{"id":"W3213568860","doi":"10.15530/ap-urtec-2021-208394","title":"Physics-Constrained Deep Learning for Production Forecast in Tight Reservoirs","year":2021,"lang":"en","type":"article","venue":"Proceedings of the 2021 Asia Pacific Unconventional Resources Technology Conference","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; University of Calgary","funders":"","keywords":"Production (economics); Computer science; Deep learning; Petroleum engineering; Geology; Artificial intelligence; Economics; Microeconomics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003869151,0.0001766315,0.0002715679,0.0002119975,0.0001011771,0.00003938609,0.0003226894,0.0001974186,0.00004661348],"category_scores_gemma":[0.000844182,0.0001636043,0.0001298466,0.0009944654,0.0002138847,0.0001391227,0.00008689979,0.000434304,0.000002478334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006578294,"about_ca_system_score_gemma":0.00003787404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.678694e-7,"about_ca_topic_score_gemma":0.000004683407,"domain_scores_codex":[0.9987977,0.00001478708,0.0003738778,0.0003143624,0.0002248748,0.0002744657],"domain_scores_gemma":[0.9991046,0.00008333447,0.0001135262,0.0001560011,0.000509066,0.00003351873],"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":[0.00009155202,0.0001460298,0.04121198,0.001138977,0.0002578137,0.000004336241,0.001549082,0.6461286,0.1990434,0.08409332,0.0003768383,0.02595801],"study_design_scores_gemma":[0.001339283,0.00008780765,0.003104251,0.000746042,0.0000580345,0.00006078224,0.008186052,0.642507,0.2384669,0.08453751,0.02035796,0.0005483638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782056,0.0003578461,0.01097446,0.001330714,0.0003258497,0.0003376184,0.000005557291,0.0001988648,0.008263488],"genre_scores_gemma":[0.9873668,0.00004458883,0.0107153,0.000001334601,0.00008044039,0.0001034425,0.000009018143,0.00002684577,0.001652259],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03942348,"threshold_uncertainty_score":0.6671589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01624819432235984,"score_gpt":0.2386594155880493,"score_spread":0.2224112212656895,"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."}}