A Multidisciplinary Approach to Inversion Feasibility Analysis for an Unconventional Reservoir Characterization
Venkatesh Anantharamu1, Lev Vernik1, Yoryenys Del Moro1, Alfonso Quaglia2 and Eduardo Carrillo2
1Ikon Science, 2Inter Rock
Unconventional oil and gas production has increased dramatically in the last decade, and in the U.S., the Permian Basin is the most prolific of all the basins. The Delaware Basin located in the western part of the Permian Basin has become one of the most active drilling sites with multi-stacked plays. Most of the production comes from the Permian-aged Avalon, Bone Springs and Wolfcamp formations. These formations are comprised of a heterogeneous mixture of organic mudrocks, siliciclastics, and carbonates. Due to the complex nature of these rocks, it is advantageous to understand and extract useful information from available data resources from all disciplines.
A key component of unconventional reservoir development is 3D characterization. A necessary precursor for any seismic inversion work is an inversion feasibility study. We shall demonstrate a best practice inversion feasibility workflow that has several key components: regional geology, petrophysics, rock physics, and geophysical analysis.
The study shows an integrated approach using spatially diverse well data to cover the entire Delaware Basin, focusing on Avalon and Bone Springs formations. The total organic carbon (TOC) which is one of the most important reservoir characterization parameters for unconventionals was calculated. Using the TOC logs, Rock Physics Models (RPMs) were calibrated to understand the link between elastic properties and the corresponding petrophysical properties. The calibrated RPMs were used to replace sections of poor or missing data (including Vs data) with predicted logs. The facies classification was carried with a complete set of logs on petro criteria. Inversion feasibility was performed by classifying elastically separable data referred to as seismic facies. Then, several depth trends and their associated probability density functions (PDFs) were generated per facies. Further testing at seismic frequency was carried out with upscaled log data using Bayesian classification to evaluate potential success for facies classification from the inversion. Finally, multi-scenario isotropic, anisotropic, and varying TOC models were generated to understand the impact on elastic log data and seismic amplitudes.
The results showed the ranking of petrophysical properties that contributes to the changes in elastic properties. A good relationship was established between TOC, vclay, and porosity to elastic logs using both conventional and unconventional RPMs. A class IV AVO response was observed in the Avalon formation. Finally, a depth trend-based 1D Bayesian classification was able to separate high TOC facies from siliciclastic mudrocks and carbonates.
To conclude, an integrated approach involving geology, petrophysics, rock physics, and inversion feasibility study increased our understanding of the basin and set the path for further analysis. The workflow outlined in this study potentially can lead to a 3D inversion analysis, reservoir property estimations from seismic, TOC mapping, and finally for finding sweet spots and better-producing zones in the subsurface.
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