{"paper":{"title":"Realization of Precise Perforating Using Dynamic Threshold and Physical Plausibility Algorithm for Self-Locating Perforating in Oil and Gas Wells","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Dynamic threshold and physical plausibility checks enable real-time collar recognition for automated well perforating.","cross_cats":["cs.SY","eess.SP"],"primary_cat":"eess.SY","authors_text":"Guo-Hui Ren, Jun-Jie Wang, Kai Tang, Shuang Liu, Si-Yu Xiao, Tian-Hao Mao, Tu-Pei Chen, Xin-Di Zhao, Yang Liu, Yi-An Liu, Yu-Qiao Chen, Zhi-Jian Yu","submitted_at":"2025-08-30T21:08:20Z","abstract_excerpt":"Accurate depth measurement is critical for targeting designated perforation intervals to maximize hydrocarbon recovery. While next-generation automated wireless perforating techniques reduce reliance on costly surface infrastructure and personnel, they lack the continuous depth correlation provided by conventional wireline cables. Consequently, correlating real-time casing collar locator (CCL) signals with a pre-recorded casing tally is essential for automatic depth determination. However, implementing this measurement remains challenging: downhole instruments must process CCL signals in real-"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Field tests demonstrate a collar recognition F1 score of 98.6% at a throughput of 1000 Sa/s. Notably, the algorithm requires only 1.5 μs per sample, confirming its computational efficiency and suitability for deployment on resource-constrained, high-temperature downhole platforms.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The physical plausibility verification rules remain effective and general across varying well conditions, interference levels, and casing configurations beyond those encountered in the reported field tests.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"The DTPPMP system achieves 98.6% F1 score in collar recognition for self-locating perforating at 1000 Sa/s using dynamic threshold and physical plausibility algorithms that run in 1.5 μs per sample.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Dynamic threshold and physical plausibility checks enable real-time collar recognition for automated well perforating.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"885ac9adea254e40863d9ce91896ecc10651d77367601b0e5956dc71dc39b307"},"source":{"id":"2509.00608","kind":"arxiv","version":3},"verdict":{"id":"62787cdd-e22b-4b0e-b8dc-f7ead9d75dc0","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-18T19:12:29.427139Z","strongest_claim":"Field tests demonstrate a collar recognition F1 score of 98.6% at a throughput of 1000 Sa/s. Notably, the algorithm requires only 1.5 μs per sample, confirming its computational efficiency and suitability for deployment on resource-constrained, high-temperature downhole platforms.","one_line_summary":"The DTPPMP system achieves 98.6% F1 score in collar recognition for self-locating perforating at 1000 Sa/s using dynamic threshold and physical plausibility algorithms that run in 1.5 μs per sample.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The physical plausibility verification rules remain effective and general across varying well conditions, interference levels, and casing configurations beyond those encountered in the reported field tests.","pith_extraction_headline":"Dynamic threshold and physical plausibility checks enable real-time collar recognition for automated well perforating."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.00608/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"ebdc36805467bee1900121be12d4dc3f32144e8e7e565825ab564fb81c0be188"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}