| Citation: |
SUN Ting, ZHAO Ying, YANG Jin, YIN Qishuai, WANG Wenxing, CHEN Yuan. Real-Time Intelligent Identification Method under Drilling Conditions Based on Support Vector Machine[J]. Petroleum Drilling Techniques, 2019, 47(5): 28-33. DOI: 10.11911/syztjs.2019033
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At present, the analysis of drilling time-efficiency usually relies on manual post-analysis, which is subjective and arbitrary, and not able to reflect the real field situation in time, with a lot of deviation. In order to identify drilling conditions automatically and accurately in real time and improve drilling efficiency, a data-driven real-time identification method of drilling conditions based on support vector machine (SVM) has been put forward, and established several intelligent identification models. By analyzing and comparing the kernel functions in the models, the optimal model parameters were obtained. Logging data from four wells were used to verify the correctness of the model, and the recognition results were basically consistent with the actual working conditions, and the recognition accuracy under six working conditions was higher than 95%. The analysis of drilling time-efficiency showed that the application of working condition identification result in drilling process shortened the invisible non-production time. Support vector machine has realized real-time intelligent identification of drilling conditions, and improved drilling time-efficiency, which could meet the requirements of digital and intelligent development of oilfields.
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Supervisor：SINOPEC GROUP
Sponsor：Sinopec Research Institute of Petroleum Engineering
Serial Number：CN 11-1763/TE, ISSN 1001-0890
Chief Editor： WANG Minsheng
Supported by: Beijing Renhe Information Technology Co., Ltd. 
Copyright © 2009《Petroleum Drilling Techniques 》 All Rights Reserved.
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