عنوان مقاله [English]
Brittleness is one of the most important properties of the rock. Brittleness is a function of strength and indicates rock strength to deformation in the elastic modules. However, there is no direct and standard method for brittleness measurement but it can be done indirectly by using rock properties such as different ratios of compressive strength and tensile strength of rock to determine the concept of brittleness. The purpose of this study is to investigate the concepts of brittleness which are presented by the researchers and to use seismic inversion, multi-attribute analysis and neural network in the Whicher-Range field in Perth, Western Australia to estimate brittleness. The Perth sedimentary basin stretches about 100,000 square kilometers in the north-south direction of the western Australian margin. About half of this sedimentary basin is located 1 km deep in the sea. Whicher-Range gas field is 22 km south of Baselton and 200 km south of Perth. Two wells and one seismic section of Whicher-Range field are selected in this research. The lowest brittleness indexes in the first and second wells drilled 1 km apart, are 1.69 and 1.67 MPa using criteria. The highest values of the brittleness are 39.78 and 48.15 MPa, respectively, which are difficult for drilling. The starting point of the inversion process is to have post-stack seismic data, velocity model, well logs, and seismic horizons. The product of the density log and sonic velocity is equal to the impedance. Then the current impedance is converted from depth to time using the appropriate depth to time relation. As a result, the convolution of a suitable wavelet and reflectivity over time will produce the synthetic seismic trace. Adaptation rate between synthetic seismic trace and composite field seismic trace yields an acceptable result. Next, the initial modeling is performed using wavelet, geological model, and a model-based algorithm. Next, the acoustic impedance of the inversion process along with other attributes is used to construct the optimal combination of attributes to estimate the brittleness index. First, an attribute is selected that has the highest correlation with the target log and the least estimation error in the training step. Then the second attribute, which makes the best combination with the first attribute, has the lowest estimation error. Then each attribute step is added to its previous step combination until the resulting combination results in the lowest estimation error. The results based on this method are obtained by increasing the number of attributes and decreasing the estimation error, while the error in the validation stage until the optimal attribute combination, is ascending. In the next step, three types of neural network algorithm including probabilistic method, multi-layer feed forward and radial basis function are used to estimate the target parameter, with optimal combination of available attributes and the use of neural network algorithm training from the optimal attributes using the Hampson-Russell software. In the last step, multi-attribute analysis is compared with three neural network algorithms. The results indicate a higher correlation coefficient for probabilistic neural network than that of multi-attribute analysis for determination of the brittleness index.