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

• Autor(a) principal: Marana, Aparecido Nilceu [UNESP]
• Data de Publicação: 2010
• Outros Autores: Guilherme, Ivan Rizzo [UNESP], Papa, João Paulo [UNESP], Ferreira, Marystela [UNESP], Miura, K., Torres, F. A C
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.2118/128916-MS; http://hdl.handle.net/11449/71779
• topic: Artificial intelligence techniques; Artificial Neural Network; Bayesian classifier; Borehole wall; Data analysis; Data analysis system; Downholes; Drilled cuttings; Drilling problems; Drilling process; Field test; Geological analysis; High definition; Material balance; Multi-layer perceptrons; Non-intrusive; Offshore floating; Shale shakers; Surface systems; Data reduction; Intelligent systems; Mud logging; Neural networks; Offshore oil wells; Oil wells; Pattern recognition systems; Petroleum industry; Sailing vessels; Shale; Support vector machines; Well drilling

Cuttings 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.