Abstract
It has done a study of porosity prediction by using neural network. The study uses 2D seismic data post-stack time migration (PSTM) and 2 well data at field “T”. The objective is determining distribution of porosity. Porosity in carbonate reservoir is actually heterogeneous, complex and random. To face the complexity the neural network method has been implemented. The neural network algorithm uses probabilistic neural network based on best seismic attributes. It has been selected by using multi-attribute method with has high correlation. The best attributes which have been selected are amplitude envelope, average frequency, amplitude weighted phase, integrated absolute amplitude, acoustic impedance, and dominant frequency. The attribute is used as input to probabilistic neural network method process. The result porosity prediction based on probabilistic neural network use non-linear equation obtained high correlation coefficient 0.86 and approach actual log. The result has a better correlation than using multi-attribute method with correlation 0.58. The value of distribution porosity is 0.05–0.3 and it indicates the heterogeneous porosity distribution generally from the bottom to up are decreasing value.
Highlights
Carbonate reservoir is the main target for hydrocarbon production because carbonate reservoirs contain almost 60% of the world's total hydrocarbon reserves and are estimated to have 50% of total hydrocarbon production [1]
Hydrocarbon production can be increased if the permeability value of the reservoir is predictable, where permeability has a strong correlation with the porosity of the rock itself [2]
The results obtained through Probabilistic neural network (PNN) analysis will form a nonlinear transformation between the target log and seismic attributes in the cross plot diagram
Summary
Carbonate reservoir is the main target for hydrocarbon production because carbonate reservoirs contain almost 60% of the world's total hydrocarbon reserves and are estimated to have 50% of total hydrocarbon production [1]. Porosity in the carbonate reservoir has heterogeneous and complex properties. By combining several attributes or implementing multiattributes can increase accuracy in predicting the reservoir property. The neural network method is a tool used to carry out an analysis that makse a correlation between seismic data and well data. The method can be used to predict physical property such as porosity. The best parameters that can be used as input from the neural network method are multiattributes from the integration of well data and seismic data. The study location at “T” field in North Sumatera Basin It is back-arc basin with Tertiary sediment deposited above pra-Tertiary. The focus formation is Peutu Formation (Figure 1) This formation is deposited during the Early Miocene – Middle Miocene. This formation has sandstone at eastern region which known as Belumai Formation [4]
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