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An optimum drill bit selection technique using artificial neural networks and genetic algorithms to increase the rate of penetration

Momeni, M. and Hosseini, S.J. and Ridha, S. and Laruccia, M.B. and Liu, X. (2018) An optimum drill bit selection technique using artificial neural networks and genetic algorithms to increase the rate of penetration. Journal of Engineering Science and Technology, 13 (2). pp. 361-372. (Submitted)

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Drill bit is the most essential tool in drilling and drill bit selection plays a significant role in drilling process planning. This paper discusses bit selection by employing a method of combining Artificial Neural Network (ANN) and the computation of Genetic Algorithm (GA). In this method, offset well drilling records are used for training the ANN model and International Association Drilling Contractors (IADC) bit codes are used to name each bit. However, some researchers have used bit codes as input or output variables. This paper illustrates that the bit codes are better used in referring to the name of each bit instead of using them as values for calculation in the ANN model. The ANN black box was converted to white box to obtain a visible mathematical model for predicting the Rate of Penetration (ROP). This mathematical model, which was the objective function in the GAs, was used to find the optimum drilling values and to maximize the ROP. When drilling a new well, bit selection process requires the maximum ROP of a bit that corresponds to the optimum drilling parameters being obtained by combining the trained ANN model with GA. A bit selection example is provided by using the Shadegan oil field drilling data. The mean square error (MSE) obtained a value of 0.0037 whereas the coefficient of determination obtained a value of 0.9473. In other words, the predicted ROP model based on the field drilling data indicated a good correlation with the real ROP. © School of Engineering, Taylor�s University.

Item Type:Article
Impact Factor:cited By 0
Departments / MOR / COE:Research Institutes > Institute for Hydrocarbon Recovery
ID Code:21803
Deposited By: Ahmad Suhairi
Deposited On:01 Aug 2018 02:03
Last Modified:01 Feb 2019 02:18

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