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
- Offshore Technology Conference, 4-7 May, Houston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2020. Offshore Technology Conference
- Machine Learning, Lost Circulation prediction, Big Data
- 24 in the last 30 days
- 107 since 2007
- Show more detail
- View rights & permissions
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.
|File Size||667 KB||Number of Pages||11|
Yin, Q.,YANG, J.,ZHOU, B.,JIANG, M.,CHEN, X.,FU, C., … LIU, Z. (2018a, January 29). Improve the Drilling Operations Efficiency by the Big Data Mining of Real-Time Logging. Society of Petroleum Engineers. doi:10.2118/189330-MS
Yin, Q.,Yang, J.,Liu, S.,Sun, T.,Li, W.,Li, L., … Deng, H. (2017, May 9). Intelligent Method of Identifying Drilling Risk in Complex Formations Based on Drilled Wells Data. Society of Petroleum Engineers. doi:10.2118/187472-MS
Reza Jahanbakhshi,Reza Keshavarzi & Sajad Jalili (2014) Artificial neural network-based prediction and geomechanical analysis of lost circulation in naturally fractured reservoirs: a case study, European Journal of Environmental and Civil Engineering, 18:3, 320–335, DOI:10.1080/19648189.2013.860924.
Ghalambor, A.,Salehi, S.,Shahri, M. P., & Karimi, M. (2014, February 26). Integrated Workflow for Lost Circulation Prediction. Society of Petroleum Engineers. doi:10.2118/168123-MS.
Abbas, A. K., Hamed, H. M., Al-Bazzaz, W., & Abbas, H. (2019, October 21). Predicting the Amount of Lost Circulation While Drilling Using Artificial Neural Networks: An Example of Southern Iraq Oil Fields. Society of Petroleum Engineers. doi:10.2118/198617-MS.
Al-Hameedi, A. T. T.,Alkinani, H. H., Dunn-Norman, S.,Flori, R. E., Hilgedick, S. A., Alkhamis, M. M., … Alsaba, M. T. (2018a, August 16). Predictive Data Mining Techniques for Mud Losses Mitigation. Society of Petroleum Engineers. doi:10.2118/192182-MS.
Al-Hameedi, A. T. T., Alkinani, H. H., Dunn-Norman, S.,Al-Alwani, M. A., Alkhamis, M. M., & Al-Bazzaz, W. H. (2019, October 25). Application of Artificial Intelligence in the Petroleum Industry: Volume Loss Prediction for Naturally Fractured Formations. Society of Petroleum Engineers. doi:10.2118/196243-MS.
Alkinani, H. H., Al-Hameedi, A. T. T., Dunn-Norman, S.,Alkhamis, M. M., & Mutar, R. A. (2019a, April 8). Prediction of Lost Circulation Prior to Drilling for Induced Fractures Formations Using Artificial Neural Networks. Society of Petroleum Engineers. doi:10.2118/195197-MS.
Sabah, M.,Talebkeikhah, M.,Agin, F.,Telebkeikhah, F.,Hasheminasab, E., Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: A case study from Marun oil field, Journal of Petroleum Science and Engineering (2019), doi: https://doi.org/10.1016/j.petrol.2019.02.045.
Abbas, Ahmed & Al-haideri, Najim & Bashikh, Ali. (2019a). Implementing artificial neural networks and support vector machines to predict lost circulation. Egyptian Journal of Petroleum. 10.1016/j.ejpe.2019.06.006.
Yin, Q.,Yang, J.,Zhou, B.,Luo, M.,LI, W.,Huang, Y., … Wang, J. (2018b, August 27). Operational Designs and Applications of MPD in Offshore Ultra-HTHP Exploration Wells. Society of Petroleum Engineers. doi:10.2118/191060-MS.
Hou, X.,Yang, J.,Yin, Q.,Chen, L.,Cao, B.,Xu, J., … Zhao, X. (2019, October 21). Automatic Gas Influxes Detection in Offshore Drilling Based on Machine Learning Technology. Society of Petroleum Engineers. doi:10.2118/198534-MS
Han Cheng.,Huang, K.,Luo, M.,Liu, X.,Deng, W.. Plugging Technology of High Temperature and High-Pressure Wells in Yingqiong Basin, South China Sea[J]. Oil Drilling & Production Technology, http://kns.cnki.net/kcms/detail/11.1763.TE.20191108.1155.002.html