Reservoir simulation
This paper presents a high-fidelity, reservoir-centric stimulation-to-production modeling workflow to prevent fracture-driven interactions.
This work elaborates on recommended practices for calibrating a numerical model to a set of available infill-well field observations.
Known as Arena, the collaboration aims to improve decision-making across increasingly complex reservoirs and growing volumes of subsurface data.
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The index integrates three independent components extracted from static and dynamic parameters: reservoir permeability thickness, movable gas, and reservoir pressure from a historically matched dynamic model. -
The authors of this paper propose a hybrid approach that combines physics with data-driven approaches for efficient and accurate forecasting of the performance of unconventional wells under codevelopment.
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This paper describes a full-field and near-wellbore poromechanics coupling scheme used to model productivity-index degradation against time.
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This paper develops a deep-learning work flow that can predict the changes in carbon dioxide mineralization over time and space in saline aquifers, offering a more-efficient approach compared with traditional physics-based simulations.
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The authors of this paper present an advanced dual-porosity, dual-permeability (A-DPDK) work flow that leverages benefits of discrete fracture and DPDK modeling approaches.
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This study presents a novel approach to screen thermally stable surfactants at high pressures and high temperatures for the explicit purpose of wettability alteration in the operator’s Eagle Ford acreage.
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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.
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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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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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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.
