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A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning
Published 2020-07-01“…The identification of underground formation lithology can serve as a basis for petroleum exploration and development. This study integrates Extreme Gradient Boosting (XGBoost) with Bayesian Optimization (BO) for formation lithology identification and comprehensively evaluated the performance of the proposed classifier based on the metrics of the confusion matrix, precision, recall, F1-score and the area under the receiver operating characteristic curve (AUC). …”
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Dynamic Productivity Prediction Method of Shale Condensate Gas Reservoir Based on Convolution Equation
Published 2023-02-01Get full text
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Recovery of Low Permeability Reservoirs Considering Well Shut-Ins and Surfactant Additivities
Published 2017-08-01Get full text
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Geophysical methods for quantitative resumption of paleo-structures and its application
Published 2021-03-01Get full text
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Method of identifying and characterizing of volcanic traps and its application
Published 2021-05-01Get full text
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Reactive Transport Simulation of Cavern Formation along Fractures in Carbonate Rocks
Published 2020-12-01Get full text
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Fracture system in shale gas reservoir: Prospect of characterization and modeling techniques
Published 2021-06-01Get full text
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Classification evaluation criteria and exploration potential of tight oil resources in key basins of China
Published 2019-12-01Get full text
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