Lithological facies classification using deep convolutional neural network

Abstract The aim of this paper is the development of an effective model based on deep learning for geological facies classification in wells. Facies classification is carried out by studying the lithological properties of rocks, which are characteristic of modern sediments, accumulating in certain physical and geographical conditions. In this study, a new 1D-CNN model, which is trained on various optimization algorithms, is proposed. The photoelectric effect, gamma ray, resistivity logging, neutron-density porosity difference, average neutron density porosity and geologic constraining variables are considered as input data of the model. Acceptable accuracy and the use of conventional well log data are the main advantages of the proposed intellectual model. The proposed model is compared with a recurrent neural network model, a long short-term memory model, a support vector machine model, and a k-nearest neighbor model and shows more accurate results in comparison with them. The model shows successful results in the study of well log data and can, therefore, be recommended as a suitable and effective approach for well log data processing required for lithological discrimination.

[1]  Francisco de Assis Boldt,et al.  Binary feature selection classifier ensemble for fault diagnosis of submersible motor pump , 2017, 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE).

[2]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[3]  Luis F. Ayala,et al.  Analysis of Gas-Cycling Performance in Gas/Condensate Reservoirs Using Neuro-Simulation , 2005 .

[4]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[5]  Brendon Hall,et al.  Facies classification using machine learning , 2016 .

[6]  Derek Ohl,et al.  Rock formation characterization for carbon dioxide geosequestration: 3D seismic amplitude and coherency anomalies, and seismic petrophysical facies classification, Wellington and Anson-Bates Fields, Kansas, USA , 2014 .

[7]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[8]  Ali Kadkhodaie-Ilkhchi,et al.  A hybrid approach for litho-facies characterization in the framework of sequence stratigraphy: A case study from the South Pars gas field, the Persian Gulf basin , 2014 .

[9]  R. Zazoun.  Fracture density estimation from core and conventional well logs data using artificial neural networks: The Cambro-Ordovician reservoir of Mesdar oil field, Algeria , 2013 .

[10]  Alireza Bahadori,et al.  Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool , 2015 .

[11]  Donato Malerba,et al.  Machine Learning and Knowledge Discovery in Databases , 2017, Lecture Notes in Computer Science.

[12]  Salaheldin Elkatatny,et al.  Development of new correlations for the oil formation volume factor in oil reservoirs using artificial intelligent white box technique , 2017, Petroleum.

[13]  Ardeshir Hezarkhani,et al.  Comparison of WAVENET and ANN for predicting the porosity obtained from well log data , 2014 .

[14]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[15]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[16]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[17]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[18]  Mohamed S. Kamel,et al.  Modular neural networks: a survey. , 1999, International journal of neural systems.

[19]  Swapan Chakrabarti,et al.  Comparison of four approaches to a rock facies classification problem , 2007, Comput. Geosci..

[20]  M. Adibifard,et al.  Artificial Neural Network (ANN) to estimate reservoir parameters in Naturally Fractured Reservoirs using well test data , 2014 .

[21]  Zhifeng Luo,et al.  Fluvial facies reservoir productivity prediction method based on principal component analysis and artificial neural network , 2016 .

[22]  John H. Doveton,et al.  Multiscale Geologic and Petrophysical Modeling of the Giant Hugoton Gas Field (Permian), Kansas and Oklahoma, U.S.A. , 2006 .

[23]  Shaochun Yang,et al.  Improved pore structure prediction based on MICP with a data mining and machine learning system approach in Mesozoic strata of Gaoqing field, Jiyang depression , 2018, Journal of Petroleum Science and Engineering.

[24]  M. Rezaee,et al.  A systematic method for permeability prediction, a Petro-Facies approach , 2012 .

[25]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.