{"id":"W4381489695","doi":"10.1002/cjce.25022","title":"Integration of machine learning and data analysis for the <scp>SAGD</scp> production performance with infill wells","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Energi Simulation","keywords":"Infill; Steam-assisted gravity drainage; Support vector machine; Machine learning; Artificial neural network; Algorithm; Artificial intelligence; Engineering; Computer science; Petroleum engineering; Structural engineering; Asphalt; Oil sands; Materials science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005020555,0.00008074227,0.0001363672,0.0002053799,0.00005824747,0.00002610354,0.0001932741,0.00003206148,0.00000170527],"category_scores_gemma":[0.0003092204,0.00004835906,0.00003149488,0.0005462778,0.0000399315,0.0001445713,0.00001251812,0.0002625769,2.64061e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003518306,"about_ca_system_score_gemma":0.00003093901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001641542,"about_ca_topic_score_gemma":0.0001635939,"domain_scores_codex":[0.9995088,0.000005674129,0.0001833954,0.00007473694,0.00009327377,0.00013415],"domain_scores_gemma":[0.9995185,0.00010745,0.00005988349,0.0001653794,0.00007978459,0.0000690316],"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":[0.000006208706,0.000002631194,0.00269054,0.0001703469,0.0005725237,0.000001605914,0.0009148933,0.9008817,0.07641035,0.00003701573,0.001305448,0.01700675],"study_design_scores_gemma":[0.0001036617,0.00004409392,0.00168174,0.00007976092,0.0002783329,0.00005282143,0.00007545835,0.6335517,0.3568691,0.00002735774,0.007182399,0.00005359256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924219,0.0007467417,0.005947378,0.0005246234,0.0001488195,0.0001074143,0.00001375784,0.00006639907,0.00002295047],"genre_scores_gemma":[0.9985593,0.0001323961,0.001079715,0.000004174141,0.0001410263,0.000003856278,0.00001484318,0.00001546086,0.00004925093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2804587,"threshold_uncertainty_score":0.1972025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01328929169509101,"score_gpt":0.2088614346574085,"score_spread":0.1955721429623175,"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."}}