Reservoir simulation
The authors of this paper present a workflow designed to achieve maximum integration between analytical and modeling activities in carbon capture and storage projects.
The authors present an efficient workflow using an embedded discrete fracture model to simulate carbon-dioxide flow by use of conductive faults.
This paper provides guidelines for thermal modeling for carbon capture and storage projects in a depleted gas field.
-
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. -
This paper describes a full-field and near-wellbore poromechanics coupling scheme used to model productivity-index degradation against time.
-
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.
-
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.
-
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.
-
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.
-
The authors of this paper write that computationally coupled models enable swift, accurate, and engineered decision-making for optimal asset development.
-
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.
-
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.
