Reservoir simulation
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.
This work presents the development of fast predictive models and optimization methodologies to evaluate the potential of carbon-dioxide EOR and storage operations quickly in mature oil fields.
The authors of this paper apply a deep-learning model for multivariate forecasting of oil production and carbon-dioxide-sequestration efficiency across a range of water-alternating-gas scenarios using field data from six legacy carbon-dioxide enhanced-oil-recovery projects.
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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. -
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.
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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.
