Water Control Diagnostic Plot Pattern Recognition Using Support Vector Machine
- Akhan Mukhanov (Schlumberger) | Carlos Arturo Garcia (Schlumberger) | Henry Torres (Schlumberger)
- Document ID
- Society of Petroleum Engineers
- SPE Russian Petroleum Technology Conference, 15-17 October, Moscow, Russia
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 6 Health, Safety, Security, Environment and Social Responsibility, 7 Management and Information, 7.6.6 Artificial Intelligence, 7.6.4 Data Mining, 6.1.5 Human Resources, Competence and Training, 7.6 Information Management and Systems, 6.1 HSSE & Social Responsibility Management
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Petroleum engineers widely use Chan water control diagnostic plots to visually examine patterns for mechanisms behind excessive water production in petroleum wells. Distinct signatures reveal constant water-oil ratio (WOR), normal displacement of oil by water, multilayer channeling, and rapid channeling. Visual diagnosis requires extensive practical experience. High well counts amplify the need for timely relevant solutions. This study presents a supervised machine learning (ML) technique, support vector machine (SVM), to automatically identify Chan diagnostic patterns for timely detection and control of excess water production.
The project involved publicly available production data. First, we performed manual identification of different Chan plot patterns and split the data set for training, cross-validation, and testing. Next, we normalized each subset, balanced the number of samples per pattern, trained the SVM model, and evaluated its ability on the test data set. The final stage involved model parameter tuning to explore opportunities to improve the model accuracy.
When collecting representative samples, we observed uneven sample counts for pattern type subsets. Balancing the training data set was necessary to ensure that the model could generalize well for previously unseen production data. To ensure a relevant comparison among wells within each pattern type, we performed time- and WOR-based normalization.
We confirmed that the SVM model was highly accurate across diverse fields and hence did not exhibit a locality bias. Operator companies can harness the prior classification of water control trends by experienced engineers. As more wells start exhibiting specific pattern signatures, the algorithm helps to proactively identify them. The approach maximizes the effectiveness of water production analysis and helps to reinforce technical quality and continuity among engineering personnel.
The study demonstrated an application of SVM to the classification task of identifying water control diagnostic plot signatures across different fields. We proposed stricter visual definitions of signature types. Patterns such as normal displacement and multilayer channeling are extremely similar; therefore, we presented feature engineering required to increase classification accuracy in similar edge cases. With various kernels, the described SVM methodology can help production engineers to proactively perform surveillance activities that require identifying signature patterns in Chan plots.
|File Size||1 MB||Number of Pages||19|
Caruana, Rich, and Alexandru Niculescu-Mizil. 2006. "An Empirical Comparison of Supervised Learning Algorithms." 23 International Conference on Machine Learning. Pittsburgh, PA, USA: ACM. 161-168. doi: https://doi.org/10.1145/1143844.1143865.
Chan, K. S. 1995. "Water Control Diagnostic Plots." SPE ATCE, SPE-30775-MS. Dallas, TX, USA. doi: https://doi.org/10.2118/30775-MS.
Liu, Yintao, Ke-Thia Yao, S. Cauligi Raghavenda, Anqi Wu, Dong Guo, Jingwen Zheng, Lanre Olabinjo, Oluwafemi Balogun, and Eraj Ershaghi. 2013. "Global Model for Failure Prediction for Rod Pump Artificial Lift Systems." SPE Western & AAPG Pacific Section Meeting, SPE-165374-MS. Monterey, CA, USA. Accessed May 1, 2018. https://doi.org/10.2118/165374-MS.
Pedregosa, F, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, M Blondel, . 2011. "Scikit-learn: Machine Learning in Python." Journal of Machine Learning Research 2825-2830. http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html.
Rabiei, M., R. Gupta, Y. P. Cheong, and G. A. Sanchez Soto. 2010. "Transforming Data into Knowledge using Data Mining Techniques: Application in Water Production Problem Diagnosis in Oil Wells." SPE Asia Pacific Oil & Gas Conference and Exhibition, SPE-133929-MS. Brisbane, QLD, Australia. https://doi.org/10.2118/133929-MS.
Rasmussen, Carl Edward, and Christopher K. I. Williams. 2006. Gaussian Processes for Machine Learning. Cambridge, Massachusetts: The MIT Press. http://www.gaussianprocess.org/gpml/chapters/RW.pdf.
Yang, Z., and I. Ershaghi. 2005. "A Method for Pattern Recognition of WOR Plots in Waterflood Management." SPE Western Regional Meeting, SPE-93870-MS. Irvine, CA, USA. Accessed April 30, 2018. https://doi.org/10.2118/93870-MS.