An intelligent system to detect drilling problems through drilled cuttings return analysis

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorMarana, Aparecido Nilceu
dc.creatorGuilherme, Ivan Rizzo
dc.creatorPapa, João Paulo
dc.creatorFerreira, Marystela
dc.creatorMiura, K.
dc.creatorTorres, F. A C
dc.date2014-05-27T11:24:44Z
dc.date2016-10-25T18:28:49Z
dc.date2014-05-27T11:24:44Z
dc.date2016-10-25T18:28:49Z
dc.date2010-07-07
dc.date.accessioned2017-04-06T01:42:00Z
dc.date.available2017-04-06T01:42:00Z
dc.identifierSPE/IADC Drilling Conference, Proceedings, v. 2, p. 1123-1130.
dc.identifierhttp://hdl.handle.net/11449/71779
dc.identifierhttp://acervodigital.unesp.br/handle/11449/71779
dc.identifier10.2118/128916-MS
dc.identifier2-s2.0-77954186253
dc.identifierhttp://dx.doi.org/10.2118/128916-MS
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/892712
dc.descriptionCuttings return analysis is an important tool to detect and prevent problems during the petroleum well drilling process. Several measurements and tools have been developed for drilling problems detection, including mud logging, PWD and downhole torque information. Cuttings flow meters were developed in the past to provide information regarding cuttings return at the shale shakers. Their use, however, significantly impact the operation including rig space issues, interferences in geological analysis besides, additional personel required. This article proposes a non intrusive system to analyze the cuttings concentration at the shale shakers, which can indicate problems during drilling process, such as landslide, the collapse of the well borehole walls. Cuttings images are acquired by a high definition camera installed above the shakers and sent to a computer coupled with a data analysis system which aims the quantification and closure of a cuttings material balance in the well surface system domain. No additional people at the rigsite are required to operate the system. Modern Artificial intelligence techniques are used for pattern recognition and data analysis. Techniques include the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC). Field test results conducted on offshore floating vessels are presented. Results show the robustness of the proposed system, which can be also integrated with other data to improve the efficiency of drilling problems detection. Copyright 2010, IADC/SPE Drilling Conference and Exhibition.
dc.languageeng
dc.relationSPE/IADC Drilling Conference, Proceedings
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial intelligence techniques
dc.subjectArtificial Neural Network
dc.subjectBayesian classifier
dc.subjectBorehole wall
dc.subjectData analysis
dc.subjectData analysis system
dc.subjectDownholes
dc.subjectDrilled cuttings
dc.subjectDrilling problems
dc.subjectDrilling process
dc.subjectField test
dc.subjectGeological analysis
dc.subjectHigh definition
dc.subjectMaterial balance
dc.subjectMulti-layer perceptrons
dc.subjectNon-intrusive
dc.subjectOffshore floating
dc.subjectShale shakers
dc.subjectSurface systems
dc.subjectData reduction
dc.subjectIntelligent systems
dc.subjectMud logging
dc.subjectNeural networks
dc.subjectOffshore oil wells
dc.subjectOil wells
dc.subjectPattern recognition systems
dc.subjectPetroleum industry
dc.subjectSailing vessels
dc.subjectShale
dc.subjectSupport vector machines
dc.subjectWell drilling
dc.titleAn intelligent system to detect drilling problems through drilled cuttings return analysis
dc.typeOtro


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