Seismic inversion for CO2 volume monitoring and comprehensive evaluation of pore fluid properties: a case study

Abstract

A comprehensive evaluation of pore fluid properties, involves detailed analysis of various characteristics and behaviours relevant to its storage and management in subsurface reservoirs. The assessment includes variations in CO₂ density, bulk modulus, temperature, pressure, velocities, and interactions with reservoir fluids and rocks. The seismic response of porous rocks hosting pore fluids is influenced by these physical properties, crucial for understanding CO₂ behaviour in carbon capture and storage (CCS) initiatives. In this study, we first utilize the Batzle–Wang model to predict the behavior of common pore fluids, such as brine and gas, which are key to understanding the seismic response of the reservoir. This initial analysis provides the foundation for the next step: monitoring the behavior of injected CO₂ at the Sleipner field in Norway. To accurately track changes in the subsurface related to CO₂ injection, we employ seismic inversion using the simulated annealing (SA) technique. This global optimization approach offers significant advantages over traditional local optimization methods, yielding more reliable and near-optimal solutions for estimating the changes in acoustic impedance caused by CO₂ saturation. The study examines five sets of time-lapse seismic data from the Sleipner field, from 1994 to 2006. Acoustic impedances are computed for the pre-injection period and post-injection years, revealing a low impedance zone spanning from 2000 to 2500 m/s/g/cc. This inversion result predicts the injected CO₂ volume by calculating the CO₂ area from the uppermost time slice of different years, based on acoustic impedance seismic sections. To address inherent non-uniqueness in time-lapse analysis, the estimated volume is compared with the original production volume. The results indicate that the estimated volume closely resembles the original injected volume for different time-lapse seismic data.