Understanding the behavior of elastic properties to get the best discrimination on lithology and fluid prediction is the most important knowledge to minimize the pitfalls in the interpretation. Rock Physics Template (RPT) which is geologically constrained rock physics model can serve the information about the lithology and fluid separation; hence it can guide the interpreter to reduce the pitfalls in prediction and misinterpretation. The objective of this paper is to show the performance of new RPT in discriminating the lithology and pore fluids. The new RPT has been developed based on SQp and SQs attributes. These attribute were derived from attenuation–rock physics approximation. The new RPT was tested on soft sediment and cemented model and it was also applied on the real dataset from a field in Malay Basin. The performance of new RPT on lithology and pore fluid separation was compared with the existing RPT generally used in the industry. The results show that the new RPT which is based on SQp and SQs attributes give better separation in lithology and hydrocarbon prediction. The lithology effect was separated in the SQp domain and pore fluid was separated in the SQs domain which are both domain are orthogonal to each other. The implementation of this RPT on the real data shows that the gas sand separated clearly from wet sand and coal layer. The pitfalls due to the coal masking can be solved by implementing these attributes. The lithology and pore fluid were separated optimally. This new RPT provides an optimum separation on lithology and pore fluid effect. Pitfalls due to Coal mask can be separated properly; hence this new technology potentially can be used to reduce the pitfalls and uncertainty in hydrocarbon prediction. Further improvement is still required on the extended applications of this new technology on other cases such as low resistivity low contrast pay prediction.

Amplitude versus offset (AVO) analysis integration to well log analysis is considered one of the advanced techniques to improve the understanding of facies and fluid analysis. Generating AVO attributes are one solution to give an accurate result in facies and fluid characterization. This study is focused on a field of Northern Malay basin, which is associated with a fluvial-deltaic environment, where this system has high heterogeneity, whether it is vertically or horizontally. This research is aimed to demonstrate an application of the scale of quality factor of P-wave (SQp) and the scale of quality factor of S-wave (SQs) AVO attributes for facies and fluid types separation in field scale. These methods are supposed to be more sensitive to predict the hydrocarbons and give less ambiguity. SQp and SQs are the new AVO attributes, which derived from AVO analysis and created according to the intercept product (A) and gradient (B). These new attributes have also been compared to the common method, which is the Scaled Poisson’s Ratio attribute. By comparing with the Scaled Poisson’s Ratio attribute, SQp and SQs attributes are more accurate in determining facies and hydrocarbon. SQp and SQs AVO attributes are integrated with well log data and considered as the best technique to determine facies and fluid distribution. They are interpreted by using angle-stack seismic data based on amplitude contrast on interfaces. Well log data, e.g., density and sonic logs, are used to generate synthetic seismogram and well tie requirements. The volume of shale, volume of coal, porosity, and water saturation logs are used to identify facies and fluid in well log scale. This analysis includes AVO gradient analysis and AVO cross plot to identify the fluid class. Gassmann’s fluid substitution modeling is also generated in the well logs and AVO synthetics for in situ, pure brine, and pure gas cases. The application of the SQp and SQs attributes successfully interpreted facies and fluids distributions in the Northern Malay Basin.

Reservoir characterization is one of the most crucial processes in the exploration of hydrocarbons. Thus, having sufficient information such as the lithology and fluid distribution of a reservoir is vital. With the application of Simultaneous Seismic Inversion, we will be able to improve the prediction of the lithology and fluid distribution of the prospect area, as it generates information such as Density (ρ), Lambda-rho (λρ) (Incompressibility), and Mu-rho (µρ) (Rigidity). Real data from the Malay basin were used to show the effectiveness of this method in enhancing the resolution and characterizing the gas sand layer of the prospect.