Reservoir simulation
The aim of this study is to incorporate detailed geological, petrophysical, and hydraulic fracturing models to better predict and mitigate the effects of interbench interactions.
The objective of this paper is to apply a developed workflow to determine the propped hydraulic fracture geometry in a horizontal multistage fractured well, incorporating production, pressure, and strain data.
This study explores the feasibility of implementing in-situ carbon dioxide recycling for sequestration as a fit-for-purpose developmental strategy for a Malaysian gas field characterized by an initial carbon-dioxide content of approximately 60%.
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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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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.
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Artificial intelligence (AI) and machine learning (ML) technologies have rapidly progressed and have significantly affected traditional reservoir engineering, bringing innovative methodologies to reservoir simulations. However, it is essential to understand that these AI and ML technologies are only as effective and trustworthy as the data they are trained on.
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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.
