{"id":"W4414565025","doi":"10.1021/acsomega.5c06404","title":"Production-Increase Potential Evaluations after Refracturing Low-Shale-Oil-Producing Wells via Machine-Learning-Driven Multisource Data Mining","year":2025,"lang":"en","type":"article","venue":"ACS Omega","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"SINOPEC Petroleum Exploration and Production Research Institute; State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation; Science Foundation of China University of Petroleum, Beijing; Southwest Petroleum University; China University of Petroleum, Beijing","keywords":"Ranking (information retrieval); Oil shale; Variance (accounting); Well control; Current (fluid); Completion (oil and gas wells); Hydrogeology; Feature (linguistics); Data set","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006117983,0.0003381998,0.0003680005,0.0004470551,0.0003752137,0.0001508233,0.0006572709,0.0001542807,0.00007339986],"category_scores_gemma":[0.0006547302,0.0003327214,0.0001178946,0.0006221223,0.00005563187,0.0004786909,0.0003895538,0.0006381008,0.0001290124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001315479,"about_ca_system_score_gemma":0.00004981196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000481934,"about_ca_topic_score_gemma":0.0001246962,"domain_scores_codex":[0.9976761,0.0001238516,0.0004873653,0.0008087328,0.000408162,0.0004957319],"domain_scores_gemma":[0.9980096,0.0001120317,0.00008803843,0.001544288,0.0001057055,0.0001403696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002861026,0.00006162203,0.003295458,0.0002822676,0.0004342271,0.00004148524,0.0006764574,0.9704345,0.007816944,2.353258e-7,0.00194561,0.01498259],"study_design_scores_gemma":[0.0006896471,0.00001838992,0.01042705,0.0004907251,0.0006042796,0.00003192603,0.0002037021,0.9527835,0.0140309,0.00001437729,0.02003141,0.0006740715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850907,0.001587324,0.00956448,0.000827225,0.0008423683,0.0001722501,0.00003529236,0.000613681,0.001266619],"genre_scores_gemma":[0.990313,0.0001981317,0.001986291,0.00008502362,0.0005680078,0.00005236836,0.0003317958,0.00007040269,0.00639498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0180858,"threshold_uncertainty_score":0.9999125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009164497553386827,"score_gpt":0.2495973204603137,"score_spread":0.2404328229069269,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}