthys field offshore Australia.
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The authors demonstrate how artificial intelligence and machine learning can help build a purely data-driven reservoir simulation model that successfully history matches dynamic variables for wells in a complex offshore field and that can be used for production forecasting.
