Feasibility of the quantitative time-lapse seismic characterisation of a heterogeneous CO2 injection. Issue 1 (1st December 2019)

• Record Type: Journal Article
• Authors: Isaenkov, Roman; Glubokovskikh, Stanislav; Gurevich, Boris
• Is Part Of: ASEG Extended Abstracts (Online). Volume 2019:Issue 1(2019)
• Page Start: 1
• Page End: 6
• Publication Date: 2019-12-01
• Subjects: Time-lapse quantitative interpretation -- inversion robustness -- CO2 monitoring; Prospecting -- Geophysical methods -- Periodicals; Prospecting -- Geophysical methods; Periodicals
• DOI: 10.1080/22020586.2019.12073195
• Languages: English
• ISSNs: 2202-0586

Abstract

Summary: CO2 injection into clastic brine-saturated reservoirs leads to a detectable reduction of the elastic moduli of the reservoir rocks. However, quantitative interpretation of the time-lapse seismic anomalies obtained for CO2 storage projects is challenging, because the injected gas can form thin plumes with low saturated narrow streaks. That is why, the time-lapse interpretation is often limited to qualitative detection of CO2 leakages. This paper is concerned with two questions: what CO2 plume parameters can be estimated from realistic bandlimited seismic data and how noise in the data affects the quality of the estimates. To this end we perform stochastic rock physics simulations of the injection reservoirs. The reservoir realisations differ in thickness, net-to-gross, contrast between the permeable and impermeable sediments and vertical distribution of the CO2 . The rock physics analysis suggests that maximum and integral value of the relative acoustic impedance changes are most sensitive to the parameters of the plume. The remainder of the analysis of the noise focuses on the survey repeatability and errors in the wavelet estimation. We show that both of the noise types strongly affect the accuracy of the timelapse inversion. The proposed workflow provided rigorous means to estimate limitations of the time-lapse seismic inversion for CO2 storage projects. It may be easily adapted to real projects and guide the monitoring system design or optimisation of data analysis workflows.