A committee machine with empirical formulas for permeability prediction |
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| Affiliation: | 1. Department of Resources Engineering, National Cheng Kung University, Tainan, Taiwan;2. Department of Information Management, Transworld Institute of Technology, Yunlin, Taiwan;1. Saudi Aramco, Dhahran, Saudi Arabia;2. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia;1. Department of Petroleum Engineering, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran;2. Department of Petroleum Engineering, Nour Branch, Islamic Azad University, Nour, Iran;3. Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran;4. DWA Energy Limited, Lincoln, LN5 9JP, United Kingdom;5. Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran;6. School of Earth Sciences & Engineering, Tomsk Polytechnic University, Lenin Avenue, Tomsk, Russia;7. Faculty of Engineering, Department of Computer Engineering, Shahid, Chamran University, Ahwaz, Iran;8. Department of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran;1. Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran, Iran;2. Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz, Iran;1. Department of Oil and Gas Technologies, Perm National Research Polytechnic University, Perm, 614990, Russia;2. Department of Geophysical Research of Wells, LLC LUKOIL-PERM, Perm, 614068, Russia;3. School of Earth Sciences & Engineering, Tomsk Polytechnic University, Lenin Avenue, Tomsk, Russia;4. Department of Mineralogy and Petrography, Perm State National Research University, Perm, 614068, Russia;5. School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, 266580, PR China;1. Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), P.O. Box 63431, Ahwaz, Iran;2. School of Mining, Petroleum & Geophysics Engineering, University of Shahrood, Shahrood, Iran;3. Department of Petroleum Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran;1. Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran;2. National Iranian Oil Company, Tehran, Iran;3. Institute of Geophysics, University of Tehran, Tehran, Iran |
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| Abstract: | This study integrates log-derived empirical formulas and the concept of the committee machine to develop an improved model for predicting permeability. A set of three empirical formulas, such as the Wyllie–Rose, Coates–Dumanoir, and porosity models to correlate reservoir well-logging information with measured core permeability, are used as expert members in a committee machine. A committee machine, a new type of neural network, has a parallel architecture that fuses knowledge by combining the individual outputs of its experts to arrive at an overall output. In this study, an ensemble-based committee machine with empirical formulas (CMEF) is used. This machine combines three individual formulas, each of which performs the same evaluation task. The overall output of each ensemble member is then computed according to the coefficients (weights) of the ensemble averaging method that reflects the contribution of each formula. The optimal combination of weights for prediction is also investigated using a genetic algorithm.We illustrate the method using a case study. Eighty-two data sets composed of well log data and core data were clustered into 41 training sets to construct the model and 41 testing sets to validate the model's predictive ability. A comparison of prediction results from the CMEF model and from three individual empirical formulas showed that the proposed CMEF model for permeability prediction provided the best generalization and performance for validation. This indicated that the CMEF model was more accurate than any one of the individual empirical formulas performing alone. |
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