Rock Physics Modeling and Iterative Petrophysics Driven Inversion Results: Estimating Subsurface Petrophysical Properties and Characterizing Reservoirs - A Real-Time Case Study of Clastic Gas Field in Pakistan

Abstract Rock physics modeling (RPM) using iterative petrophysics is a vital tool in the oil and gas industry for predicting subsurface properties and characterizing reservoirs. This technique has revolutionized the way in which analysis and interpretation of seismic inversion data has been performed and provided better understanding of the subsurface geology. The main scope of this work was to highlight the importance of using RPM logs data as opposed to using directly recorded and linear regression based corrected logs for seismic inversion input. The approach that was followed for this study started with building a consistent RPM based upon the iterative petrophysics. The results of RPM data were compared with recorded and multi-linear regression (MLR) data-set. The main quality check for the health of the well log data was set to see which data will provide a closer correlation to the original seismic amplitude versus offset (AVO) response. Using well to seismic tie of RPM and recorded well logs data, the well based wavelets were extracted and compared. Moreover, the pre-stack inversion was performed with the optimized parameters. Furthermore, consistent well based probability density functions (PDFs) for two different facies were generated and implemented on the inversion results. Finally, the results were quality assured on the blind wells. It was concluded that, the well based elastic logs response were improved dramatically using RPM and iterative petrophysics approach. The modeled AVO response (intercept and gradient) of the RPM based data was much closer to the original conditioned seismic data as compared to data from Multi-linear regression (MLR) and original recorded logs data. RPM data provided a far better well to seismic tie, an average cross-correlation improvement from 50 to 70%, resulting in a consistent well based wavelet. In addition, this led to improved PDFs and implementing them to the pre-stack inversion results. Furthermore, the correlation of the predicted litho-facies at the blind wells were consistent with the encountered petrophysical properties. This study highlights the importance of RPM and iterative petrophysics as a vital tool for oil and gas industry. Current study not only highlights their importance but also highlights importance of effective reservoir characterization with enhanced seismic data interpretation, robust well-ties and accurate porosity and litho-facies away from wells. These insights not only have a huge impact on reservoir monitoring and management but also crucial for strategic decision in the industry.