Characterization of Fractured Basement Using Statistical and Fractal Methods
Authors: Sung W.M. , Park J.H. 
Published: December 2007

This study presents the characterization of fractured basement by using statistical and fractal methods by using outcrop data, seismic fault data, and FMI log data. In statistical method, fracture intensity and length have been calculated from various types of outcrops by window sampling survey. The optimum distribution function of fracture length for various types of outcrops has been identified with the use of discriminant equation derived from Crofton's theory. The Fisher distribution constant, representing fracture orientation has been obtained by FMI data of several wells. With the afore-mentioned statistical value and distribution function, three-dimensional fracture system has been generated. The result shows that there is no distinct in orientation of the fractures, and excellently matched with the input data. In the case of fractal method, fractal dimension of the length and strike of seismic faults in areal distribution was calculated, and the great value of fractal dimension shows fracture system has intensive fractal characteristics. Meanwhile, vertical distribution and dip angle of fracture have been evaluated from FMI data. Resulting three-dimensional fracture system presents that the major strike and distribution are well mached with those of input data, and its average fracture length is greater than that calculated with the statistical method.

Introduction

A large proportion of the world's proven oil reserves have been found in reservoir rock that is naturally fractured. In a recent book1 gives a list of some 370 fields where natural fractures are important for production and a significant proportion are in basement rocks.

The occurrence of naturally fractured basement reservoirs has been known within the hydrocarbon industry for many years, but because they have been generally regarded as non-productive, so they have failed to draw the attention of the exploration. Yet, they are commonly distributed in various petroliferous regions throughout the world. However commercial naturally fractured basement oil deposits have been found by accident, while looking for other types of reservoir, there are some suggestions that basement reservoir oil accumulations are not freaks to be found solely by chance but are normal concentrations of hydrocarbons obeying the rules of origin, migration and entrapment.

Most basement rocks are hard and brittle with very low matrix pososity and permeability, consequently in this type reservoir, fluid flow mainly depends on the secondary porosity. Secondary porosity may be divided into two kinds by origin; tectonic porosity (joints, faults, and fracture, etc.) and dissolution porosity (weathered zone, etc.). So, a greater understanding of the fracture distribution and connectivity within basement reservoir may prove to be the key tool for improved production management of this type of reservoir.

To do this work, not only dynamic data analysis of welltest, being able to provide the information for large area, but also various kinds of static data analysis, which can characterize each fracture properties have been applied.

However in some cases, where large fracture system controls the fluid flow of fractured reservoir, we can not be solely dependent on average properties derived from welltest analysis. In this occasion more reliable characterization can be possible if static analysis as well as welltest analysis simultaneously.

Static data are core analysis data acquired while drilling, locational and directional data of discontinuities, various log data, seismic data, and outcrop data found nearby the reservoir. Now then one can evaluate fracture properties composed of fracture intensity, location, length, and orientation which can be expressed as dip and dip angle, by applying the static data either to statistical method or fractal method.