Reservoir simulation
The main goal of this research work was to determine subseismic faults and fracture corridors and their characteristics, including density and orientation, for a Paleocene fractured carbonate reservoir.
In this paper, an energy-based 3D fracture-reconstruction method is proposed to derive the complex fracture network from microseismic data in a shale gas reservoir.
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.
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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.
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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.
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This work presents an integrated multiphase flow model for downhole pressure predictions that produces relatively more-accurate downhole pressure predictions under wide flowing conditions while maintaining a simple form.
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