Reservoir simulation
This paper presents a fundamental research study with the main objective of building a mechanistic numerical model that captures the important mechanisms of polymer flooding through various mechanistic equations using a combined reservoir flow and geochemical numerical simulator.
The authors of this paper describe reservoir-fluid-geodynamics processes that explain the reasons behind varying oil compositions and properties within and across different reservoir compartments.
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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The authors of this paper describe a model-driven work flow developed for hydraulic fracturing design and execution that could be a resource for other shale plays with similar challenges worldwide. -
This paper presents a case study of integrated geomechanical and reservoir simulation with a developed fracture conductivity calculation work flow to evaluate well spacing and completions design.
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The authors of this paper write that computationally coupled models enable swift, accurate, and engineered decision-making for optimal asset development.
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The authors of this paper describe a procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables.
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This paper presents a physics-assisted deep-learning model to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models.
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This article presents the application of a reinforcement learning control framework based on the Deep Deterministic Policy Gradient. The crack propagation process is simulated in Abaqus, which is integrated with a reinforcement learning environment to control crack propagation in brittle material. The real-world deployment of the proposed control framework is also dis…
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This paper describes a work flow that integrates data analysis, machine learning, and artificial intelligence to unlock the potential of large relative permeability databases.
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The objective of this study was to establish an efficient optimization work flow to improve vertical and areal sweep in a sour-gas injection operation, thereby maximizing recovery under operation constraints.
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The objective of this paper is to present a fundamentals-based model of three-phase flow consistent with observation that avoids the pitfalls of conventional models such as Stone II or Baker’s three-phase permeability models.
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A new program offers an affordable way to figure out if salt precipitation could be behind underperforming gas wells and suggests a path to higher production.
