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

Adil Azeem, Ali Hameed, M. M. Ali Virk, F. R. Awan, Akash Mathur, M. F. Abid
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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.
岩石物理建模和迭代岩石物理驱动反演结果:估算地下岩石物理特性并确定储层特征--巴基斯坦碎屑岩气田实时案例研究
利用迭代岩石物理学建立岩石物理模型(RPM)是油气行业预测地下属性和描述储层特征的重要工具。这项技术彻底改变了地震反演数据的分析和解释方式,使人们能够更好地了解地下地质情况。这项工作的主要范围是强调使用 RPM 测井数据的重要性,而不是使用直接记录和基于线性回归的校正测井数据作为地震反演输入。本研究采用的方法首先是根据迭代岩石物理学建立一致的 RPM。将 RPM 数据结果与记录和多线性回归 (MLR) 数据集进行比较。对测井数据健康状况的主要质量检查是看哪种数据与原始地震振幅与偏移(AVO)响应的相关性更接近。利用 RPM 和记录的测井数据的井与地震配合,提取并比较了基于井的小波。此外,使用优化参数进行了叠前反演。此外,还生成了两个不同层位的一致的井基概率密度函数(PDF),并在反演结果中实施。最后,对盲井的结果进行了质量保证。结论是,使用 RPM 和迭代岩石学方法,基于油井的弹性测井响应得到了显著改善。与多线性回归(MLR)数据和原始记录的测井数据相比,基于 RPM 数据的建模 AVO 响应(截距和梯度)更接近原始条件地震数据。RPM 数据提供了更好的油井与地震联系,平均交叉相关性从 50% 提高到 70%,从而产生了一致的基于油井的小波。此外,这还改进了 PDF,并将其应用到叠前反演结果中。此外,盲井预测岩性的相关性与遇到的岩石物理特性一致。这项研究强调了 RPM 和迭代岩石物理学作为油气行业重要工具的重要性。当前的研究不仅强调了它们的重要性,还强调了通过加强地震数据解释、稳固的井系以及准确的孔隙度和岩性来有效描述储层特征的重要性。这些见解不仅对储层监测和管理有巨大影响,而且对该行业的战略决策也至关重要。
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