{"id":"W2586963217","doi":"10.2118/185082-ms","title":"Bayesian History-Matching and Probabilistic Forecasting for Tight and Shale Wells","year":2017,"lang":"en","type":"article","venue":"SPE Unconventional Resources Conference","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Mitacs","keywords":"Markov chain Monte Carlo; Frequentist inference; Computer science; Monte Carlo method; Bayesian probability; Posterior probability; Bayesian inference; Algorithm; Geology; Mathematical optimization; Mathematics; Artificial intelligence; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001868001,0.0003847595,0.0006519476,0.001209736,0.0003764557,0.0007066315,0.00110349,0.0008990141,0.00129873],"category_scores_gemma":[0.005792338,0.0003318315,0.0004229149,0.00123189,0.0006308742,0.0009971296,0.0005550775,0.0005802847,0.000108009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002185022,"about_ca_system_score_gemma":0.001138759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1004596,"about_ca_topic_score_gemma":0.0768423,"domain_scores_codex":[0.9996566,0.0000846143,0.00001996645,0.0001018395,0.00007080842,0.0000661349],"domain_scores_gemma":[0.9983235,0.001045854,0.0002892535,0.00007656396,0.0001791925,0.0000855499],"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":[0.00003007028,0.00001011141,0.00573184,0.000009111724,0.000008830417,0.00005693475,0.000026683,0.9837652,0.0002876136,0.003402681,0.0001194361,0.006551376],"study_design_scores_gemma":[0.000001015123,0.000002520764,0.001066516,0.000001334657,0.000001322879,0.000002921559,0.000006239433,0.9975673,0.00007351377,0.001230413,0.00004314716,0.000003820434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7729787,0.0002182168,0.2233794,0.0003252085,0.00001489594,0.00005480543,0.0008346456,0.0002213836,0.001972688],"genre_scores_gemma":[0.9922068,0.00004943956,0.006869181,0.000007687829,0.000005357136,0.00001380269,0.0003120481,0.000008221725,0.0005275515],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1004596,"threshold_uncertainty_score":0.1997498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05726527128001663,"score_gpt":0.2656705532503695,"score_spread":0.2084052819703529,"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."}}