Document Type
Article
Publication Date
2019
Department/Program/Center
Petroleum and Natural Gas Engineering
Abstract
Abstract Drilling performance monitoring and optimization are crucial in increasing the overall NPV of an oil and gas project. Even after rigorous planning, drilling phase of any project can be hindered by unanticipated problems, such as bit balling. The objective of this paper is to implement artifcial intelligence technique to develop a smart model for more accurate and robust real-time drilling performance monitoring and optimization. For this purpose, the back propagation, feed forward neural network model was developed to predict rate of penetration (ROP) using diferent input parameters such as weight on bit, rotations per minute, mud fow (GPM) and diferential pressures. The heavy hitter features identifcation and dimensionality reduction are performed to understand the impacts of each of the drilling parameters on ROP. This will be used to optimize the input parameters for model development and validation and performing the operation optimization when bit is underperforming. The model is frst developed based on the drilling experiments performed in the laboratory and then extended to feld applications. From both laboratory and feld test data provided, we have proved that the data-driven model built using multilayer perceptron technique can be successfully used for drilling performance monitoring and optimization, especially identifying the bit malfunction or failure, i.e., bit balling. We have shown that the ROP has complex relationship with other drilling variables which cannot be captured using conventional statistical approaches or from diferent empirical models. The data-driven approach combined with statistical regression analysis provides better understanding of relationship between variables and prediction of ROP.
Digital Commons Citation
Lashari, Shan e Zehra; Takbiri-Borujeni, Ali; Fathi, Ebrahim; Sun, Ting; Rahmani, Reza; and Khazaeli, Mehdi, "Drilling Performance Monitoring and Optimization: A Data-driven Approach" (2019). Faculty & Staff Scholarship. 1388.
https://researchrepository.wvu.edu/faculty_publications/1388
Source Citation
Lashari, S. e Z., Takbiri-Borujeni, A., Fathi, E., Sun, T., Rahmani, R., & Khazaeli, M. (2019). Drilling performance monitoring and optimization: a data-driven approach. Journal of Petroleum Exploration and Production Technology, 9(4), 2747–2756. https://doi.org/10.1007/s13202-019-0657-2
Comments
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativeco mmons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.