1. Introduction

Fractures are important for improving permeability in unconventional reservoirs including shale gas, coal bed methane, tight gas sand, and fractured basement reservoir. In these reservoirs, hydrofracturing is commonly practiced to stimulate fractures and to significantly improve oil/gas flow. The target of hydrofracturing technique is focused on the brittle area (the area with a high tendency to break), which is expected to be able to generate more fractures. To support this objective, understanding of mechanical properties (such as brittleness) of the rock is very important. Estimation of brittleness from seismic data is an important task for better well hydrofracturing and drilling placement.

The success of hydrofracturing depends on the geomechanical brittleness of the formation; brittle rocks tend to generate more fractures compared to ductile rocks. Brittleness is the measurement of stored energy before failure and is a function of

rock strength, lithology, texture, effective stress, temperature, fluid type, digenesis, and TOC [1].

The brittleness is determined by a number of mineral contents of rock. The most brittle minerals like quartz and the less percentage of ductile minerals like clay mineral in the rock tend to make rock more brittle. Rock physics shows that the mineral content determine the elastic properties of rock. Hence, it is reasonable to estimate brittleness using elastic properties. However, the selection of which elastic properties can be used to indicate brittleness is the main task in seismic quantitative interpretation. Estimation of brittleness index which is based on calculation of mineral content and brittleness average which is based on elastic properties and can be derived from seismic data has been successfully applied in the shale gas field [2].

During unconventional reservoir exploration and development, not only how to find out the most brittle area where expected fracture can be generated during horizontal well drilling and hydrofracturing but also how to find the sweet spot where the hydrocarbon is accumulated largely and also how to estimate the capacity and reserve of unconventional reservoir are very challenging. As in case of fractured basement reservoir, the problem on how to find the possible location of generated secondary porosity and permeability and to find where the hydrocarbon accumulation is and what type of hydrocarbon is there is still difficult to be solved and needs advance tool to make more accurate and significant during quantification.

The main objective of this paper is to introduce a new workflow for unconventional reservoir characterization by introducing new attributes: scaled inverse quality factor of P-wave (SQp) and scaled inverse quality factor of S-wave (SQs). These attributes are derived from the attenuation concept through rock physics approximation, which can be implemented on the result of seismic inversion. The existing method, brittleness average, is commonly used to indicate the brittle rock calculated from elastic properties; Poisson's ratio and Young's modulus will be discussed and compared with new attributes to indicate the fracture density. A well data example from fractured basement reservoir in the Malaysian Basin is used to test the performance of these methods to indicate fracture density and hydrocarbon column which also will be discussed.
