Intelligent optimization of drill bits by combining multi-source data fusion and deep neural networks

ABSTRACT

Rational selection of the drill-bit type can effectively increase the drilling speed and ensure the stability of the wellbore. This paper considers the shortcomings in the existing research on the extraction and fusion of multi-source data related to drill-bit types. In this in-depth analysis, the internal connection is established between the multi-source data and the type of drill bit used. An intelligent optimization method has been developed for drill bits by combining multi-source data fusion and deep neural networks. In this method, data extraction, data mining, and fusion analysis are implemented for multi-source data that characterize the formation characteristics by using principal component analysis (PCA). Based on this method, the deep belief network (DBN) mathematical model for drill-bit optimization is established, and the drill-bit type is optimized for the whole well. The PCA–DBN drill-bit intelligent selection method was applied to five wells in the K structural zone in the T basin. The comparison results prove that the PCA–DBN method is a powerful drill-bit selection tool that is more effective and applicable than conventional methods. The proposed method is 17.31%, 5.77%, 21.15%, and 1.92% more accurate than the rock mechanics parameter method, back propagation (BP) neural network, PCA–BP neural network, and DBN neural network, respectively. The accuracy of the drill-bit selection increased by approximately 40.38% as compared with the single type data because multi-source data were used. This proves that the intelligent optimization method that combines multi-source data fusion and deep neural networks can be effectively used for drill-bit selection, this method promotes cost reduction and enhances the efficiency of drilling engineering.

KEYWORDS:

• Drill-bit selection
• multi-source data fusion
• PCA–DBN neural network
• intelligent decision
• stratum physical properties

Nomenclature

Table

Acknowledgments

This research was supported by the CNPC major science and technology project (2019E-26). We also thank the Young Scientific and Technological Innovation Team of Rock Physics in Unconventional Strata of Southwest Petroleum University (No. 2018CXTD13).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Young Scientific and Technological Innovation Team of Rock Physics in Unconventional Strata of Southwest Petroleum University [2018CXTD13]; CNPC major science and technology project [2019E-26].

Notes on contributors

Youwei Wan

Youwei Wan, born in 1996, male, student pursuing a PhD degree, engaged in petroleum engineering logging and intelligent decision-making research.

Xiangjun Liu

Xiangjun Liu, born in 1969, female, Ph.D., professor, engaged in wellbore stability, rock physics and rock mechanics, and petroleum engineering logging research.

Jian Xiong

Jian Xiong, born in 1986, male, Ph.D., associate professor, engaged in rock physics and geomechanics research.

Lixi Liang

Lixi Liang, born in 1976, male, Ph.D., associate professor, engaged in wellbore stability, rock physics and rock mechanics, and petroleum engineering logging research.