Year-end Sale % OFF 
Abstract
Optimizing bit selection is one of the main challenges in drilling operations. Bit selection is based on the recorded performance of similar bits from offset wells. There are too many parameters intervening in drilling bit selection. Therefore, developing a logical relationship between them to assist in proper bit selection is extremely necessary and complicated though. In such a case, artificial neural networks (ANNs) have proven to be helpful in recognizing the complex relationship between variables. In this new approach, two models are developed with high proficiency using ANNs. The first model provides appropriate drilling bit selection based on the desired rate of penetration (ROP) to be obtained by applying specific drilling parameters. The second model uses proper drilling parameters obtained from an optimizing procedure to select the drilling bit that provides the maximum achievable rate of penetration. Genetic algorithms (GAs), as a class of optimizing methods for complex functions, are applied to help bit optimization and its related drilling parameters. With the given data sets, these new models predicted successfully the bit types and the optimum drilling parameters. The correlation coefficients for the predicted bit types and optimum drilling parameters in testing the obtained networks are 0.96 and 0.86, respectively. MATLAB software was used to perform ANN and GA solutions.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
