A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Offshore Technology and Management, School of Engineering and Technology

Thesis (M. Eng) - Asian Institute of Technology, 2011

The study area is located in North Pattani Basin, Gulf of Thailand. Although the major wells in the interested area did have logging and coring, not all depth intervals could be cored and not all logging curves were measured. Some logs were even missing. Assessment or prediction of petrophysical parameters, including porosity in uncored intervals and at other wells locations may have an impo1tant role in the reserve estimation. To help so lving this issue, Back-propagation Artificial Neural Network (ANN) with early stopping method was employed in this study to help estimate porosity from well logging data or seismic attributes. Well logging data and core measurement of two wells (wells A and B) were studied. Quicklook interpretation was conducted in order to identify reservoir, estimate shale volume, porosity, permeability, and water saturation. The Back-propagation ANN with early stopping method was studied and applied for porosity prediction by using Matlab software. The data sets were separated into three subsets, i.e. training, validating, and testing. The input data were from well logging data or seismic attribute data. The output data used for training were from core porosity. The ANN model with least performance error was applied for porosity prediction in uncored well and other wells locations. The first group of ANN analyses used the well logging data as input layer with the standard set consisting of GR, LLD, RHOB, NPHI, and DT. The desired output data were core porosity measurements. The tangent hyperbolic function was employed as transfer function for the hidden layer, while the linear function was used as transfer function for the output layer. A total of 56 ANN analyses were performed to predict porosity for uncored intervals. It was found that the ANN-based porosity of the best model matched very well with core porosity, MSE was 10.09. Within this first group of ANN analyses, it is wo1th mentioning that a number of analyses were done for the case the sonic log (DT) was missing. To solve this problem of missing DT, the synthetic DT curves were reconstructed using both methods of Log Response Equation (LRE) and ANN. As a good finding of this study, the DT curves were well reconstructed, MSE was 80.41. Again, based on the reconstructed DT, a complete input data layer including GR, LLD, RHOB, NPHI, and DT, was obtained for the ANN analysis, MSE was 9.22. The second group of ANN analyse was conducted with the major change in the input layer, with the input data being se ismic attributes (dip, azimuth, instantaneous phase, and relative acoustic impedance). These seismic attributes were extracted from seismic cube by Petrel software. A total of 6 ANN analyses were run to study the seismic attribute sensitivity. The matching of ANN-based porosity was not that good as those obtained in the first group. The best results obtained were obtained for the case using all seismic attributes as input data, MSE was 44.30. However, the result was encouraging in the sense that if seismic attributes could be successfully used as the input data in an ANN analysis one has good opportunity to make a satisfactory 3D petrophysical modelling (or 3D distribution of porosity in this case) for the concerned oil field.