Nine-component seismic amplitude inversion: a case study in the Eagle Ford shale

Abstract

Over the last decade, unconventional hydrocarbon resources have led the United States to a spot atop the list of largest oil producers in the world. During this time, major improvements in both technology and the general knowledge of shale reservoirs have driven down production costs while improving oil and gas recovery rates. The Reservoir Characterization Project (RCP) Phase XVII work seeks to further these advancements through a study of the value of time-lapse datasets and advanced reservoir characterization methods in the Eagle Ford Shale of South Texas, one of the most prolific unconventional plays in the world. RCP's project involves the analysis of various geophysical, geological, and engineering datasets sampling an approximately 50-square-mile study area of the Eagle Ford play. The available geophysical data includes time-lapse, multicomponent seismic data and vertical seismic profiles, along with microseismic event monitoring during hydraulic fracturing. The work presented in this thesis seeks to study the value of the nine-component surface seismic data for seismic amplitude variation with offset/angle (AVO/AVA) inversion and reservoir characterization. The vast majority of seismic datasets utilized in the oil and gas industry are single-component P-wave (PP) surveys. Nine-component surveys, which include converted wave (PS) data and pure-shear (SS) data, are rarely acquired due to the added costs and complexity of acquisition, processing, and interpretation. However, a better understanding of the additional information available in these datasets could be useful for determining whether or not these acquisitions are worthwhile. My analysis begins with their theoretical value for seismic inversion, which aims to recover P-impedance, S-impedance, and density models from recorded seismic amplitudes. Based on linearized approximations of the Zoeppritz equations, which describe the AVA of each data type, I determine that the PP data alone can provide accurate estimates of P-impedance; however, S-impedance and density are poorly constrained. The AVA equations suggest that PS data should improve estimates of S-impedance, but that SS data are likely necessary to obtain the critically important density term. These hypotheses are tested and confirmed through inversions of synthetic seismograms modeled from well log values, but with varying elastic parameters within the reservoir. The synthetic data are also used for comparison and quality control of the field data. After conditioning the field data, most notably through the removal of unrealistic lateral amplitude variations, four inversion methods are applied using a commercial software package: poststack PP, poststack SS (TT component), prestack PP, and joint prestack PP-PS. Inversion parameters are extensively tested to optimize results, and their effects are discussed along with some of the potential drawbacks of the methods applied. Comparing the inversion outputs shows that the joint inversion method produces the best results to utilize for further analysis and interpretation. Next, from the impedance estimates, elastic properties are calculated and analyzed. The results indicate that the seismic data are controlled mostly by matrix properties and see little influence of pore fluids. Finally, elastic property maps are compared to microseismic event locations and production. These comparisons suggest that the inversion results can be used to predict hydraulic fracture behavior and locate `sweet spots,' or zones of greater hydrocarbon production potential in the Eagle Ford, which could result in more efficient field development. To further study the potential benefits of including pure-shear wave modes in an AVA inversion workflow in ways that could not be accomplished through commercially available software packages, a code is developed for independent or joint inversion of PP, PS, and SS wave modes. It is applied to the Eagle Ford field data, and a method of anomaly detection through analysis of the inversion prediction error is demonstrated. Though the project data is suboptimal for testing the SS modes, findings indicate that the addition of S-wave data slightly improves the density estimates at the cost of decreased resolution due to the lower temporal frequency. In a location where accurate density values are required for reservoir characterization, this could potentially justify the added cost of S-wave acquisition.