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

Authors

  • Ayub Abbasi Garavand DQ-CCNE/UFSM
  • Gholamreza Esmaeilian

DOI:

https://doi.org/10.5902/2179460X20844

Abstract

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.

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Published

2015-12-21

How to Cite

Garavand, A. A., & Esmaeilian, G. (2015). Development of an imperialist competitive algorithm (ICA)-based committee machine to predict bit penetration rate in oil wells of Iran. Ciência E Natura, 37, 173–182. https://doi.org/10.5902/2179460X20844

Issue

Vol. 37 (2015): SPECIAL EDITION - PART 1

Section

Special Edition

License

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