{"id":"W3206922600","doi":"10.1115/omae2021-63018","title":"Data Driven Prediction of the Minimum Miscibility Pressure (MMP) Between Mixtures of Oil and Gas Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Miscibility; Bubble point; Volume (thermodynamics); Artificial neural network; Petroleum engineering; Range (aeronautics); Nitrogen; Bubble; Materials science; Computer science; Environmental science; Chemistry; Thermodynamics; Machine learning; Polymer; Engineering; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007912661,0.0009626137,0.0006991723,0.001290103,0.0002029252,0.0008440893,0.000726723,0.0009996245,0.0006153153],"category_scores_gemma":[0.002122326,0.0003686303,0.0007349005,0.0006478074,0.0003676423,0.0009098086,0.000488065,0.000899069,0.0002055963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008460895,"about_ca_system_score_gemma":0.0007123656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007294983,"about_ca_topic_score_gemma":0.005265059,"domain_scores_codex":[0.9997736,0.00003754667,0.00001673288,0.00007270489,0.00006328625,0.0000361836],"domain_scores_gemma":[0.9990513,0.0005681396,0.0001284719,0.00003357462,0.000174743,0.00004382566],"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":[0.0002902026,0.0001674203,0.01462433,0.0002336117,0.00008314459,0.0001821026,0.00002592962,0.9307334,0.005858934,0.0004756643,0.001400367,0.04592491],"study_design_scores_gemma":[0.000002028932,0.00001367305,0.0005545635,0.00000395316,0.000003690989,0.000005334351,0.000002998599,0.9977167,0.001464309,0.0001561752,0.00007390101,0.000002674992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8026628,0.003610407,0.1855709,0.0006813666,0.0001112681,0.00006175123,0.002192787,0.001934455,0.003174137],"genre_scores_gemma":[0.9840223,0.000346683,0.01320998,0.00005740642,0.00001959187,0.00003913946,0.001413624,0.00002959985,0.0008616464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007294983,"threshold_uncertainty_score":0.01450503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02529329457403648,"score_gpt":0.2537573109849089,"score_spread":0.2284640164108725,"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."}}