Dynamic prediction method of rate of penetration （ROP） in deep geological drilling process based on regional multi-well data optimization and model pre-training

doi:10.12143/j.ztgc.2023.04.001

Home > Archive>Volume 50, Issue 4, 2023 >1-8. DOI:10.12143/j.ztgc.2023.04.001

Dynamic prediction method of rate of penetration （ROP） in deep geological drilling process based on regional multi-well data optimization and model pre-training
DOI:
                        10.12143/j.ztgc.2023.04.001
                    
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Author:
                        GAN Chao1,2,3,5GAN Chao
School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan Hubei 430074, China;Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan Hubei 430074, China;Drilling Engineering Technology Research Center of Shandong Provincial Bureau of Geology & Mineral Resources, Yantai Shandong 264004, China
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WANG Xiang1,2,3WANG Xiang
School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan Hubei 430074, China;Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan Hubei 430074, China
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WANG Luzhao4,5WANG Luzhao
Shangdong No.3 Exploration Institute of Geology and Mineral Resources, Yantai Shandong 264004, China;Drilling Engineering Technology Research Center of Shandong Provincial Bureau of Geology & Mineral Resources, Yantai Shandong 264004, China
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CAO Weihua1,2,3CAO Weihua
School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan Hubei 430074, China;Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan Hubei 430074, China
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WU Min1,2,3WU Min
School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan Hubei 430074, China;Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan Hubei 430074, China
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Affiliation:1.School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;2.Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan Hubei 430074, China;3.Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan Hubei 430074, China;4.Shangdong No.3 Exploration Institute of Geology and Mineral Resources, Yantai Shandong 264004, China;5.Drilling Engineering Technology Research Center of Shandong Provincial Bureau of Geology & Mineral Resources, Yantai Shandong 264004, China
Clc Number:P634
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Abstract:

Accurate prediction of rate of penetration （ROP） in deep geological drilling process can help to improve drilling efficiency and reduce drilling costs， which can provide key technical support for safety and efficient deep geological drilling construction. In this paper， a dynamic prediction method of ROP in deep geological drilling process based on regional multi-well data optimization and model pre-training is proposed. First， the deep geological drilling data warehouse is designed by selecting lithology identification software， drilling process intelligent monitoring cloud platform， and geological cloud system as data sources. Secondly， the regional multi-well data optimization technique is used to select the matching data with the target well in the data warehouse， and the ROP model is pre-trained on this basis. Finally， the ROP prediction model is dynamically updated through combining the actual drilling data of deep geological drilling process， and introducing techniques such as the wavelet filtering， extreme learning machine， and incremental learning strategy. The experimental comparison results verify that the proposed method has strong ROP prediction performance and generalization capability.

Key words:deep geological drilling;regional multi-well data optimization;model pre-training;rate of penetration dynamic prediction;rate of penetration model update

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Received:May 21,2023
Revised:June 24,2023
Adopted:June 25,2023
Online: July 20,2023
Published: July 10,2023

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