Reservoir simulation
This paper presents a case study from a mature field redevelopment project where pulsed neutron logging was integrated with advanced reservoir modeling to improve the understanding of fluid-contact dynamics and optimize new horizontal well placement.
This paper aims to showcase examples of how integrated analysis of surveillance data in the Azeri-Chirag-Gunashli field has improved reservoir understanding and informed reservoir-management decisions.
This work applies fuzzy logic, a well-established artificial-intelligence technique, to quantitatively assess connectivity between injectors and producers in a giant presalt field in the Santos Basin of Brazil.
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A numerical simulation study based on experimental data of 2D and 3D models is presented to examine immiscible fingering during field-scale polymer-enhanced oil recovery. -
Virtual reality and related visualization technologies are helping reshape how the industry views 3D data, makes decisions, and trains personnel.
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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.
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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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This paper presents a specialized workflow that aims to quantify the severity of condensate banking and subsequently optimize reservoir development strategies for a deep formation in the Permian Basin.
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This paper describes numerical modeling studies of fracture-driven interactions using a coupled hydraulic-fracturing-propagation, reservoir-flow, and geomechanics tool.
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A proposed integrated workflow aims to guide prediction and mitigating solutions to reduce casing-deformation risks and improve stimulation efficiency.
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This paper presents the processes of identifying production enhancement opportunities, as well as the methodology used to identify underperforming candidates and analyze well-integrity issues, in a brownfield offshore Malaysia.
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This paper presents a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight-reservoir forecasting.
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The authors of this paper review the advantages of machine learning in complex compositional reservoir simulations to determine fluid properties such as critical temperature and saturation pressure.
