Lithology and fluid discrimination using combined seismic attributes with the constraint of rock physics: a case study from W field, South Sumatra Basin

Abstract

Reservoir characterization and fluid discrimination based on seismic reflection amplitude play important roles in seismic exploration industry. Stable fluid-sensitive attributes from seismic data can help reduce uncertainties of hydrocarbon prediction in interwell locations and increase the reliability of drilling plans. In this study, in combination with seismic AVO inversion impedance and the rock physics template analysis, we proposed a new attribute (DI) to discriminate the hydrocarbon-associated anomalies and predict the reservoir parameters including porosity and water saturation quantitatively in Upper Gumai formation of W field, South Sumatra Basin. The new attribute (DI) is constructed using a set of combined impedances derived from prestack seismic inversion with the constraint of rock physics. Numerical modelling based on patchy-saturated model was implemented to test the sensitivity and stability of the new attribute and results showed that the DI attribute can predict the existence of hydrocarbon-filled sands with less ambiguity. With known well log data in W field, a feasibility study including cross-plotting and histogram methods was carried out and concluded that the DI attribute contributes higher resolution in distinguishing the hydrocarbon-filled sands from the background trend. In the process of quantitative interpretation, the DI attribute shows a good regression relationship with porosity and water saturation properties within the hydrocarbon sands with the correlation coefficients reaching 92% and 85%, respectively. By using seismic AVO inversion impedance, combining the application of the DI attribute and Vp/Vs ratio for lithology and fluid contents discrimination was conducted to improve the reservoir prediction accuracy in the field. Application results show that low values of the DI attribute (<18,000 m/s.g/cc) was indicative of hydrocarbon-filled sands effectively while Vp/Vs ratio presented a higher resolution in separating wet sand, dry-sand from shale. By using the linear regression relationship derived from cross-plotting analysis with well data, we calculated the volumes of porosity and water saturation from DI attribute and successfully screened out the hydrocarbon accumulation distribution, which reveals the potential zones of exploration interest in South Sumatra Basin.