{"id":"W4243530027","doi":"10.2118/196902-ms","title":"Development and Application of Algorithm for Stress Inversion Based on Image Log Data","year":2019,"lang":"en","type":"article","venue":"SPE Russian Petroleum Technology Conference","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Hydraulic fracturing; Geology; Inversion (geology); Breakout; Stress (linguistics); Offshore geotechnical engineering; Stress field; Petroleum engineering; Geomechanics; Drilling; Poromechanics; Anisotropy; Geotechnical engineering; Tectonics; Engineering; Seismology; Mechanical engineering; Structural 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004195993,0.0008255568,0.0006217003,0.001501914,0.0003776318,0.0007980528,0.001325278,0.0007291148,0.00444137],"category_scores_gemma":[0.001359908,0.0004492782,0.0005084606,0.0009919306,0.0002616619,0.0009457163,0.0008294482,0.00068889,0.002759308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003421005,"about_ca_system_score_gemma":0.001358186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005691927,"about_ca_topic_score_gemma":0.004317981,"domain_scores_codex":[0.9996564,0.00003110993,0.00002458308,0.00009376243,0.0001537116,0.00004041662],"domain_scores_gemma":[0.9996011,0.00007955669,0.00004566853,0.00003513617,0.0002198781,0.00001872039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001621772,0.0001199353,0.002458925,0.0001480756,0.00005262411,0.0001092065,0.00009779476,0.05202601,0.05114454,0.001988904,0.00405554,0.8876362],"study_design_scores_gemma":[0.00001988669,0.00004553632,0.001224159,0.0000116027,0.00001074316,0.00006550991,0.00003899298,0.9811161,0.01396647,0.001180789,0.002304199,0.00001613214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005187484,0.00003644929,0.9914526,0.00003972784,0.00002093714,0.0000786156,0.0001123379,0.002711668,0.0003601762],"genre_scores_gemma":[0.08721278,0.0001248544,0.9095708,0.00005543532,0.00003098326,0.0004161029,0.0007869618,0.0001598153,0.001642277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005691927,"threshold_uncertainty_score":0.01485789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008883550649430541,"score_gpt":0.22414664477824,"score_spread":0.2152630941288094,"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."}}