{"id":"W2289167581","doi":"10.1016/j.jngse.2016.02.043","title":"History matching and production optimization of water flooding based on a data-driven interwell numerical simulation model","year":2016,"lang":"en","type":"article","venue":"Journal of Natural Gas Science and Engineering","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Aquifer; Reservoir simulation; Petroleum engineering; Production (economics); Mathematical optimization; Matching (statistics); Flooding (psychology); Reservoir engineering; Computer science; Engineering; Groundwater; Geology; Mathematics; Geotechnical engineering; Petroleum","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":[0.0005787779,0.0004070943,0.0009347502,0.0004995951,0.0004294815,0.0008060958,0.001004834,0.00126101,0.001375973],"category_scores_gemma":[0.00192552,0.0006437589,0.0004770372,0.0005242227,0.0006579819,0.0009067109,0.0008124266,0.000610186,0.00007948997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042386,"about_ca_system_score_gemma":0.001171254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01891167,"about_ca_topic_score_gemma":0.01151169,"domain_scores_codex":[0.9998494,0.0000492378,0.000009619725,0.00003736366,0.00002448134,0.00002984052],"domain_scores_gemma":[0.9993466,0.0003574189,0.0000946588,0.00004163457,0.00009271133,0.00006695538],"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":[0.00001358983,0.00001108247,0.00022674,0.000003738171,0.000003986969,0.00001115572,0.000003759489,0.9982495,0.0002023274,0.0004850124,0.00003067249,0.000758337],"study_design_scores_gemma":[0.000001563141,0.00000203156,0.00003244129,1.952863e-7,6.244873e-7,3.758905e-7,6.326082e-7,0.9998457,0.00003179611,0.00007463137,0.000009173326,7.308394e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6350343,0.0002806325,0.3559071,0.0006528453,0.00008696276,0.00009788999,0.0004061773,0.00034569,0.007188397],"genre_scores_gemma":[0.9922609,0.00003828681,0.006734724,0.00001571491,0.000007772845,0.00003063216,0.00007649819,0.00002098857,0.000814536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01891167,"threshold_uncertainty_score":0.03760314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02641178475623578,"score_gpt":0.2600590332794175,"score_spread":0.2336472485231817,"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."}}