{"id":"W4391329375","doi":"10.2118/217809-ms","title":"Application of Machine Learning to Create a Discrete Fracture Network Model for Utah FORGE Fracture Injections","year":2024,"lang":"en","type":"article","venue":"SPE Hydraulic Fracturing Technology Conference and Exhibition","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Forge; Fracture (geology); Computer science; Engineering; Materials science; Mechanical engineering; Composite material; Forging","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":[0.0001961591,0.000228289,0.0002753134,0.000375764,0.0001278421,0.00005986002,0.0001209077,0.0003607179,0.00001288155],"category_scores_gemma":[0.0000699351,0.0002193781,0.00007071529,0.0003897483,0.0000414077,0.0001845109,0.00003916225,0.0005294105,0.000005284573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004428719,"about_ca_system_score_gemma":0.0000182518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002579515,"about_ca_topic_score_gemma":0.00001853037,"domain_scores_codex":[0.9989785,0.00001452984,0.0002878713,0.0003215808,0.0001033396,0.0002941807],"domain_scores_gemma":[0.9994724,0.0001086994,0.00004285128,0.0002402623,0.00006389938,0.00007187683],"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":[0.00001473896,0.000005032952,0.0001309688,0.000193957,0.00003827275,9.495852e-7,0.0002987754,0.9847138,0.002328956,0.001154336,0.0002191602,0.01090109],"study_design_scores_gemma":[0.000175813,0.00006386135,0.0001294247,0.0001715202,0.00003579382,0.000007619743,0.00003643976,0.9524502,0.006802886,0.02056667,0.01934893,0.0002108753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05961982,0.0008894952,0.9361184,0.001588698,0.00009923007,0.0003983442,0.00001972648,0.001069264,0.0001970511],"genre_scores_gemma":[0.9651722,0.0002134813,0.03401288,0.00007438674,0.0001224598,0.0001641005,0.00009239786,0.00004958747,0.00009852575],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9055524,"threshold_uncertainty_score":0.894598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01170179172164331,"score_gpt":0.263410543092032,"score_spread":0.2517087513703887,"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."}}