Discrete fracture network modelling in a naturally fractured carbonate reservoir in the Jingbei Oilfield, China

Fang, Junling, Zhou, Fengde and Tang, Zhonghua (2017) Discrete fracture network modelling in a naturally fractured carbonate reservoir in the Jingbei Oilfield, China. Energies, 10 2: . doi:10.3390/en10020183


Author Fang, Junling
Zhou, Fengde
Tang, Zhonghua
Title Discrete fracture network modelling in a naturally fractured carbonate reservoir in the Jingbei Oilfield, China
Journal name Energies   Check publisher's open access policy
ISSN 1996-1073
Publication date 2017-02-01
Year available 2017
Sub-type Article (original research)
DOI 10.3390/en10020183
Open Access Status DOI
Volume 10
Issue 2
Total pages 19
Place of publication Basel, Switzerland
Publisher MDPI AG
Collection year 2018
Language eng
Abstract This paper presents an integrated approach of discrete fracture network modelling for a naturally fractured buried-hill carbonate reservoir in the Jingbei Oilfield by using a 3D seismic survey, conventional well logs, and core data. The ant tracking attribute, extracted from 3D seismic data, is used to detect the faults and large-scale fractures. Fracture density and dip angle are evaluated by observing drilling cores of seven wells. The fracture density distribution in spatiality was predicted in four steps; firstly, the ant tracking attribute was extracted as a geophysical log; then an artificial neural network model was built by relating the fracture density with logs, e.g., acoustic, gamma ray, compensated neutron, density, and ant tracking; then 3D distribution models of acoustic, gamma ray, compensated neutron and density were generated by using a Gaussian random function simulation; and, finally, the fracture density distribution in 3D was predicted by using the generated artificial neural network model. Then, different methods were used to build the discrete fracture network model for different types of fractures of which large-scale fractures were modelled deterministically and small-scale fractures were modelled stochastically. The results show that the workflow presented in this study is effective for building discrete fracture network models for naturally fractured reservoirs.
Keyword Discrete fracture network model
Ant track
Artificial neural network
Buried-hill reservoir
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
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