#TITLE_ALTERNATIVE#

• Main Author: PAMILWA CITAJAYA (NIM 12304009), NOVRIANTO
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/8845
• Institution: Institut Teknologi Bandung

This thesis will discuss about multiattribute evaluation from seismic attribute and log property. The result will be compared with conventional seismic inversion method to modeling channel delineation and porosity prediction. Blackfoot training data set from Hampson Russell Software Service used for this study. Multiatrribute method used to predict porosity logs. The objective is to derive a multiattribute transform, which is a linear or nonlinear transform between a subset of the attributes and the target log values then we compare the result with conventional seismic inversion. The selected subset is determined by a process of forward stepwise regression, which derives increasingly larger subsets of attributes. An extension of conventional crossplotting involves the use of a convolutional operator to resolve frequency differences between the target logs and the seismic data. In the linear mode, the transform consists of a series of weights derived by least squares minimization. In the nonlinear mode, a neural network is trained, using the selected attributes as inputs. Three types of neural networks have been evaluated: the multilayer feedforward network (MLFN), probabilistic neural network (PNN) and Radial basis function (RBF). Because of its higher correlation, the PNN appears to be the network of choice. To estimate the reliability of the derived multiattribute transform, crossvalidation is used. In this process, each well is systematically removed from the training set, and the transform is rederived from the remaining wells. The prediction error for the hidden well is then calculated. The validation error, which is the average error for all hidden wells, is used as a measure of the likely prediction error when the transform is applied to the seismic volume. Model based method applied for conventional seismic inversion. Multi-attribute Neural Network result give distribution of porosity prediction on top channel. Acoustic impedance result give the model of channel distribution.