Capillary pressure equilibrium theory mapping of 4D seismic inversion results to predict saturation in a gas-water system Moumita Sengupta 1 , Ranjana Ghosh 2 , Amrita Sen 3 , and Saumen Maiti 4 ABSTRACT Understanding the effect of fluid concentration in rocks is cru- cial to characterize a hydrocarbon reservoir. Monitoring of fluid concentration specifically becomes challenging during a seques- tration/enhanced oil recovery program because the objective is to understand the amount of initial fluid replaced by liquid/gas in- jection and identify the probability of leakage with time. The prior assumption of uniform or patchy type of distribution of gas in a rock-physics theory leads to large uncertainty in the prediction of saturation. As a result, we have used the capillary pressure equi- librium theory (CPET) for creating a reservoir model that matches the physics of capillary-induced fluid invasion and avoids the un- certainty related to the type of distribution of gas in pores. We create a CPET model using the reservoir parameters of the clean and unconsolidated sandstone formation of the Sleipner field, North Sea, which is the world’s first industrial-scale CO 2 -injection project, assuming that there is no significant change in the rock frame throughout the field. The model is then used to analyze the fluid content from the time-lapse seismic inversion results of the Sleipner field. CO 2 at higher quantities, according to our research, is analogous to a uniform distribution, whereas CO 2 at lower con- centrations is mostly in between patchy and uniform distribution or slightly patchy type. We predict maximum CO 2 saturation from a quantitative interpretation of six time-lapse seismic data from 1999 to 2010 using the CPET as 75% of the pore space, and the footprint of the CO 2 plume in the topmost layer is spreading from zero in 2001 to 7 × 10 5 m 2 in 2010. Our model finds that injected CO 2 from all layers below will migrate to the top layer approximately 50 years after the commencement of the injection. INTRODUCTION Linking a reservoir’s remotely observed elastic properties to its pore-fluid content (water, hydrocarbon, or injected gas) with time at a site in the subsurface is the basis of interpreting 4D (time-lapse) seismic data. Precisely, we want to be able to calculate gas satura- tion accurately based on elastic characteristics. Because geoscient- ists and engineers are responsible for ensuring that the injected gas remains trapped underground; quantitative interpretation is impor- tant, especially in the case of CO 2 sequestration and enhanced oil recovery projects, which require realistic estimates of volume and distribution of fluid with the progress of injection. There are two necessary steps to establish a link between seismic characteristics and fluid saturations in a rock. The first step is inverting for seismic features, such as P- and S-wave impedances. The second is to apply a technique to establish the relation between the seismic properties to fluid concentration, which is usually accomplished by following any of the three approaches, namely (1) rock-physics simulation (Mavko et al., 2009), (2) integration of geostatistical data (Doyen, 1988), and (3) neural-network simulation (Hampson and Russell, 2013). We use a rock-physics-based approach to transform the res- ervoir’s elastic characteristics into fluid saturation. One must decide on the selection of an appropriate fluid substitution method once all of the seismic properties are determined. The exact fluid substitu- tion equation proposed by Gassmann (1951) assumes that a single parameter, bulk modulus (or compressibility — the inverse of the bulk modulus), describes the pore fluid. Evaluating this parameter is complex in a case where two or more fluid phases coexist in the reservoir under investigation. One approach to calculating the Manuscript received by the Editor 27 January 2022; revised manuscript received 14 October 2022; published ahead of production 24 November 2022; published online 13 February 2023. 1 Cairn Oil and Gas, Gurugram, Haryana, India. E-mail: moumita_ism@yahoo.co.in. 2 National Geophysical Research Institute, Hyderabad, India. E-mail: ranjana159@gmail.com (corresponding author). 3 C3.AI, Houston, Texas, USA. E-mail: angelofsnow006@gmail.com. 4 IIT (ISM) Dhanbad, Jharkhand, India. E-mail: saumen@iitism.ac.in. © 2023 Society of Exploration Geophysicists. All rights reserved. M49 GEOPHYSICS, VOL. 88, NO. 2 (MARCH-APRIL 2023); P. M49–M58, 11 FIGS., 1 TABLE. 10.1190/GEO2022-0054.1 Downloaded 02/16/23 to 106.210.102.189. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/page/policies/terms DOI:10.1190/geo2022-0054.1