{"id":"W4411989414","doi":"10.2139/ssrn.5338481","title":"Physics-Informed Temporal Attention Dynamic Convolution Network for Oil and Gas Well Production Forecasting","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Convolution (computer science); Production (economics); Physics; Environmental science; Statistical physics; Computer science; Economics; Artificial intelligence; Microeconomics; Artificial neural network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001610733,0.0003123436,0.0003460211,0.0001448194,0.000214582,0.0001062959,0.0001521249,0.0002581347,0.000001591876],"category_scores_gemma":[0.0001232418,0.0003336899,0.0001849194,0.000163701,0.00002224731,0.0001853892,0.00006197544,0.002135815,0.000001124126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001399232,"about_ca_system_score_gemma":0.0006692865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001310277,"about_ca_topic_score_gemma":0.0001353111,"domain_scores_codex":[0.9976567,0.00005625214,0.0004793533,0.000273851,0.0001758454,0.001358012],"domain_scores_gemma":[0.9993088,0.00009542392,0.0001830511,0.0002017101,0.0001480935,0.00006293869],"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":[0.0000315745,0.000006217411,0.0002368254,0.0005745501,0.0001994776,2.146385e-7,0.00003994179,0.9156115,0.00004807196,0.0009158948,0.0001198726,0.08221585],"study_design_scores_gemma":[0.0005226832,0.00004826406,0.00008944957,0.0004134803,0.00009798941,0.00004383094,0.00007632676,0.9125893,0.00002330657,0.08471586,0.001092761,0.0002867098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1712645,0.004230451,0.8207334,0.0001832595,0.002705899,0.0002417415,0.00001097241,0.0002617197,0.0003679812],"genre_scores_gemma":[0.9181523,0.02171234,0.04783431,0.00001030519,0.003343983,0.0001496937,0.0004076994,0.0001330133,0.008256335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7728992,"threshold_uncertainty_score":0.9999115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01572688779298779,"score_gpt":0.2681092247904789,"score_spread":0.2523823369974911,"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."}}