DSDE: In Theory
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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 paper presents the proof of concept of artificial-intelligence-based well-integrity monitoring for gas lift, natural flow, and water-injector wells.
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The authors of this paper present a machine-learning-based solution that predicts pertinent gas-injection studies from known fluid properties such as fluid composition and black-oil properties.
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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 project aimed at automating the task of cuttings descriptions with machine-learning and artificial-intelligence techniques, in terms of both lithology identification and quantitative estimation of lithology abundances.
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This paper presents a novel sustainable cost-effective method developed to scale up synthesis of Janus carbon nanoparticles from waste plastic feedstock by combined pyrolysis, chemical functionalization, and pulverization.
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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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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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In this paper, the authors present data analyses to comprehensively evaluate the performance of a steady-state multiphase-flow point model in predicting high-pressure, near-horizontal data from independent experiments.
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Sustainability data management is a relatively new discipline that requires a unique set of tools, processes, and procedures to meet corporate demands.
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