Reservoir simulation
In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.
This study presents a production-optimization method that uses a deep-learning-based proxy model for the prediction of state variables and well outputs to solve nonlinearly constrained optimization with geological uncertainty.
In this work, a perturbed-chain statistical associating fluid theory equation of state has been developed to characterize heavy-oil-associated systems containing polar components and nonpolar components with respect to phase behavior and physical properties.
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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. -
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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Whether it be Derrick Turk asking us to “resist the temptation to accrue vocabulary rather than understanding” or Mark Bentley telling us that “if you can sketch it you can model it,” there does seem to be a growing pushback against the notions that the modeling/simulation process can be successfully shrink-wrapped and that fundamental understanding is increasingly a …
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This paper evaluates learnings from the past 30 years of methods that aim to quantify the uncertainty in the subsurface using multiple realizations, describing major challenges and outlining potential ways to overcome them.
