{"id":"W3109791975","doi":"10.2118/1020-0065-jpt","title":"Machine-Learning Approach Determines Spatial Variation in Shale Decline Curves","year":2020,"lang":"en","type":"article","venue":"Journal of Petroleum Technology","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Production (economics); Oil shale; Learning curve; Cluster analysis; Artificial neural network; Computer science; Artificial intelligence; Completion (oil and gas wells); Structural basin; Machine learning; Operations research; Quality (philosophy); Geology; Industrial engineering; Econometrics; Petroleum engineering; Engineering; Mathematics; Economics; Paleontology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003589727,0.0001200119,0.0003239115,0.0004144325,0.00001703691,0.0000131718,0.0002404784,0.0001469486,0.0000182311],"category_scores_gemma":[0.0005321149,0.0001129637,0.00006364377,0.0003851867,0.00001561931,0.0001135477,0.00004125392,0.000655473,0.000002734822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003870509,"about_ca_system_score_gemma":0.00001419585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005236817,"about_ca_topic_score_gemma":0.000002838045,"domain_scores_codex":[0.9990446,0.00004843218,0.0004918331,0.00009341823,0.000159634,0.0001620808],"domain_scores_gemma":[0.9996135,0.00006176742,0.000122676,0.00008925453,0.00005408028,0.00005874441],"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":[0.00001543154,0.0000141993,0.01146008,0.0001327868,0.00002071335,0.00002237849,0.0000633586,0.9788239,0.006244049,0.00004543243,0.00003124245,0.003126456],"study_design_scores_gemma":[0.0007015497,0.000126579,0.003555652,0.0000550254,0.00001293302,0.00005817644,0.0000198224,0.9925582,0.0006726256,0.0001425857,0.001992553,0.0001042997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4227792,0.002359519,0.5730392,0.001145518,0.0001572343,0.00003943838,0.000001444705,0.0001940917,0.0002843423],"genre_scores_gemma":[0.9599065,0.0004452072,0.0394085,0.00004118988,0.0001571314,0.000002933998,0.000002159398,0.00002423887,0.00001219177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5371273,"threshold_uncertainty_score":0.4606525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01462363890532113,"score_gpt":0.2453132205496528,"score_spread":0.2306895816443316,"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."}}