Abstract
Effective optimization of ROP (Rate of Penetration) is a crucial part of successful well drilling process. Due to the penetration complexities and the formation heterogeneity, traditional way such as ROP equations and regression analysis are confined by their limitations in the drilling prediction. Intelligent methods like ANN and PSO become powerful tools to obtain the optimized parameters with the accumulation of the geology data and drilling logs. This paper presents a ROP optimization approach based on improved BP neural network and PSO algorithm. The main idea is, first, to build prediction model of the target well from well logs using BP neural network, and then obtain the optimized well operating parameters by applying PSO algorithm. During the modelling process, the traditional BP training algorithm is improved by introducing momentum factor. Penalty function is also introduced for the constraints fulfillment. We collect and analyze the well log of the No.104 well in Yuanba, China. The experiment results show that the proposed approach is able to effectively utilize the engineering data to provide effective ROP prediction and optimize well drilling parameters.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Rhodes, C.: Intelligent Planning Reduces Non-productive Drilling Times. Oilfeld Review 13(2), 1–4 (2001)
Bourgoyne, A.T, Young, F.S. A Multiple Regression Approach to Optimal Drilling and Abnormal Pressure Detection. SPEJ, Trans. AIME, 257, 371 (2000)
Wang, K.: Application of Well Log Information in ROP Prediction. 16(3), 889-900 (2007)
Lin, Y., Zong, Y., Zheng, L., Shi, T., Li, R.: The Developments of ROP Prediction for Oil Drilling. Petroleum Drilling Techniques 32(1), 10–13 (2004)
Bahari, A., Seyed, B.: Trust-region approach to find constants of bourgoyne and young penetration rate model in khangiran iranian gas field. In: SPE Latin American and Caribean Petroleum Engineering Conference, SPE 107520, Buenos Aires, vol. 26, pp. 20-22 (2007)
Irawan, S., Rahman, A.M.A., Tunio, S.Q.: Optimization of Weight on Bit During Drilling Operation Based on Rate of Penetration Model. Research Journal of Applied Sciences, Engineering and Technology 4(12), 1690–1695 (2012)
Marana, A.N., Guilherme, I.R., Papa, J.P., Ferreira, M., Miura, K., Torres, F.A.C.: An intelligent system to detect drilling problems through drilled cuttings return analysis. In: IADC/SPE Drilling Conference and Exhibition, IADC/SPE 128916, New Orleans, pp. 103-110 (2010)
Fruhwirth, R.K., Thonhauser, G., Mathis, W.: Hybrid simulation using neural networks to predict drilling hydraulics in real time. In: SPE Annual Technical Conference and Exhibition, San Antonio, pp. 24-27 (2006)
Dashevskly, D.: Application of neural networks for predictive control in drilling dynamics. In: SPE Annual Technical Conference and Exhibition, pp. 248-251 (1999)
Karri, V.: RBF neural network for thrust and torque predictions in drilling operations. In: Third International Conference Computational Intelligence and Multimedia Applications(ICCIMA 1999), pp. 55-59 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Duan, J., Zhao, J., Xiao, L., Yang, C., Li, C. (2015). A ROP Optimization Approach Based on Improved BP Neural Network PSO. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9142. Springer, Cham. https://doi.org/10.1007/978-3-319-20469-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-20469-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20468-0
Online ISBN: 978-3-319-20469-7
eBook Packages: Computer ScienceComputer Science (R0)