Wolfcamp formation
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A 44-well development tests what ConocoPhillips has learned about maximizing the value of the wells by figuring out how they drain the reservoir.
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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 authors of this paper aim to identify the effect of various geologic controls on hydrocarbon maturation in the Delaware Basin by restoring regional cross sections and performing simulations of organic-matter maturity.
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This paper presents a work flow that has been applied to crossdipole sonic data acquired in a vertical pilot well drilled in the Permian Basin.
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Present industry solutions to the challenge of well spacing involve expensive geomechanical Earth modeling or fracture-geometry monitoring that is time-consuming, data-intensive, and geography-specific.
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The dynamic nature of unconventional-reservoir developments calls for the availability of fast and reliable history-matching methods for simulation models. In this paper, the authors apply an assisted-history-matching approach to a pair of wells in the Wolfcamp formation.
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This paper presents a data set involving the pumping of multiple, unique chemical tracers into a single Wolfcamp B fracture stage.
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This paper introduces a new core-analysis work flow for determining resistivity index (RI), formation factor (FF), and other petrophysical properties directly from an as-received (AR) set of core samples.
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Houston-based Surge Energy drilled the Medusa Unit C 28-09 3AH well in the Midland Basin to a TMD of 24,592 ft, with a total horizontal displacement of 17,935 ft, or 3.4 miles.
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