A data-driven field-scale approach to estimate the permeability of fractured rocks

Abstract In this study, based on compiled numerical-experimental datasets, important variables, including porosity, density, compressional and shear wave velocity, were utilised to develop a novel model for predicting coal permeability with high accuracy. Multiple linear regression, response surface methodology and gene expression programming were employed to develop empirical models based on the foregoing variables. The performance of the developed models was evaluated using statistical indices. The results showed that the GEP-based model has the best prediction performance compared with other techniques. The proposed GEP-based model with its explicit structure can be readily used in practical applications by field engineers.

[1]  Roohollah Shirani Faradonbeh,et al.  The propensity of the over-stressed rock masses to different failure mechanisms based on a hybrid probabilistic approach , 2022, Tunnelling and Underground Space Technology.

[2]  Danqi Li,et al.  Numerical simulation of fully encapsulated rock bolts with a tri-linear constitutive relation , 2021, Tunnelling and Underground Space Technology.

[3]  Hossein Masoumi,et al.  A novel workflow based on physics-informed machine learning to determine the permeability profile of fractured coal seams using downhole geophysical logs , 2021 .

[4]  M. Cai,et al.  A constitutive model for modified cable bolts exhibiting cone shaped failure mode , 2021 .

[5]  H. Pan,et al.  Machine learning - A novel approach of well logs similarity based on synchronization measures to predict shear sonic logs , 2021 .

[6]  E. Burnaev,et al.  Machine Learning on Field Data for Hydraulic Fracturing Design Optimization: Digital Database and Production Forecast Model , 2020 .

[7]  M. Ducros,et al.  Map-based uncertainty analysis for exploration using basin modeling and machine learning techniques applied to the Levant Basin petroleum systems, Eastern Mediterranean , 2020 .

[8]  Danqi Li,et al.  Assessing the mechanical performance of different cable bolts based on design of experiments techniques and analysis of variance , 2020 .

[9]  Hao Li,et al.  Machine learning for the subsurface characterization at core, well, and reservoir scales , 2020 .

[10]  Roohollah Shirani Faradonbeh,et al.  Rockburst assessment in deep geotechnical conditions using true-triaxial tests and data-driven approaches , 2020 .

[11]  D. Gao,et al.  Machine learning-based seismic spectral attribute analysis to delineate a tight-sand reservoir in the Sulige gas field of central Ordos Basin, western China , 2020 .

[12]  P. Glover,et al.  Permeability prediction and diagenesis in tight carbonates using machine learning techniques , 2020, Marine and Petroleum Geology.

[13]  Li Kong,et al.  Fast prediction of reservoir permeability based on embedded feature selection and LightGBM using direct logging data , 2020, Measurement Science and Technology.

[14]  A. Hedayat,et al.  Coupling Taguchi and Response Surface Methodologies for the Efficient Characterization of Jointed Rocks’ Mechanical Properties , 2019, Rock Mechanics and Rock Engineering.

[15]  Sami Shaffiee Haghshenas,et al.  A new conventional criterion for the performance evaluation of gang saw machines , 2019, Measurement.

[16]  P. Hagan,et al.  Parametric study of fully grouted cable bolts subjected to axial loading , 2019, Canadian Geotechnical Journal.

[17]  Mojtaba Rajabi,et al.  Automated classification of metamorphosed coal from geophysical log data using supervised machine learning techniques , 2019, International Journal of Coal Geology.

[18]  Shahab Mohaghegh,et al.  Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media , 2019, Fluids.

[19]  Zhongwei Chen,et al.  Coal identification using neural networks with real-time coalbed methane drilling data , 2019, The APPEA Journal.

[20]  Saro Lee,et al.  Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models , 2019, Applied Sciences.

[21]  Yufeng Gu,et al.  Permeability prediction using hybrid techniques of continuous restricted Boltzmann machine, particle swarm optimization and support vector regression , 2018, Journal of Natural Gas Science and Engineering.

[22]  Y. Liu,et al.  Characterizations of full-scale pore size distribution, porosity and permeability of coals: A novel methodology by nuclear magnetic resonance and fractal analysis theory , 2018, International Journal of Coal Geology.

[23]  Roohollah Shirani Faradonbeh,et al.  Development of GP and GEP models to estimate an environmental issue induced by blasting operation , 2018, Environmental Monitoring and Assessment.

[24]  Zhixi Chen,et al.  Coal permeability: Gas slippage linked to permeability rebound , 2018 .

[25]  Hao Li,et al.  Prediction of Subsurface NMR T2 Distributions in a Shale Petroleum System Using Variational Autoencoder-Based Neural Networks , 2017, IEEE Geoscience and Remote Sensing Letters.

[26]  Masoud Monjezi,et al.  Roadheader performance prediction using genetic programming (GP) and gene expression programming (GEP) techniques , 2017, Environmental Earth Sciences.

[27]  T. N. Singh,et al.  Predicting CO2 permeability of bituminous coal using statistical and adaptive neuro-fuzzy analysis , 2017 .

[28]  Hao Xu,et al.  Fluid velocity sensitivity of coal reservoir and its effect on coalbed methane well productivity: A case of Baode Block, northeastern Ordos Basin, China , 2017 .

[29]  A. Salmachi,et al.  Fluid flow characteristics of Bandanna Coal Formation: a case study from the Fairview Field, eastern Australia , 2017 .

[30]  Masoud Monjezi,et al.  Prediction and minimization of blast-induced ground vibration using two robust meta-heuristic algorithms , 2017, Engineering with Computers.

[31]  Sayan Ghosh,et al.  Estimation of ash, moisture content and detection of coal lithofacies from well logs using regression and artificial neural network modelling , 2016 .

[32]  Roohollah Shirani Faradonbeh,et al.  Rock strength assessment based on regression tree technique , 2016, Engineering with Computers.

[33]  R. Littke,et al.  Characterizing coal cleats from optical measurements for CBM evaluation , 2016 .

[34]  Yanyu Zhang,et al.  Productivity Prediction of Coalbed Methane Considering the Permeability Changes in Coal , 2014 .

[35]  Yanbin Yao,et al.  Permeability evolution in fractured coal — Combining triaxial confinement with X-ray computed tomography, acoustic emission and ultrasonic techniques , 2014 .

[36]  Xiao Liang,et al.  Log evaluation of a coalbed methane (CBM) reservoir: a case study in the southern Qinshui basin, China , 2014 .

[37]  M. Riahi,et al.  Permeability prediction and construction of 3D geological model: application of neural networks and stochastic approaches in an Iranian gas reservoir , 2013, Neural Computing and Applications.

[38]  Zhongwei Chen,et al.  Laboratory Study of Gas Permeability and Cleat Compressibility for CBM/ECBM in Chinese Coals , 2012 .

[39]  Satya Harpalani,et al.  Laboratory measurement and modeling of coal permeability with continued methane production: Part 1 – Laboratory results , 2012 .

[40]  L. Connell,et al.  Modelling permeability for coal reservoirs: A review of analytical models and testing data , 2012 .

[41]  Abdulrahman Mohammad Saleh Al-Moqbel,et al.  Carbonate Reservoir Characterization Based on Integration of 3-D Seismic Data and Well Logs Using Conventional and Artificial Intelligence Approaches , 2011 .

[42]  Yanbin Yao,et al.  Evaluation of the reservoir permeability of anthracite coals by geophysical logging data , 2011 .

[43]  C. Özgen Karacan,et al.  Coal mine methane: A review of capture and utilization practices with benefits to mining safety and to greenhouse gas reduction , 2011 .

[44]  Sadegh Karimpouli,et al.  A new approach to improve neural networks' algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (SCMNN) , 2010 .

[45]  D. Tang,et al.  Petrophysical characterization of coals by low-field nuclear magnetic resonance (NMR) , 2010 .

[46]  Luke D. Connell,et al.  Laboratory characterisation of coal reservoir permeability for primary and enhanced coalbed methane recovery , 2010 .

[47]  Ian D. Gates,et al.  Support-Vector Regression for Permeability Prediction in a Heterogeneous Reservoir: A Comparative Study , 2010 .

[48]  Victor Rudolph,et al.  Evaluation of coal structure and permeability with the aid of geophysical logging technology , 2009 .

[49]  Sam Ameri,et al.  Predicting production performance of CBM reservoirs , 2009 .

[50]  Sevket Durucan,et al.  Drawdown Induced Changes in Permeability of Coalbeds: A New Interpretation of the Reservoir Response to Primary Recovery , 2004 .

[51]  Tamás D. Gedeon,et al.  An improved technique in porosity prediction: a neural network approach , 1995, IEEE Trans. Geosci. Remote. Sens..

[52]  Yumao Pang,et al.  Source–reservoir relationships and hydrocarbon charging history in the central uplift of the south Yellow Sea basin (East Asia): Constrained by machine learning procedure and basin modeling , 2021 .

[53]  P. Hagan,et al.  Experimental and analytical study on the mechanical behaviour of cable bolts subjected to axial loading and constant normal stiffness , 2019, International Journal of Rock Mechanics and Mining Sciences.

[54]  Jennifer Market,et al.  ADVANCED PETROPHYSICAL APPLICATIONS FOR THE AUSTRALIAN MINING INDUSTRY , 2019, SPWLA 60th Annual Logging Symposium Transactions.

[55]  Xiaoli Zhao,et al.  Mechanical Properties of Coal Measure Rocks Containing Fluids at Pressure , 2018 .

[56]  Mohammad Jafar Mohammadzadeh,et al.  Evaluating distribution pattern of petrophysical properties and their monitoring under a hybrid intelligent based method in southwest oil field of Iran , 2016, Arabian Journal of Geosciences.

[57]  S. Chalov,et al.  Surface water pathways and fluxes of metals under changing environmental conditions and human interventions in the Selenga River system , 2016, Environmental Earth Sciences.

[58]  P. K. Pal,et al.  Estimation of In-situ Stress and Coal Bed Methane Potential of Coal Seams from Analysis of Well Logs, Ground Mapping and Laboratory Data in Central Part of Jharia Coalfield—An Overview , 2015 .

[59]  E. M. El-M. Shokir,et al.  Permeability estimation from well log responses , 2005 .