Reservoir simulation
Virtual reality and related visualization technologies are helping reshape how the industry views 3D data, makes decisions, and trains personnel.
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.
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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. -
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.
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Schlumberger announced that ConocoPhillips will use its DELFI platform to move its data to the cloud.
