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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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. -
In the complete paper, a novel hybrid approach is presented in which a physics-based nonlocal modeling framework is coupled with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs.
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In the complete paper, the authors reduce nonuniqueness and ensure physically feasible results in multiwell deconvolution by incorporating constraints and knowledge to methodology already established in the literature.
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In the complete paper, the authors derive a novel analytical solution to model the temperature signal associated with the shut-in during flowback and production periods.
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The complete paper demonstrates the benefits of honoring data measurements from a multitude of potential sources to help engineers do a better job of including more diagnostics into routine operations to provide additional insight and result in improved models and completion designs.
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Most history-matching studies have fixed resources—that is, the team of engineers and geoscientists is predetermined. Moreover, the deadlines are always very strict. This constrained scenario often leads to an unfortunate result: The quality of the study suffers.
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Proper lateral and vertical well spacing is critical for efficient development of unconventional reservoirs. Much research has focused on lateral well spacing but little on vertical spacing, which is challenging for stacked-bench plays such as the Permian Basin.
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The complete paper explores the use of multilevel derivative-free optimization for history matching, with model properties described using principal component analysis (PCA) -based parameterization techniques.
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The Russian company has built a computing cluster in St. Petersburg designed to generate digital twins of oil fields. The new distributed-computing system is capable of processing more than 100 gigabits per second, speeding up the digital-modeling process five-fold.
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In this paper, the authors show the limitation of CEOS for modeling reservoir behavior of liquid-phase black and volatile oil in highly undersaturated reservoirs.
