{"id":"W4413903637","doi":"10.1016/j.ptlrs.2025.08.007","title":"Estimation of fluid saturation and pressure distribution throughout a reservoir using machine learning techniques","year":2025,"lang":"en","type":"article","venue":"Petroleum Research","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University; Petroleum Technology Research Centre; University of Regina","funders":"Mitacs; Petroleum Technology Research Centre","keywords":"Saturation (graph theory); Petroleum engineering; Geology; Fluid pressure; Mechanics; Mathematics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000936788,0.0001069865,0.0001961011,0.0002983335,0.0002163451,0.00006896906,0.0001301762,0.0001220055,0.000008637055],"category_scores_gemma":[0.0004229792,0.00009673883,0.00004666013,0.0005264343,0.00009191314,0.0001770771,0.00009569618,0.0005180879,0.000001533022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009533405,"about_ca_system_score_gemma":0.00003299736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006389954,"about_ca_topic_score_gemma":0.0000506594,"domain_scores_codex":[0.9987689,0.000183538,0.0002385798,0.0001823904,0.0003708455,0.0002557643],"domain_scores_gemma":[0.9994032,0.0001771146,0.00002842615,0.0002159935,0.0001325211,0.00004271005],"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":[0.00003879335,0.00002341833,0.00195426,0.0005895098,0.0001249636,0.000002024532,0.0001490536,0.9414629,0.04574867,0.000141766,0.0002111109,0.009553552],"study_design_scores_gemma":[0.0001126175,0.00002964317,0.00102594,0.0001245957,0.00003318741,0.000001568085,0.00004708484,0.8879463,0.1051818,0.0001956979,0.005232184,0.00006939992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.840755,0.005574561,0.1500642,0.0004441535,0.0000589871,0.0002231406,0.00003208699,0.0003198868,0.00252796],"genre_scores_gemma":[0.9975731,0.0005937224,0.001155972,0.000001942026,0.00003406571,0.00001796508,0.0001033923,0.0000139575,0.0005059022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1568181,"threshold_uncertainty_score":0.3944895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01996950418756979,"score_gpt":0.3308388579636,"score_spread":0.3108693537760303,"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."}}