A Study on Drilling Risk Real Time Recognition Technology Based on Fuzzy Reasoning

A Study on Drilling Risk Real Time Recognition Technology Based on Fuzzy Reasoning Zhilong Lian; Zhilong Lian Drilling Technology Research Department, Drilling Research Institute, CNPC Search for other works by this author on: This Site Google Scholar Yingcao Zhou; Yingcao Zhou Drilling Technology Research Department, Drilling Research Institute, CNPC Search for other works by this author on: This Site Google Scholar Qing Zhao; Qing Zhao Drilling Technology Research Department, Drilling Research Institute, CNPC Search for other works by this author on: This Site Google Scholar Zongqiang Huo Zongqiang Huo Drilling Technology Research Department, Drilling Research Institute, CNPC Search for other works by this author on: This Site Google Scholar Paper presented at the International Oil and Gas Conference and Exhibition in China, Beijing, China, June 2010. Paper Number: SPE-131886-MS https://doi.org/10.2118/131886-MS Published: June 08 2010 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Lian, Zhilong , Zhou, Yingcao , Zhao, Qing , and Zongqiang Huo. "A Study on Drilling Risk Real Time Recognition Technology Based on Fuzzy Reasoning." Paper presented at the International Oil and Gas Conference and Exhibition in China, Beijing, China, June 2010. doi: https://doi.org/10.2118/131886-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE International Oil and Gas Conference and Exhibition in China Search Advanced Search Abstract Drilling risk real time recognition technology is vital important for drilling risk early detection, evaluation and control. Drilling risk symptom automatic extraction technology was studied. The maximum value, minimum value, average value, pick value, skew value and kurtosis value were chose as drilling risk symptom indicators, and time serial of real time data was set up. Signal changing tendency rate automatic extraction technology was realized. Drilling risk database was created, and inference engine based on fuzzy production rule was also realized. The application results of some cases indicated that the drilling risk real time recognition technology based on fuzzy reasoning is accurate and reliable, the drilling risk can be found in early time, and it can be used to monitor borehole complex situations in real time. Average value, peak value, maximum value, minimum value, skew value and kurtosis value can be used as drilling risk indicators Keywords: automatic extraction technology, kurtosis, artificial intelligence, minimum value, fuzzy logic, inference engine, expert system, drilling risk symptom indicator, drilling risk, average value Subjects: Information Management and Systems, Artificial intelligence Copyright 2010, Society of Petroleum Engineers You can access this article if you purchase or spend a download.