Reservoir simulation
The opening ceremony highlighted maximizing production sustainably to meet global demand, integration of simulation and optimization in a single platform with automation, and energy security.
The authors of this paper describe a three-way coupled modeling approach that integrates dynamic, geochemistry, and geomechanics models to obtain cumulative effects of all three changes to evaluate future carbon dioxide storage.
In this study, surfactants that can alter wettability or develop ultralow interfacial tension were identified through laboratory measurements for a target carbonate reservoir.
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The authors of this paper present an application of a Bayesian framework for uncertainty assessment and efficient history matching of a Permian CO2 enhanced oil recovery field for reliable production forecast. -
This paper presents results from evaluating the rate of thermal energy that can be extracted under various completion scenarios in end-of-life oil and gas wells using a transient flow simulator.
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This paper presents a numerical simulation work flow, with emphasis on hydraulic fracture simulation, that optimizes well spacing and completion design simultaneously.
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The authors discuss the development of a deep-learning model to identify errors in simulation-based performance prediction in unconventional reservoirs.
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The authors of this paper compare case studies from the Bakken and the STACK plays to conclude that mineralogy, petrophysics, and reservoir-condition differences between basins cause differences in the effect of fracture-driven interactions.
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The authors of this paper propose a novel approach to data-driven modeling for transient production of oil wells.
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This paper describes a novel distributed quasi-Newton derivative-free optimization method for reservoir-performance-optimization problems.
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The complete paper builds on existing tools in the literature to quantify the effect of changing well spacing on well productivity for a given completion design, using a new, simple, intuitive empirical equation.
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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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