Reservoir simulation
The authors present an open-source framework for the development and evaluation of machine-learning-assisted data-driven models of CO₂ enhanced oil recovery processes to predict oil production and CO₂ retention.
The authors of this paper propose hybrid models, combining machine learning and a physics-based approach, for rapid production forecasting and reservoir-connectivity characterization using routine injection or production and pressure data.
Virtual reality and related visualization technologies are helping reshape how the industry views 3D data, makes decisions, and trains personnel.
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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. -
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.
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The paper describes an end-to-end deep surrogate model capable of modeling field and individual-well production rates given arbitrary sequences of actions.
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Physics-based simulations plus machine-learning exercises are yielding a more comprehensive look at production volumes from unconventional assets.
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As we see in these papers, new tools and techniques are being developed to match the ability of engineers to meet the challenges posed by assets such as shale reservoirs and maturing fields.
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This paper presents a step-by-step work flow to facilitate history matching numerical simulation models of hydraulically fractured shale wells.
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In the complete paper, the authors present a novel approach that uses data-mining techniques on operations data of a complex mature oil field in the Gulf of Suez that is currently being waterflooded.
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The complete paper discuses a well with a history of sand production that exhibits long cyclic slugging behavior.
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Fit-for-purpose tactics likely will be of ever-increasing focus going forward. If it is not adding value, it should not be done. But “fit for purpose” encompasses a wide range of possibilities—leveraging new approaches as well as learning from old approaches and improving current approaches.
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In the complete paper, the authors revisit fundamental concepts of reservoir simulation in unconventional reservoirs and summarize several examples that form part of an archive of lessons learned.
