DSDE: In Practice
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This paper highlights a thermodynamic modeling approach used to determine the optimal depth for installation of a wax-inhibition tool.
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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 an approach to optimize the location of wellhead towers using an algorithm based on multiple parameters related to well cost.
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The authors of this paper present an artificial-lift timing and selection work flow using a hybrid data-driven and physics-based approach that incorporates routinely available pressure/volume/temperature, rate, and pressure information.
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Large methane emissions occur from a wide variety of oil and gas industry sites with no discernable patterns, thus requiring methods to monitor for these releases throughout the production chain. This paper describes a continuous monitoring system based on the Internet of Things using methane concentration sensors permanently deployed at facilities and connected to a…
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This paper presents a comprehensive technical review of applications of distributed acoustic sensing.
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The authors of this paper analyze a robust, well-distributed parent/child well data set using a combination of available empirical data and numerical simulation outputs to develop a predictive machine-learning model.
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In this paper, example machine-learning models were trained using geologic, completion, and spacing parameters to predict production across the primary developed formations within the Midland Basin.
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The site survey and inspection of an offshore gas platform in UAE waters was executed entirely from an onshore remote operations center without sending personnel offshore.
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The authors of this paper describe a solution using machine-learning techniques to predict sandstone distribution and, to some extent, automate the process of optimizing well placement.
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