Publication Name: Australasian Exploration Geoscience Conference 2019
Authors: Mohammad Risyad, Muhammad Fahmi Yahya Faizan
Date Published: September 2019
Number of Pages: 4
Abstract:
Gumai Formation is often regarded as merely regional seal in South Sumatra Basin as it is dominated by shale, claystone and siltstone. However, a variety of geological and geophysical approaches have been conducted in Northeast Betara (NEB) field to boost the confidence of hydrocarbon bearing reservoir distribution in the untapped formation. A proprietary seismic attribute screening approach is developed for this study in order to optimise the match between modeled and actual porosity logs. Multi-attribute analysis, neural properties, density, gamma rays, and porosity are utilized to discriminate sand, thin sand and shale. Neural network analysis is done to improve the pseudo volume results including pseudo-gamma-ray and pseudo porosity which are then used for geological interpretation. Validation is done by examining existing mud log data on several wells. Good result from multi-attribute and neural network analysis is exploited to map the distribution of lithology and porosity. The result is then overlaid on depth structure map and shows a good match between predicted porosity and actual porosity of the well. Training results and validation values also shows good correlation and validation. Correlation value of 0.6005 and 0.8060 are achieved by multi-attribute linear regression and Probabilistic Neural Network (PNN), respectively. Northwestern part of the field is considered having promising reservoir distribution and porosity. Average density of 2.0 to 2.25 g/cc and average porosity of 14 to 16% are inferred to be present in the reservoir based on this study.