Modeling pressure and saturation distribution in a CO₂storage project using a Surrogate Reservoir Model (SRM)

Abstract

Capturing carbon dioxide (CO2) from large point sources and depositing it in a geological formation is an efficient way of decreasing CO2 concentration in the atmosphere. A comprehensive study is required to perform a safe and efficient CO2 capture and storage (CCS) project. The study includes different steps, such as selecting proper underground storage and keeping track of CO2 behavior in the storage environment. Numerical reservoir simulators are the conventional tools used to implement such an analysis. The intricacy of simulating multiphase flow, having a large number of time steps required to study injection and post‐injection periods of CO2 sequestration, a highly heterogeneous reservoir, a large number of wells, etc., will lead to a complicated reservoir model. A single realization for such a reservoir takes hours to run. Additionally, a thorough understanding of the CO2 sequestration process requires multiple realizations of the reservoir model. Consequently, using a conventional numerical simulator makes the computational cost of the analysis too high to be practical. In this paper, we examine the application of a relatively new technology, the Surrogate Reservoir Model (SRM), as an alternative tool to solve the aforementioned problems. SRM is a replica of full‐field reservoir simulation models. It can generate outputs in a very short time with reasonable accuracy. These characteristics make SRM a unique tool in CO2 sequestration modeling. This paper proposes developing an SRM for a CO2 sequestration project ongoing in the SACROC unit to model pressure behavior and phase saturation distributions during different time steps of the CO2 storage process.

Assisted History Matching Using Pattern Recognition Technology

“…Since the advent of SRMs in 2006 ( Mohaghegh, et al) many successful examples of their applications have been published Jalali, et al, 2009;Amini, et al, 2012;Amini, et al, 2014;Shahkarami, et al, 2014a). Mohaghegh et al (2012a;2012b) have discussed the results of several projects involving surrogate reservoir models for the fast track analysis of numerical simulation models.…”

“…Mohaghegh et al (2012a;2012b) have discussed the results of several projects involving surrogate reservoir models for the fast track analysis of numerical simulation models. Other publications regarding the SRMs can be found in variety of reference materials (Mohaghegh, 2009;Mohaghegh, 2011;Mohaghegh, 2014;Shahkarami, et al, 2014a;Amini, et al, 2014).…”

Smart Proxy Modeling of SACROC CO2-EOR

“…Mohaghegh describes SRM as an "ensemble of multiple, interconnected neuro-fuzzy systems that are trained to adaptively learn the fluid flow behavior from a multi-well, multilayer reservoir simulation model, such that they can reproduce results similar to those of the reservoir simulation model (with high accuracy) in real-time" [15]. Since 2006, SRM as a rapid replica of a numerical simulation model with quite high accuracy has been applied and validated in different case studies [16][17][18][19][20][21][22]. SRM can be categorized in well-based [17][18][19]21,23] or grid-based types [16,20,24] depending on the objective or the output of the model.…”

Simplified physics model of CO2 plume extent in stratified aquifer‐caprock systems

“…This leads to the identification of key dimensionless groups affecting system performance, and the development of response surface fits for plume migration in terms of these dimensionless variables. This two‐step hybrid approach, where the model parameters are selected from full‐physics simulation‐based insights, is thus different from a conventional experimental design/response surface approach, where a purely statistical model is developed between model inputs and the output of interest. Detailed compositional simulations of CO 2 injection into a saline aquifer system are carried out using CMG‐GEM® for a broad range of reservoir and caprock properties.…”