Formation evaluation
Regional pore-pressure variations in the Leonardian- and Wolfcampian-age producing strata in the Midland and Delaware basins are studied using a variety of subsurface data.
This paper presents a novel workflow for using electromagnetic resistivity-based reservoir mapping logging-while-drilling technologies for successful well placement and multilayer mapping in low-resistivity, low-contrast, thinly laminated clastic reservoirs.
This paper details how the reservoir modeling workflow can be accelerated, and uncertainty reduced, even for challenging greenfield prospects by constructing multiple small fit-for-purpose integrated adaptive models.
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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 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 propose an artificial-intelligence-assisted work flow that uses machine-learning techniques to identify sweet spots in carbonate reservoirs.
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This paper describes an effort to use multiple technologies to better understand an Arkoma Basin reservoir and the interdisciplinary relationship between the reservoir’s subsurface hazards and a stimulation treatment.
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The main objective of this paper is to investigate the relationship between strain change and pressure change under various fractured reservoir conditions to better estimate conductive fractures and pressure profiles.
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The authors of this paper describe an approach in which all available technologies are combined to improve understanding of reservoir depositional environments.
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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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The natural fractures discovered on Mars during rover missions might be so important that they are worth bringing back to Earth.
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The authors of this paper describe a technology built on a causation-based artificial intelligence framework designed to forewarn complex, hard-to-detect state changes in chemical, biological, and geological systems.
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Geochemical parameters such as total organic carbon (TOC) provides valuable information to understand rock organic richness and maturity and, therefore, optimize hydrocarbon exploration. This article presents a novel work flow to predict continuous high-resolution TOC profiles using machine learning.
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