Assessment of Big Data Analytics Based Ensemble Estimator Module for the Real-Time Prediction of Reservoir Recovery Factor
- Saurabh Tewari (Rajiv Gandhi Institute of Petroleum Technology, India) | U. D. Dwivedi (Rajiv Gandhi Institute of Petroleum Technology, India) | Mohammed Shiblee (King Khalid University, Saudi Arabia)
- Document ID
- Society of Petroleum Engineers
- SPE Middle East Oil and Gas Show and Conference, 18-21 March, Manama, Bahrain
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- 6.1 HSSE & Social Responsibility Management, 7.6 Information Management and Systems, 5.7.2 Recovery Factors, 5.7 Reserves Evaluation, 6 Health, Safety, Security, Environment and Social Responsibility, 5 Reservoir Desciption & Dynamics, 7.6.4 Data Mining, 7 Management and Information, 6.1.5 Human Resources, Competence and Training
- pattern recognition, Reservoir recovery factor, Machine learning, Ensemble methods, Intelligent estimators
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Production of oil & gas depends upon the recoverable amount of hydrocarbon existing beneath the underlying reservoir. Reservoir recovery factor provides of the production potential of ‘proven reservoirs’ which helps the planning of field development and production. Estimation of reservoir recovery factor, with a good degree of accuracy, is still a challenging task for engineers due to the high level of uncertainty, large inexactness, noise, and high dimensionality associated with reservoir measurements. In this paper, we propose a big data-driven ‘ensemble estimator’ (E2) module, comprising of wavelet associated ensemble models for the estimation of reservoir recovery factor. All the ensemble models in E2 were trained on big reservoir data and tested with unknown reservoir data samples obtained from U.S.A. oil & gas fields. Bagging and Random forest ensembles have been utilized to correlate several reservoir properties with reservoir recovery factor. Further, E2 utilizes Relief algorithm to understand the significance of reservoir properties effecting the recovery factor of a reservoir. The proposed E2 module has provided impressive estimation results for the determination of reservoir recovery factor with minimum prediction error. Random forest has given the highest coefficient of correlation (R2=0.9592) and minimum estimation errors viz. mean absolute error (MAE=0.0234) and root mean square error (RMSE=0.0687). The performance of the proposed E2 module was also compared with conventional estimators viz. Radial basis function, Multilayer perceptron, Regression tree and Support vector regression. The experimental results have demonstrated the supremacy of E2 over conventional learners for the estimation of reservoir recovery factor.
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Dietterich, T.G., 2000. Ensemble Methods in Machine Learning. In: Multiple Classifier Systems. MCS 2000. Lecture Notes in Computer Science, Vol. 1857, Springer, Berlin, Heidelberg, 1-15. https://doi.org/10.1007/3-540-45014-9_1
Tewari, S. and Dwivedi, U. D. 2018b. Ensemble-Based Big Data Analytics of Lithofacies for Automatic Development of Petroleum Reservoirs, Computer & Industrial Engineering, https://doi.org/10.1016/j.cie.2018.08.018.
Zang, C. and Ma, Y. 2012. Ensemble machine learning: Methods and application, Springer publication, DOI 10.1007/978-1-4419-9326-71.