Auto recognition of carbonate microfacies based on an improved back propagation neural network

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• 作者：Yu-xi Wang 王玉猿/a> ; Bo Liu 刘波 ; Ji-xian Gao 高计匿/a>…
• 关键词：carbonate microfacies ; quantitative recognition ; bayes stepwise discrimination ; backward propagation ; neural network ; particle swarm optimizer
• 刊名：Journal of Central South University
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：22
• 期：9
• 页码：3521-3535
• 全文大小：3,554 KB
• 参考文献：[1]BHATT A, HELLE H B. Determination of facies from well logs using modular neural networks [J]. Petroleum Geoscience, 2002, 8: 217′8.CrossRef
  [2]HALBOUTY M T. Giant oil and gas fields of the 1990s: An introduction [C]// Giant Oil and Gas Fields of the Decade 1990?99, AAPG Memoir 78. Tulsa: AAPG, 2003: 1″.
  [3]AQRAWI A A M, THEHNI G A, SHERWANI G H, KAREEM B M A. Mid-Cretaceous rudist-bearing carbonates of the M Formation: An important reservoir sequence in the Mesopotamian basin, Iraq [J]. Journal of Petroleum Geology, 1998, 21(1): 57′.CrossRef
  [4]HAYKIN S. Neural networks: A comprehensive foundation [M]. London, Prentice Hall, 1999: 20‵.
  [5]WOLFF M, PELISSIER-COMBESCURE J. FACIOLOG: Automatic electrofacies determination [C]// SPWLA Annual Logging Symposium. Texas, 1982: 6?
  [6]SERRA O. Fundamentals of well-log interpretation: Developments in petroleum science [M]. New York: Elsevier, 1986: 354‵6.
  [7]DELFINER P, PEYRET O, SERRA O. Automatic determination of lithology from well logs [J]. SPE Formation Evaluation, 1987, 2: 303?0.CrossRef
  [8]BUSH J M, FORTNEY W G, BERRY L N. Determination of lithology from well logs by statistical analysis [J]. SPE Formation Evaluation, 1987, 2: 412?8.CrossRef
  [9]DUDA R O, HART P E. Pattern classification and scene analysis [M]. Oxford: John Wiley & Sons, 1973: 482.
  [10]GRIMM E C. CONISS: A FORTRAN 77 program for stratigraphically constrained cluster analysis by the method of incremental sum of squares [J]. Computers & Geoscience, 1987, 13: 13‵.CrossRef
  [11]FANG J H, CHEN H C, SHULTZ A W, SHULTZ A W, MAHMOUND W. Computer-aided well correlation [J]. AAPG Bulletin, 1992, 76: 307?7.
  [12]GILL D, SHAMRONY A, FLIGELMAN H. Numerical zonation of log suites and log facies recognition by multivariate clustering [J] AAPG Bulletin, 1993, 77: 1781?91.
  [13]WERBOS P J. Beyond regression: New tools for prediction and analysis in the behavioral sciences [D]. Cambridge, MA: Harvard University, 1975.
  [14]LI Yang, TIAN Yan-tao, CHEN Yan-tao. Multi-pattern recognition of sEMG based on improved BP neural network algorithm [C]// 29th Chinese Control Conference (CCC 2010). Beijing, 2010: 2867?72.
  [15]CHEN Hao-rui, WANG Shao-li, GAO Zhan-yi, HU Ya-qiong. Artificial neural network approach for quantifying climate change and human activities impacts on shallow groundwater level-A case study of wuqiao in north China plain [C]// 18th International Conference on Geoinformatics. Beijing, 2010.CrossRef
  [16]IRANI R, NASIMI R. An evolving neural network using an ant colony algorithm for a permeability estimation of the reservoir [J]. Petroleum Science and Technology, 2012, 30(4): 375?4.CrossRef
  [17]LI Zheng, LI Ren-wang, SHANG Zhao-hui WANG Hai-yan, CHEN Xiu-lan, MO Can-lin. Application of BP neural network to sale forecast for H company [C]// 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2012). Wuhan, 2012: 304‰7.
  [18]DEZFOOLIAN M A. Body wave velocities estimation from wireline log data utilizing an artificial neural network for a carbonate reservoir, South Iran [J]. Petroleum Science and Technology, 2013, 31(1): 32″.CrossRef
  [19]REN Chao, AN Ning, WANG Jian-zhou, LI Lian, HU Bin, SHANG Duo. Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting [J]. Knowledge Based Systems, 2014, 56: 226″9.CrossRef
  [20]SAMLI R, SIVRI N, SEVGEN S, KIREMITCI V Z. Applying artificial neural networks for the estimation of chlorophyll-a concentrations along the Istanbul Coast [J]. Polish Journal of Environmental Studies, 2014, 23(4): 1281′87.
  [21]ROGERS S J, FANG J H, KARR C L, STANLEY D A. Determination of lithology from well logs using a neural network [J]. AAPG Bulletin, 1992, 76: 731″9.
  [22]WONG P M, JIAN F X, TAGGART I J. A critical comparison of neural networks and discriminant analysis in lithofacies, porosity and permeability predictions [J] Journal of Petroleum Geology, 1995, 18: 191‰6.CrossRef
  [23]SAGGAF M M, NEBRIJA E L. Estimation of lithologies and depositional facies from wire-line logs [J] AAPG Bulletin, 2000, 84: 1633?46.
  [24]KOHONEN T. Self-organization and associate memory [M]. Berlin: Springer-Verlag, 1989: 4?CrossRef
  [25]HAGAN M T, DEMUTH H B, BEALE M. Neural network design [M]. Boston: PWS Publishing Co., 1996: 346?0.
  [26]WHITE H. Artificial Neural Networks: Approximation and learning theory [M]. Oxford: Basil Blackwell, 1992: 235?0.
  [27]GOLDBERG D E. Genetic Algorithms in Search, Optimization, and Machine Learning [M]. New Jersey: Addison-Wesley, 1989: 125″7.
  [28]FOGEL L J. Evolutionary programming in perspective: the top-down view, in Computational Intelligence: Imitating [M]. Piscataway, NJ: IEEE Press, 1994: 240?7.
  [29]RECHENBERG I. Evolution strategy, in Computational Intelligence: Imitating Life [M]. Piscataway, NJ: IEEE Press, 1994: 126?
• 作者单位：Yu-xi Wang /a> (1) (2)
  Bo Liu (1) (2)
  Ji-xian Gao /a> (3)
  Xue-feng Zhang ?/a> (1) (2)
  Shun-li Li /a> (4)
  Jian-qiang Liu /a> (1) (2)
  Ze-pu Tian /a> (1) (2)
  
  1. Institute of Oil & Gas, Peking University, Beijing, 100871, China
  2. School of Earth and Space Science, Peking University, Beijing, 100871, China
  3. China United Coalbed Methane Limited Company, Beijing, 100011, China
  4. School of Energy Resource, China University of Geosciences, Beijing, 100083, China
  
• 刊物类别：Engineering
• 刊物主题：Engineering, general
  Metallic Materials
  Chinese Library of Science
  
• 出版者：Central South University, co-published with Springer
• ISSN：2227-5223

文摘

Though traditional methods could recognize some facies, e.g. lagoon facies, backshoal facies and foreshoal facies, they couldn’t recognize reef facies and shoal facies well. To solve this problem, back propagation neural network (BP-ANN) and an improved BP-ANN with better stability and suitability, optimized by a particle swarm optimizer (PSO) algorithm (PSO-BP-ANN) were proposed to solve the microfaciesuto discrimination of M formation from the R oil field in Iraq. Fourteen wells with complete core, borehole and log data were chosen as the standard wells and 120 microfacies samples were inferred from these 14 wells. Besides, the average value of gamma, neutron and density logs as well as the sum of squares of deviations of gamma were extracted as key parameters to build log facies (facies from log measurements)—microfacies transforming model. The total 120 log facies samples were divided into 12 kinds of log facies and 6 kinds of microfacies, e.g. lagoon bioclasts micrite limestone microfacies, shoal bioclasts grainstone microfacies, backshoal bioclasts packstone microfacies, foreshoal bioclasts micrite limestone microfacies, shallow continental micrite limestone microfacies and reef limestone microfacies. Furthermore, 68 samples of these 120 log facies samples were chosen as training samples and another 52 samples were gotten as testing samples to test the predicting ability of the discrimination template. Compared with conventional methods, like Bayes stepwise discrimination, both the BP-ANN and PSO-BP-ANN can integrate more log details with a correct rate higher than 85%. Furthermore, PSO-BP-ANN has more simple structure, smaller amount of weight and threshold and less iteration time. Keywords carbonate microfacies quantitative recognition bayes stepwise discrimination backward propagation neural network particle swarm optimizer