Video: Lost Circulation Prediction in South China Sea using Machine Learning and Big Data Technology
- Xinxin Hou (China University of Petroleum-Beijing) | Jin Yang (China University of Petroleum-Beijing) | Qishuai Yin (China University of Petroleum-Beijing) | Hexing Liu (CNOOC China Limited, Zhanjiang Branch) | Haodong Chen (CNOOC China Limited, Zhanjiang Branch) | Jinlong Zheng (CNOOC China Limited, Zhanjiang Branch) | Junxiang Wang (China University of Petroleum-Beijing) | Bohan Cao (China University of Petroleum-Beijing) | Xin Zhao (China University of Petroleum-Beijing) | Mingxuan Hao (China University of Petroleum-Beijing) | Xun Liu (China University of Petroleum-Beijing)
- Document ID
- Offshore Technology Conference
- Publication Date
- Document Type
- 2020. Copyright is retained by the author. This document is distributed by OTC with the permission of the author. Contact the author for permission to use material from this document.
- 7 Management and Information, 7.6.7 Neural Networks, 7.2.1 Risk, Uncertainty and Risk Assessment, 1.11 Drilling Fluids and Materials, 4.1.2 Separation and Treating, 4.1 Processing Systems and Design, 7.6.6 Artificial Intelligence, 1.12.6 Drilling Data Management and Standards, 6.1 HSSE & Social Responsibility Management, 7.6 Information Management and Systems, 6 Health, Safety, Security, Environment and Social Responsibility, 1.6 Drilling Operations, 7.2 Risk Management and Decision-Making, 4 Facilities Design, Construction and Operation, 1.12 Drilling Measurement, Data Acquisition and Automation, 2.2 Installation and Completion Operations, 6.1.5 Human Resources, Competence and Training
- Big Data, Lost Circulation prediction, Machine Learning
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Lost circulation is one of the frequent challenges encountered in the well drilling and completion process, which can not only increase well construction time and operational cost but also pose great risk to the formation. However, choosing the most useful treatments may still be a problem due to the complexity of the drilling and geological condition.
In this paper, machine-learning algorithms and big data technology are employed to mine and analyze drilling data of wells in South China Sea where lost circulation is severe. Geological characteristics, drilling fluids property parameters and operational drilling parameters are both considered. Moreover, an artificial neural network is employed to conduct supervised learning. The four metrics: accuracy, precision, f1 score and recall are used to evaluate the model. The trained artificial neural network model is employed to predict the lost circulation risks.
To train and test the proposed model, drilling operation parameters, geological parameters and drilling property parameters are collected for lost circulation events for 50 drilled wells over past two years in South China Sea. The trained model is excellent with the most important evaluation metrics, attaining an accuracy up to 92%, with f1 score, recall and precision up to 89% similarly. This suggests that the model have a good generalization ability and can be applied to other fields. Data analysis through an artificial neural network is carried out to develop a lost circulation prediction system model. This methodology can predict six lost circulation risks, each is defined according to drilling mud loss rate.
This is one of the first attempts to predict lost circulation using data-analytics and artificial intelligence. The proposed intelligent lost circulation prediction method can assist the drilling engineer to choose the optimal drilling parameters prior to drilling and avoid lost circulation events.