Advanced Supervised Machine Learning Algorithms for Efficient Electrofacies Classification of a Carbonate Reservoir in a Giant Southern Iraqi Oil Field
- Watheq J Al-Mudhafar (Basrah Oil Company-Iraq)
- Document ID
- Offshore Technology Conference
- Offshore Technology Conference, 4-7 May, Houston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2020. Offshore Technology Conference
- Carbonate Reservoirs, Machine Learning, Electrofacies Classification, Random Forests, K-Nearest Neighbors
- 6 in the last 30 days
- 96 since 2007
- Show more detail
- View rights & permissions
Understanding the vertical discrete electrofacies distributions in wells is a vital step to preserve the reservoir heterogeneity. Predicting the electrofacies distribution at all wells is commonly conducted manually or with the use of some graphing approaches, but recently different machine learning techniques have been adopted to categorize electrofacies. In this paper, two supervised machine-learning techniques were implemented to model electrofacies given well logging data for a well in order to predict the distributions in all other wells (classification) in a carbonate reservoir in a giant southern Iraqi Oil Field.
The available data included open-hole and CPI well logging records in addition to the routine core analysis. The well discrete electrofacies distribution for the entire reservoir thickness has been obtained in our paper [OTC-29269-MS] using the Ward Hierarchical Clustering Analysis. For electrofacies classification, two supervised machine-learning techniques, K-Nearest Neighbors (KNN) and Random Forests (RF), were adopted to model the resulting electrofacies given the CPI well logging data for a well to predict at other wells that have missing data. These two supervised learning techniques were implemented as non-linear and non-parametric classifiers, which are imperative attribute due to the non-linearity of the electrofacies properties and the geological reservoir control.
The results of this research illustrated that the reservoir electrofacies can be predicted through the use of the supervised learning techniques when well logging records and core data are available. The two adopted classification algorithms were analyzed and compared based on confusion table, transition probability matrix and total percent correct (TCP) of the identified electrofacies that reveal the accuracy of the classification. RF was observed to be the optimum approach as it led to better electrofacies classification in this carbonate reservoir than the KNN.
The application of supervised machine learning techniques enhanced the accuracy and reduced the time spent in electrofacies classification. The two machine learning algorithms were implemented by R software, the most powerful statistical programming language.
|File Size||936 KB||Number of Pages||10|
Al-Mudhafar W. J. M. (2014) Integrating Markov chains for Bayesian estimation of vertical facies sequences through linear discriminant analysis. In: European association of geoscientists and engineers. https://doi.org/10.3997/2214-4609.20141336
Al-Mudhafar, W. J (2017). Integrating kernel support vector machines for efficient rock facies classification in the main pay of Zubair formation in South Rumaila oil field, Iraq. Model Earth Syst Environ 3:12. https://doi.org/10.1007/s40808-017-0277-0
Al-Mudhafar, W. J., Al Lawe, E. M., and Noshi, C. I. (2019). Clustering Analysis for Improved Characterization of Carbonate Reservoirs in a Southern Iraqi Oil Field. Offshore Technology Conference. https://doi.org/10.4043/29269-MS
Adoghe L. I., Aniekwe, O. S., Nwosu, C. (2011) Improving electrofacies modeling using multivariate analysis techniques: a deepwater turbidite case study. In: Nigeria annual international conference and exhibition (30 July–3 August), Abuja, Nigeria. https://doi.org/10.2118/150776-MS
Avseth P, Mukerji T. (2002). Seismic lithofacies classification from well logs using statistical rock physics. Petrophysics 43(02): 70–81. https://doi.org/10.2118/170748-MS
Lee SH, Datta-Gupta A. (1999). Electrofacies characterization and permeability predictions in carbonate reservoirs: role of multi- variate analysis and nonparametric regression. In: SPE annual technical conference and exhibition (3–6 October), Houston, Texas. https://doi.org/10.2118/56658-MS
Tang, H. (2008). Improved carbonate reservoir facies classification using artificial neural network method. In: Canadian international petroleum conference (17–19 June), Calgary, Alberta, Canada. https://doi.org/10.2118/2008-122
Thanh Noi, P., and Kappas, M. (2017). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors (Basel, Switzerland), 18(1), 18. https://doi.org/10.3390/s18010018
Wong, P. M., Jian. F.X., Taggart, I. J. (1995). A critical comparison of neural networks and discriminant analysis in lithofacies, porosity and permeability predictions. J Pet Geol 18:191–206. https://doi.org/10.1111/j.1747-5457.1995.tb00897.x
Zong, M., Zhu, X., and Cheng, D. (2017). Learning k for kNN Classification. ACM Transactions on Intelligent Systems and Technology, Vol. 8, No. 3, Article 43. https://doi.org/10.1145/2990508