{"id":"W4391023709","doi":"10.2118/219448-pa","title":"A One-Dimensional Convolutional Neural Network for Fast Predictions of the Oil-CO2 Minimum Miscibility Pressure in Unconventional Reservoirs","year":2024,"lang":"en","type":"article","venue":"SPE Journal","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Overfitting; Convolutional neural network; Computer science; Artificial neural network; Principal component analysis; Reservoir simulation; Mixing (physics); Data set; Approximation error; Set (abstract data type); Enhanced oil recovery; Algorithm; Artificial intelligence; Petroleum engineering; Engineering","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":[0.0005995,0.0001270899,0.0001755842,0.0000987389,0.0001060678,0.00004399227,0.0002150822,0.0001082106,0.0001688241],"category_scores_gemma":[0.00008982733,0.0001067883,0.0002414159,0.0002804272,0.00009454918,0.0002160872,0.00005090749,0.0005744229,0.000002541139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001514491,"about_ca_system_score_gemma":0.0001564203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007875745,"about_ca_topic_score_gemma":0.0001254979,"domain_scores_codex":[0.9986696,0.00006360318,0.0004621016,0.0001699905,0.0003578001,0.0002769496],"domain_scores_gemma":[0.9993591,0.0002274326,0.00006005012,0.0001718398,0.0001172762,0.00006427449],"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":[0.00007536379,0.00006452919,0.002764614,0.0002760605,0.0001803278,0.000003699107,0.00007443556,0.9527649,0.004088274,0.001370893,0.03683906,0.001497834],"study_design_scores_gemma":[0.0006295654,0.0001180375,0.03687559,0.001188188,0.0001007952,0.000139397,0.00002872121,0.9066809,0.00232533,0.03649246,0.01517772,0.0002432378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9676731,0.007167565,0.01221071,0.001977382,0.006667865,0.0004732908,0.0006917882,0.0003603207,0.002778005],"genre_scores_gemma":[0.9908764,0.00008085141,0.006868564,0.00002699383,0.000837573,0.00004467456,0.00001687498,0.00003399447,0.001214114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04608397,"threshold_uncertainty_score":0.4354699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01534116136393123,"score_gpt":0.2505822865574762,"score_spread":0.2352411251935449,"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."}}