oa 4D Geostatistical Seismic Inversion for Mapping Probability of Changes in Δ-Properties:A CCS Case Study on Sleipner

Time-lapse geostatistical inversion is crucial for subsurface monitoring, particularly in CO2 storage. Unlike deterministic approaches, geostatistical inversion provides probabilistic models, improving uncertainty quantification and reservoir characterization. By capturing variations in pressure, saturation, and fluid movement, it enhances CO2 plume tracking, leakage detection, and risk assessment.

This study applies 4D geostatistical inversion to Sleipner data, utilizing a Markov Chain Monte Carlo (MCMC)-based Bayesian framework. This method integrates seismic data, well logs, and geological information to estimate reservoir properties with quantified confidence. Unlike traditional inversion, it generates a distribution of possible reservoir states.

The workflow involves aligning seismic datasets, estimating time shifts, and building Low-Frequency Models (LFMs) for both Base and Monitor data. Pseudo wells are created to enhance spatial analysis, and Probability Density Functions (PDFs) are derived for seismic properties. A 4D geostatistical inversion is then conducted to model subsurface dynamics. Finally, rigorous quality control checks, including impedance and Vp/Vs ratio assessments, ensure accuracy.

This multidisciplinary approach advances CO2 storage monitoring by improving reservoir management, optimizing injection strategies, and ensuring long-term storage safety. Bayesian geostatistical inversion thus represents a vital tool for enhancing subsurface studies and CCS project success.