Improving permeability estimation of carbonate rocks using extracted pore network parameters: a gas field case study

Acta Geophysica

article

Jamshidi Gohari, Mohammad Saleh¹  Emami Niri, Mohammad¹  Ghiasi-Freez, Javad² 

[1]Institute of Petroleum Engineering, College of Engineering, University of Tehran

[2]Faculty of Mining, Shahrood University of Technology

关键词: Pore network parameters;    Microscopic image analysis;    Permeability;    Carbonate rocks;    Artificial neural network;   

DOI  :  10.1007/s11600-021-00563-z

学科分类：地球科学(综合)

来源: Polska Akademia Nauk * Instytut Geofizyki

【 摘 要 】

Despite numerous studies carried out on permeability estimation from either 2D/3D images or models, a precise evaluation of the permeability for carbonate rocks is still a challenging issue. In this study, the capability and advantages of pore network parameters extracted from 2D thin-section images as inputs of intelligent methods for permeability estimation of carbonate rocks are explored. Pore network extraction in image processing is an effective approach for microstructure analysis. A physically practical pore network is not just a portrayal of the pore space in the context of both morphology and topology, but also a valuable instrument for predicting transport properties precisely. In the current research, a comprehensive workflow was first presented to extract the pore network parameters from a set of core thin-section microscopic images from the carbonate reservoir rock of the South Pars gas field located in the southern borders of Iran. Subsequently, an artificial neural network (ANN) model was designed to predict the permeability of the considered samples using the extracted pore network parameters. To highlight the efficiency of the proposed approach, the second ANN model was implemented to estimate the permeability of the samples using the conventional well log data. The quantitative comparison of the obtained results using both ANN-based models reveals a significant enhancement in the predicted permeability through the extracted pore network parameters.

【 授权许可】

Unknown