Fractures distribution modeling using fractal and multi-fractal–neural network analysis in Tabnak hydrocarbon field, Fars, Iran

Abstract

Modeling of fractures distribution in naturally fractured hydrocarbon reservoirs is a complex process that contains large amount of uncertainty. High gas production from the Dehram group of Tabnak hydrocarbon reservoir in Fars province indicates the presence of natural fractures. This study presents a novel methodology that integrates various features of geological, statistical and artificial intelligence techniques in a nested loop to characterize field fractures and then to model them. Their characterization is, to some extent, technique dependent. Secondary properties such as fracture density, fractal dimension and fractal spectrum are there defined for better description of fractures’ spatial distribution. Irregular geometry laws showed that the connectivity of fractured media depends on power-law exponent and some fracture characteristics. A neural network is incorporated in the proposed methodology to determine these relationships, by processing field data available from the six outcrops with similar lithology, image logs and core analyses. The value of 1/82 was calculated for fractal dimension of surface fractures. The fracture density in the range of 0.2 to 1.4, fractal dimension and fractal spectrum (D(q = 0)) of fractures in the range of 1.0 to 1.6 and 1.8 to 3.0 respectively were calculated for the 10-m intervals within the well and extended with fractal–neural network algorithm. Determination of fractal dimension and fractal spectrum from image logs and dual application of neural network and fractal geometry in fracture modeling are innovative in this study. Finally fracture distribution models estimated are of great interest with gas production rates of the Tabnak hydrocarbon reservoir and have more reality results compared with DFN algorithm models using Open Flow software.