1887
Volume 73, Issue 8
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

The objective of this research is to estimate the elastic properties of the CO plume in the Sleipner field and perform a comparative analysis of model‐based inversion (MBI) and sparse layer reflectivity (SLR) inversion techniques. MBI is relatively old method, whereas SLR is relatively new method for seismic inversion. Model‐based seismic inversion is a well‐established deterministic inversion technique that iteratively minimizes the misfit between observed and modelled seismic data. In contrast, SLR inversion is designed to identify and analyse the reflectivity of thin subsurface layers by emphasizing sparsity in the reflectivity sequence. This study utilizes a set of time‐lapse seismic angle stack data from the Sleipner field, comprising a 1994 pre‐injection baseline and a 1999 post‐injection monitor survey, following the injection of 2.35 million tons of CO. These angle stacks were used to generate P‐wave and S‐wave reflectivity using the two‐term Fatti amplitude versus offset (AVO) equation, which was then further utilized in the inversion process to estimate the elastic parameters. Acoustic and shear impedance (SI) were derived using MBI and SLR to evaluate their strengths, limitations, computational efficiency and adaptability to geological changes. In the CO‐injected zone, acoustic impedance values were observed between 2000 and 2400 m/s g/cm3, whereas SI values ranged from 100 to 400 m/s g/cm3. Our findings suggest that overall, MBI produces sharper and more reliable imaging across the entire seismic section. For P‐impedance, MBI yielded correlation values of 0.980 with an error of 0.137 in 1994 and 0.989 with an error of 0.141 in 1999 datasets, whereas SLR showed higher correlation at the well location 0.997 with an error of 0.073 in 1994 and 0.998 with an error of 0.061 in 1999. For S‐impedance, MBI achieved correlation values of 0.860 with an error of 0.650 in 1994 and 0.974 with an error of 0.265 in 1999 datasets. In comparison, SLR produced a correlation of 0.995 with an error of 0.072 in 1994 and 0.951 with an error of 0.370 in 1999 datasets at the well location. However, similar to the P‐impedance case, whereas SLR performed well at the well location, its application to the full seismic volume resulted in reduced performance, characterized by noisier results and longer processing time. A comparative evaluation of MBI and SLR indicates that MBI offers greater efficiency, simpler implementation and faster computational performance. As a result, the impedance outputs obtained from MBI were subsequently converted into density, P‐wave velocity and S‐wave velocity using empirical relationships derived from well log data. In the seismic volumes, a significant change in the reservoir's elastic properties was observed in the CO‐saturated zone, compared to the Utsira Formation, which serves as the reservoir into which CO has been injected. Density decreased from 1.75 to 1.35 g/cm3 (∼23%), P‐wave velocity from 2000 to 1820 m/s (∼9%) and S‐wave velocity from 1150 to 638 m/s (∼45%). These changes reflect the effects of CO replacing brine in the pore space, leading to a reduction in bulk density and stiffness and indicating overall reservoir softening due to gas injection. Integrating these inversion methods with multi‐parameter elastic estimation enables effective CO plume monitoring and reservoir characterization, highlighting the role of seismic inversion in detecting fluid‐induced changes and supporting improved monitoring strategies in carbon capture and storage (CCS) operations.

© 2025 European Association of Geoscientists & Engineers. © 2025 European Association of Geoscientists & Engineers.
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2025-10-07
2026-01-15

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Evaluating Elastic Parameters of a CO2 Plume Using Model‐Based and Sparse Layer Reflectivity Inversion of Time‐Lapse Seismic Data: A Case Study
Geophysical Prospecting 73, e70085 (2025); https://doi.org/10.1111/1365-2478.70085
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  • Article Type: Research Article
Keyword(s): model‐based inversion; seismic inversion; Sleipner field; sparse layer reflectivity; time‐lapse data

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