Abstract
A reliable and effective model for reservoir physical property prediction is a key to reservoir characterization and management. At present, using well logging data to estimate reservoir physical parameters is an important means for reservoir evaluation. Based on the characteristics of large quantity and complexity of estimating process, we have attempted to design a nonlinear back propagation neural network model optimized by genetic algorithm (BPNNGA) for reservoir porosity prediction. This model is with the advantages of self-learning and self-adaption of back propagation neural network (BPNN), structural parameters optimizing and global searching optimal solution of genetic algorithm (GA). The model is applied to the Chang 8 oil group tight sandstone of Yanchang Formation in southwestern Ordos Basin. According to the correlations between well logging data and measured core porosity data, 5 well logging curves (gamma ray, deep induction, density, acoustic, and compensated neutron) are selected as the input neurons while the measured core porosity is selected as the output neurons. The number of hidden layer neurons is defined as 20 by the method of multiple calibrating optimizations. Modeling results demonstrate that the average relative error of the model output is 10.77%, indicating the excellent predicting effect of the model. The predicting results of the model are compared with the predicting results of conventional multivariate stepwise regression algorithm, and BPNN model. The average relative errors of the above models are 12.83%, 12.9%, and 13.47%, respectively. Results show that the predicting results of the BPNNGA model are more accurate than that of the other two, and BPNNGA is a more applicable method to estimate the reservoir porosity parameters in the study area.
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References Cited
Abudeif, A. M., Attia, M. M., Radwan, A. E., 2016. Petrophysical and Petrographic Evaluation of Sidri Member of Belayim Formation, Badri Field, Gulf of Suez, Egypt. Journal of African Earth Sciences, 115: 108–120. https://doi.org/10.1016/j.jafrearsci.2015.11.028
Ahmadi, M. A., Chen, Z. X., 2019. Comparison of Machine Learning Methods for Estimating Permeability and Porosity of Oil Reservoirs via Petro-Physical Logs. Petroleum, 5(3): 271–284. https://doi.org/10.1016/j.petlm.2018.06.002
Ahmadi, M. A., Zendehboudi, S., Lohi, A., et al., 2013. Reservoir Permeability Prediction by Neural Networks Combined with Hybrid Genetic Algorithm and Particle Swarm Optimization. Geophysical Prospecting, 61(3): 582–598. https://doi.org/10.1111/j.1365-2478.2012.01080.x
Al-Anazi, A. F., Gates, I. D., 2012. Support Vector Regression to Predict Porosity and Permeability: Effect of Sample Size. Computers & Geosciences, 39(1): 64–76. https://doi.org/10.1016/j.cageo.2011.06.011
Asoodeh, M., Bagheripour, P., 2013. Core Porosity Estimation through Different Training Approaches for Neural Network: Back-Propagation Learning vs. Genetic Algorithm. International Journal of Computer Applications, 63(5): 11–15. https://doi.org/10.5120/10461-5172
Bagheripour, P., 2014. Committee Neural Network Model for Rock Permeability Prediction. Journal of Applied Geophysics, 104: 142–148. https://doi.org/10.1016/j.jappgeo.2014.03.001
Bhatt, A., Helle, H. B., 2002. Committee Neural Networks for Porosity and Permeability Prediction from Well Logs. Geophysical Prospecting, 50(6): 645–660. https://doi.org/10.1046/j.1365-2478.2002.00346.x
Cao, J. H., Yang, J. C., Wang, Y., et al., 2015. Extreme Learning Machine for Reservoir Parameter Estimation in Heterogeneous Reservoir. Mathematical Problems in Engineering, 2: 1–10. https://doi.org/10.1155/2015/287816
Chen, L., Lu, Y. C., Wu, J. Y., et al., 2015. Sedimentary Facies and Depositional Model of Shallow Water Delta Dominated by Fluvial for Chang 8 Oil-Bearing Group of Yanchang Formation in Southwestern Ordos Basin, China. Journal of Central South University, 22(12): 4749–4763. https://doi.org/10.1007/s11771-015-3027-3
Chen, W., Yang, L. Q., Zha, B., et al., 2020. Deep Learning Reservoir Porosity Prediction Based on Multilayer Long Short-Term Memory Network. Geophysics, 85(4): WA213–WA225. https://doi.org/10.1190/geo2019-0261.1
Chen, Y. F., Yu, G. Y., Long, Y., et al., 2019. Application of Radial Basis Function Artificial Neural Network to Quantify Interfacial Energies Related to Membrane Fouling in a Membrane Bioreactor. Bioresource Technology, 293: 122103. https://doi.org/10.1016/j.biortech.2019.122103
Das, B., Chatterjee, R., 2018. Well Log Data Analysis for Lithology and Fluid Identification in Krishna-Godavari Basin, India. Arabian Journal of Geosciences, 11(10): 1–12. https://doi.org/10.1007/s12517-018-3587-2
Deb, K., Pratap, A., Agarwal, S., et al., 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2): 182–197. https://doi.org/10.1109/4235.996017
Ding, Y. R., Cai, Y. J., Sun, P. D., et al., 2014. The Use of Combined Neural Networks and Genetic Algorithms for Prediction of River Water Quality. Journal of Applied Research and Technology, 12(3): 493–499. https://doi.org/10.1016/S1665-6423(14)71629-3
Dorrington, K. P., Link, C. A., 2004. Genetic-Algorithm/Neural-Network Approach to Seismic Attribute Selection for Well-Log Prediction. Geophysics, 69(1): 212–221. https://doi.org/10.1190/1.1649389
Ehrenberg, S. N., Nadeau, P. H., Steen, O., 2008. A Megascale View of Reservoir Quality in Producing Sandstones from the Offshore Gulf of Mexico. AAPG Bulletin, 92(2): 145–164. https://doi.org/10.1306/09280707062
Elkatatny, S., Mahmoud, M., 2018. Development of New Correlations for the Oil Formation Volume Factor in Oil Reservoirs Using Artificial Intelligent White Box Technique. Petroleum, 4(2): 178–186. https://doi.org/10.1016/j.petlm.2017.09.009
Félix, L. C. M., Muñoz, L. A. B., 2005. Representing a Relation between Porosity and Permeability Based on Inductive Rules. Journal of Petroleum Science and Engineering, 47(1/2): 23–34. https://doi.org/10.1016/j.petrol.2004.11.008
Goh, A. T. C., 1995. Back-Propagation Neural Networks for Modeling Complex Systems. Artificial Intelligence in Engineering, 9(3): 143–151. https://doi.org/10.1016/0954-1810(94)00011-S
Gu, Y. F., Bao, Z. D., Lin, Y. B., et al., 2017. The Porosity and Permeability Prediction Methods for Carbonate Reservoirs with Extremely Limited Logging Data: Stepwise Regression vs. N-Way Analysis of Variance. Journal of Natural Gas Science and Engineering, 42: 99–119. https://doi.org/10.1016/j.jngse.2017.03.010
Gupta, D., Ghafir, S., 2012. An Overview of Methods Maintaining Diversity in Genetic Algorithms. International Journal of Emerging Technology and Advanced Engineering, 2(5): 56–60
Harris, J. R., Grunsky, E. C., 2015. Predictive Lithological Mapping of Canada’s North Using Random Forest Classification Applied to Geophysical and Geochemical Data. Computers & Geosciences, 80: 9–25. https://doi.org/10.1016/j.cageo.2015.03.013
Helle, H. B., Bhatt, A., Ursin, B., 2001. Porosity and Permeability Prediction from Wireline Logs Using Artificial Neural Networks: A North Sea Case Study. Geophysical Prospecting, 49(4): 431–444. https://doi.org/10.1046/j.1365-2478.2001.00271.x
Janiga, D., Czarnota, R., Stopa, J., et al., 2019. Self-Adapt Reservoir Clusterization Method to Enhance Robustness of Well Placement Optimization. Journal of Petroleum Science and Engineering, 173: 37–52. https://doi.org/10.1016/j.petrol.2018.10.005
Ji, L. M., Yan, K., Meng, F. W., et al., 2010. The Oleaginous Botryococcus from the Triassic Yanchang Formation in Ordos Basin, Northwestern China: Morphology and Its Paleoenvironmental Significance. Journal of Asian Earth Sciences, 38(5): 175–185. https://doi.org/10.1016/j.jseaes.2009.12.010
Kamel, M. H., Mohamed, M. M., 2006. Effective Porosity Determination in Clean/Shaly Formations from Acoustic Logs with Applications. Journal of Petroleum Science and Engineering, 51(3/4): 267–274. https://doi.org/10.1016/j.petrol.2006.01.007
Kaydani, H., Mohebbi, A., Baghaie, A., 2011. Permeability Prediction Based on Reservoir Zonation by a Hybrid Neural Genetic Algorithm in One of the Iranian Heterogeneous Oil Reservoirs. Journal of Petroleum Science and Engineering, 78(2): 497–504. https://doi.org/10.1016/j.petrol.2011.07.017
Kaydani, H., Mohebbi, A., Eftekhari, M., 2014. Permeability Estimation in Heterogeneous Oil Reservoirs by Multi-Gene Genetic Programming Algorithm. Journal of Petroleum Science and Engineering, 123: 201–206. https://doi.org/10.1016/j.petrol.2014.07.035
Keane, A. J., 1995. Genetic Algorithm Optimization of Multi-Peak Problems: Studies in Convergence and Robustness. Artificial Intelligence in Engineering, 9(2): 75–83. https://doi.org/10.1016/0954-1810(95)95751-q
Konaté, A. A., Pan, H. P., Khan, N., et al., 2015. Generalized Regression and Feed-Forward Back Propagation Neural Networks in Modelling Porosity from Geophysical Well Logs. Journal of Petroleum Exploration and Production Technology, 5(2): 157–166. https://doi.org/10.1007/s13202-014-0137-7
Leung, F. H. F., Lam, H. K., Ling, S. H., et al., 2003. Tuning of the Structure and Parameters of a Neural Network Using an Improved Genetic Algorithm. IEEE Transactions on Neural Networks, 14(1): 79–88. https://doi.org/10.1109/tnn.2002.804317
Li, D., Li, R., Zhu, Z., et al., 2017. Rare Earth Elements Geochemistry Characteristics and Their Geological Implications of Lacustrine Oil Shale from Chang 7 Oil Layer in Southern Ordos Basin, China. Geological Journal, 52: 119–131. https://doi.org/10.1002/gj.2980
Li, Y., Kang, Z. J., Xue, Z. J., et al., 2018. Theories and Practices of Carbonate Reservoirs Development in China. Petroleum Exploration and Development, 45(4): 712–722. https://doi.org/10.1016/s1876-3804(18)30074-0
Li, Z. X., Qu, X. F., Liu, W. T., et al., 2015. Development Modes of Triassic Yanchang Formation Chang 7 Member Tight Oil in Ordos Basin, NW China. Petroleum Exploration and Development, 42(2): 241–246. https://doi.org/10.1016/S1876-3804(15)30011-2
Lin, L., Zhang, W., Ma, Z. Y., et al., 2020. Porosity Estimation of Abradable Seal Coating with an Optimized Support Vector Regression Model Based on Multi-Scale Ultrasonic Attenuation Coefficient. NDT & E International, 113: 102272. https://doi.org/10.1016/j.ndteint.2020.102272
Lin, S. C., Ting, C. J., 1996. Drill Wear Monitoring Using Neural Networks. International Journal of Machine Tools and Manufacture, 36(4): 465475. https://doi.org/10.1016/0890-6955(95)00059-3
Liu, S. Y., Zolfaghari, A., Sattarin, S., et al., 2019. Application of Neural Networks in Multiphase Flow through Porous Media: Predicting Capillary Pressure and Relative Permeability Curves. Journal of Petroleum Science and Engineering, 180: 445–455. https://doi.org/10.1016/j.petrol.2019.05.041
Lü, P., Yuan, L., Zhang, J. F., 2009. Cloud Theory-Based Simulated Annealing Algorithm and Application. Engineering Applications of Artificial Intelligence, 22(4/5): 742–749. https://doi.org/10.1016/j.engappai.2009.03.003
Majdi, A., Beiki, M., 2010. Evolving Neural Network Using a Genetic Algorithm for Predicting the Deformation Modulus of Rock Masses. International Journal of Rock Mechanics and Mining Sciences, 47(2): 246–253. https://doi.org/10.1016/j.ijrmms.2009.09.011
Matin, S. S., Chelgani, S. C., 2016. Estimation of Coal Gross Calorific Value Based on Various Analyses by Random Forest Method. Fuel, 177: 274–278. https://doi.org/10.1016/j.fuel.2016.03.031
Matin, S. S., Farahzadi, L., Makaremi, S., et al., 2018. Variable Selection and Prediction of Uniaxial Compressive Strength and Modulus of Elasticity by Random Forest. Applied Soft Computing, 70: 980–987. https://doi.org/10.1016/j.asoc.2017.06.030
Mohaghegh, S., Arefi, R., Ameri, S., et al., 1995. Design and Development of an Artificial Neural Network for Estimation of Formation Permeability. SPE Computer Applications, 7(6): 151–154. https://doi.org/10.2118/28237-pa
Morris, G. M., Goodsell, D. S., Halliday, R. S., et al., 1998. Automated Docking Using a Lamarckian Genetic Algorithm and an Empirical Binding Free Energy Function. Journal of Computational Chemistry, 19(14): 1639–1662. https://doi.org/10.1002/(sici)1096-987x(19981115)19:141639:aid-jcc10>3.0.co;2-b
Nur, A., Mavko, G., Dvorkin, J., et al., 1998. Critical Porosity: A Key to Relating Physical Properties to Porosity in Rocks. The Leading Edge, 17(3): 357–362. https://doi.org/10.1190/1.1437977
Onalo, D., Oloruntobi, O., Adedigba, S., et al., 2019. Dynamic Data Driven Sonic Well Log Model for Formation Evaluation. Journal of Petroleum Science and Engineering, 175: 1049–1062. https://doi.org/10.1016/j.petrol.2019.01.042
Paasche, H., Tronicke, J., Holliger, K., et al., 2006. Integration of Diverse Physical-Property Models: Subsurface Zonation and Petrophysical Parameter Estimation Based on Fuzzy c-Means Cluster Analyses. Geophysics, 71(3): H33–H44. https://doi.org/10.1190/1.2192927
Panda, S. S., Singh, A. K., Chakraborty, D., et al., 2006. Drill Wear Monitoring Using Back Propagation Neural Network. Journal of Materials Processing Technology, 172(2): 283–290. https://doi.org/10.1016/j.jmatprotec.2005.10.021
Paxton, S. T., Szabo, J. O., Ajdukiewicz, J. M., et al., 2002. Construction of an Intergranular Volume Compaction Curve for Evaluating and Predicting Compaction and Porosity Loss in Rigid-Grain Sandstone Reservoirs. AAPG Bulletin, 86(12): 2047–2067
Ren, X. X., Hou, J. G., Song, S. H., et al., 2019. Lithology Identification Using Well Logs: A Method by Integrating Artificial Neural Networks and Sedimentary Patterns. Journal of Petroleum Science and Engineering, 182: 106336. https://doi.org/10.1016/j.petrol.2019.106336
Ross, D. J. K., Bustin, R. M., 2009. The Importance of Shale Composition and Pore Structure upon Gas Storage Potential of Shale Gas Reservoirs. Marine and Petroleum Geology, 26(6): 916–927. https://doi.org/10.1016/j.marpetgeo.2008.06.004
Rumpf, M., Tronicke, J., 2014. Predicting 2D Geotechnical Parameter Fields in Near-Surface Sedimentary Environments. Journal of Applied Geophysics, 101: 95–107. https://doi.org/10.1016/j.jappgeo.2013.12.002
Ruuth, S. J., 2006. Global Optimization of Explicit Strong-Stability-Preserving Runge-Kutta Methods. Mathematics of Computation, 75(253): 183–208. https://doi.org/10.1090/s0025-5718-05-01772-2
Saemi, M., Ahmadi, M., Varjani, A. Y., 2007. Design of Neural Networks Using Genetic Algorithm for the Permeability Estimation of the Reservoir. Journal of Petroleum Science and Engineering, 59(1/2): 97–105. https://doi.org/10.1016/j.petrol.2007.03.007
Shi, J. A., Wang, J. P., Mao, M. L., et al., 2003. Reservoir Sandstone Diagenesis of Member 6 to 8 in Yanchang Formation (Triassic), Xifeng Oilfield, Ordos Basin. Acta Sedimentologica Sinica, 21(3): 373–380. https://doi.org/10.1007/BF02873154 (in Chinese with English Abstract)
Soepangkat, B. O. P., Pramujati, B., Effendi, M. K., et al., 2019. Multi-Objective Optimization in Drilling Kevlar Fiber Reinforced Polymer Using Grey Fuzzy Analysis and Backpropagation Neural Network-Genetic Algorithm (BPNN-GA) Approaches. International Journal of Precision Engineering and Manufacturing, 20(4): 593–607. https://doi.org/10.1007/s12541-019-00017-z
Tan, M. J., Xiao, C. W., Han, C., et al., 2020. Fluid Identification Method and Adaptability Analysis of Ultra-Low Porosity Tight Sandstone in Kuqa Depression, Tarim Basin. Geophysics. https://doi.org/10.20944/preprints202008.0559.v1
Tsanas, A., Xifara, A., 2012. Accurate Quantitative Estimation of Energy Performance of Residential Buildings Using Statistical Machine Learning Tools. Energy and Buildings, 49: 560–567. https://doi.org/10.1016/j.enbuild.2012.03.003
van der Baan, M., Jutten, C., 2000. Neural Networks in Geophysical Applications. Geophysics, 65(4): 1032–1047
Wang, G. W., Chang, X. C., Yin, W., et al., 2017. Impact of Diagenesis on Reservoir Quality and Heterogeneity of the Upper Triassic Chang 8 Tight Oil Sandstones in the Zhenjing Area, Ordos Basin, China. Marine and Petroleum Geology, 83: 84–96. https://doi.org/10.1016/j.marpetgeo.2017.03.008
Wang, H. J., Wu, W., Chen, T., et al., 2019. An Improved Neural Network for TOC, S1 and S2 Estimation Based on Conventional Well Logs. Journal of Petroleum Science and Engineering, 176: 664–678. https://doi.org/10.1016/j.petrol.2019.01.096
Wang, P., Peng, S. P., 2019. On a New Method of Estimating Shear Wave Velocity from Conventional Well Logs. Journal of Petroleum Science and Engineering, 180: 105–123. https://doi.org/10.1016/j.petrol.2019.05.033
Wang, Y., Lu, C. J., Zuo, C. P., 2015. Coal Mine Safety Production Forewarning Based on Improved BP Neural Network. International Journal of Mining Science and Technology, 25(2): 319–324. https://doi.org/10.1016/j.ijmst.2015.02.023
Wong, P. M., Gedeon, T. D., Taggart, I. J., 1995. An Improved Technique in Porosity Prediction: A Neural Network Approach. IEEE Transactions on Geoscience and Remote Sensing, 33(4): 971–980. https://doi.org/10.1109/36.406683
Xi, K. L., Cao, Y. C., Liu, K. Y., et al., 2019. Diagenesis of Tight Sandstone Reservoirs in the Upper Triassic Yanchang Formation, Southwestern Ordos Basin, China. Marine and Petroleum Geology, 99: 548–562. https://doi.org/10.1016/j.marpetgeo.2018.10.031
Yang, M. H., Li, L., Zhou, J., et al., 2015. Mesozoic Structural Evolution of the Hangjinqi Area in the Northern Ordos Basin, North China. Marine and Petroleum Geology, 66: 695–710. https://doi.org/10.1016/j.marpetgeo.2015.07.014
Yang, Y., Guo, C. H., Yuan, X. H., 2005. Application of BP Neural Network Improved by Genetic Algorithm in Log Interpretation in Luodai Gas Field. Natural Gas Industry, 25(8): 47–49. https://doi.org/10.1360/gs050303 (in Chinese with English Abstract)
Yao, Y. B., Liu, D. M., Che, Y., et al., 2010. Petrophysical Characterization of Coals by Low-Field Nuclear Magnetic Resonance (NMR). Fuel, 89(7): 1371–1380. https://doi.org/10.1016/j.fuel.2009.11.005
You, H. H., Ma, Z. Y., Tang, Y. J., et al., 2017. Comparison of ANN (MLP), ANFIS, SVM, and RF Models for the Online Classification of Heating Value of Burning Municipal Solid Waste in Circulating Fluidized Bed Incinerators. Waste Management, 68: 186–197. https://doi.org/10.1016/j.wasman.2017.03.044
Yu, S. W., Zhu, K. J., Diao, F. Q., 2008. A Dynamic All Parameters Adaptive BP Neural Networks Model and Its Application on Oil Reservoir Prediction. Applied Mathematics and Computation, 195(1): 66–75. https://doi.org/10.1016/j.amc.2007.04.088
Zeng, L. B., Li, X. Y., 2009. Fractures in Sandstone Reservoirs with Ultra-Low Permeability: A Case Study of the Upper Triassic Yanchang Formation in the Ordos Basin, China. AAPG Bulletin, 93(4): 461–477. https://doi.org/10.1306/09240808047
Zhang, F., Jiao, Y. Q., Wu, L. Q., et al., 2019. Relations of Uranium Enrichment and Carbonaceous Debris within the Daying Uranium Deposit, Northern Ordos Basin. Journal of Earth Science, 30(1): 142–157. https://doi.org/10.1007/s12583-017-0952-0
Zhao, X., Liu, C., Wang, J., et al., 2020. Provenance Analyses of Lower Cretaceous Strata in the Liupanshan Basin: From Paleocurrents Indicators, Conglomerate Clast Compositions, and Zircon U-Pb Geochronology. Journal of Earth Science, 31(4): 757–771. https://doi.org/10.1007/s12583-020-1324-8
Zhong, Z., Carr, T. R., 2016. Application of Mixed Kernels Function (MKF) Based Support Vector Regression Model (SVR) for CO2-Reservoir Oil Minimum Miscibility Pressure Prediction. Fuel, 184: 590–603. https://doi.org/10.1016/j.fuel.2016.07.030
Zhong, Z., Carr, T. R., Wu, X. M., et al., 2019. Application of a Convolutional Neural Network in Permeability Prediction: A Case Study in the Jacksonburg-Stringtown Oil Field, West Virginia, USA. Geophysics, 84(6): B363–B373. https://doi.org/10.1190/geo2018-0588.1
Zhou, Y., Ji, Y. L., Xu, L. M., et al., 2016. Controls on Reservoir Heterogeneity of Tight Sand Oil Reservoirs in Upper Triassic Yanchang Formation in Longdong Area, Southwest Ordos Basin, China: Implications for Reservoir Quality Prediction and Oil Accumulation. Marine and Petroleum Geology, 78: 110–135. https://doi.org/10.1016/j.marpetgeo.2016.09.006
Zou, C. N., Zhao, Q., Dong, D. Z., et al., 2017. Geological Characteristics, Main Challenges and Future Prospect of Shale Gas. Natural Gas Geoscience, 28(12): 1781–1796 (in Chinese with English Abstract)
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This research was supported by the National Natural Science Foundation of China (No. 41002045). We are grateful to Huabei Branch Company of SINOPEC for providing samples for analysis and the reviewers for their constructive comments which significantly improved the manuscript. The final publication is available at Springer via https://doi.org/10.1007/s12583-020-1396-5.
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Chen, L., Lin, W., Chen, P. et al. Porosity Prediction from Well Logs Using Back Propagation Neural Network Optimized by Genetic Algorithm in One Heterogeneous Oil Reservoirs of Ordos Basin, China. J. Earth Sci. 32, 828–838 (2021). https://doi.org/10.1007/s12583-020-1396-5
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DOI: https://doi.org/10.1007/s12583-020-1396-5