Abstract
The shallow gas zone in the Pantai Pakam Timur (PPT) field, located in Northern Sumatra, Indonesia, has recently become an important target for development. However, only two wells were drilled in peripheral part of the field. In this situation the method of Geostatistics is hardly applied because of less control points, but there is a new suitable method to estimate reservoir properties under the condition of such few control points (GDI: Geology Driven Integration Tool). To compensate few controls, GDI creates pseudo-wells by Monte Carlo Simulation method with regional geological constraints in its regulation, and generates theoretical seismic traces from them. Then the suitable seismic attributes are selected after checking the proportionality with the given reservoir property. Finally the artificial neural network (ANN) is applied to detect the weighting factors, which relate the selected seismic attributes to the given physical reservoir properties. We apply this method to the 2-D seismic records in the PPT field to extract successfully the distribution of porosity and thickness of the gas sandstone reservoir. The most prospected area is figured out in the southern part of the field, where the net thickness of gas zone is estimated to increase 27 meters with fairly higher porosity of 28%, which can be fairly confirmed by the well proposed and drilled by this study. Once getting the distribution, it is easier to calculate the total rock volume of the target reservoir under non-homogenized situation, and hence to progress on estimating more precise volume of reserves in place. Thus this method has an advantage in estimating reservoir characters from a few well data in the early development stage, or even in the late exploration stage. It is certainly important for asset managing that new idea should save the cost even in the stage of exploration.
