{"id":"W4409196264","doi":"10.1016/j.jgsce.2025.205617","title":"Integrating geological model via A multimodal machine learning approach in shale gas production forecast","year":2025,"lang":"en","type":"article","venue":"Gas Science and Engineering","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Shale gas; Oil shale; Production (economics); Petroleum engineering; Artificial intelligence; Computer science; Geology; Engineering; Economics; Waste management; Microeconomics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000449698,0.0006039888,0.0004008328,0.0007423604,0.0001638228,0.0005685448,0.000456924,0.0005923299,0.0008334582],"category_scores_gemma":[0.0009750875,0.0002896178,0.0005300516,0.0004365208,0.0002209366,0.00101928,0.0005738963,0.0004781942,0.000212589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006072844,"about_ca_system_score_gemma":0.0004920897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01038176,"about_ca_topic_score_gemma":0.01058937,"domain_scores_codex":[0.999849,0.0000365829,0.0000080316,0.00004838947,0.00002916251,0.00002884181],"domain_scores_gemma":[0.9997751,0.00008968661,0.00003284969,0.00001923088,0.00006371936,0.00001936833],"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":[0.00004447696,0.00003576313,0.002776444,0.00001220225,0.00002626462,0.00004787316,0.00002371299,0.9507343,0.002350291,0.0004452057,0.0001989667,0.04330445],"study_design_scores_gemma":[5.752033e-7,0.000004507473,0.0002573724,7.711295e-7,0.000002129037,0.000002401206,0.000003093456,0.9992808,0.0002211494,0.0001858033,0.00003951897,0.000001938008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3282988,0.0004360831,0.6658688,0.000435298,0.00005415137,0.00004220938,0.0003088347,0.00135355,0.003202233],"genre_scores_gemma":[0.9774325,0.00007931054,0.02158436,0.00004825507,0.00001666745,0.00002342795,0.0001364272,0.00001709779,0.000661974],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01038176,"threshold_uncertainty_score":0.02064264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712668511069419,"score_gpt":0.2493469997187496,"score_spread":0.2322203146080554,"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."}}