relation: https://khub.utp.edu.my/scholars/16193/
title: Application of Multiple Regression Technique for Predicting Drilling Rate in Loss Zone
creator: Sauki, A.
creator: Khamaruddin, P.N.F.M.
creator: Irawan, S.
creator: Kinif, I.
creator: Ridha, S.
description: Prediction of drilling rate is important to optimize drilling operation, and it is typically performed based on gathering drilling data from the history of nearby wells. Previous researchers have proposed numerous techniques to improve the mathematical model's accuracy and the multiple regression techniques in predicting the drilling rate. However, 100 accuracy of the drilling rate model as compared to field data is yet achievable. This study proposes a modification of multiple regression techniques by gathering data according to the loss zone. Thus, the Bourgoyne and Young model has been used, and the drilling data from the North Kuwait field was selected for the multiple regression analysis. Multiple regression analysis was compared according to loss zone, formation type, BHA interval and single well to achieve the Bourgoyne and Young model's coefficients. The achieved coefficients were used for drilling rate estimation using the model and compared with the real field data. Based on the findings, it was observed that using multiple regression techniques according to loss zone would result in more accurate prediction than other methods with the improvement of R2 value from 0.523 to 0.646 (12.3 improvement). Therefore, it is suggested that categorizing drilling data according to the loss zone for multiple regression analysis needs to be considered for drilling rate prediction for better accuracy. © 2022 American Institute of Physics Inc.. All rights reserved.
publisher: American Institute of Physics Inc.
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Sauki, A. and Khamaruddin, P.N.F.M. and Irawan, S. and Kinif, I. and Ridha, S.  (2022) Application of Multiple Regression Technique for Predicting Drilling Rate in Loss Zone.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142479660&doi=10.1063%2f5.0122008&partnerID=40&md5=cdc0344600148c2c353899e29b47b6bd
relation: 10.1063/5.0122008
identifier: 10.1063/5.0122008