A ROP Optimization Approach Based on Improved BP Neural Network PSO

Duan, Jinan; Zhao, Jinhai; Li, Xiao; Yang, Chuanshu; Li, Changsheng

doi:10.1007/978-3-319-20469-7_2

Cited by 6 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jacinto (2013) developed a model for prediction of ROP in salty formations of Brazil by a Bayesian clustering network. Duan et al (2015) employed the advantages of an optimization algorithm for designing a neural network model to predict ROP. Studying the previous literatures shows that single independent intelligent models have been widely used for ROP prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of an imperialist competitive algorithm (ICA)-based committee machine to predict bit penetration rate in oil wells of Iran

Garavand¹,

Esmaeilian²

2015

CeN

View full text Add to dashboard Cite

Drilling operation of a well is one the most expensive and time consuming procedures of oil and gas exploitation. Oil companies are always seeking for safe and cost-effective techniques for drilling. The main goal and motivation of drilling optimization is achieving the highest efficiency of work. Optimization and minimization of operational costs is one of the most important prerequisites of any engineering project. Rate of penetration is a crucial factor n drilling controlling cost and time of drilling. In the current research, capabilities of single independent intelligent models are employed for developing a hybrid committee machine that can predict bit penetration bit with high accuracy. To get this goal, three single intelligent models, including neural network, fuzzy logic and neuro-fuzzy, are trained. In the second step, the outputs of these models are integrated by imperialist competitive algorithm (ICA). Finally, a linear equation is achieved which gets outputs of single models as inputs and integrate them somehow the final results is closer to the actual value. The developed ICA-based committee machine is tested by 145 real data points gathered from the drilled wells in an oil field. Correlation of actual and predicted value of ROP obtained from committee machine shows that the model predicts ROP with accuracy of 88 percent. Such model can be used for optimization of drilling parameters in future drilling operations.

show abstract

Section: Literature Reviewmentioning

confidence: 99%