Sensitivity-based Parameter Calibration of Single- and Dual-continuum Coreflooding Simulation Models

Abstract

Our study is keyed to the development of a viable framework for the stochastic characterization of coreflooding simulation models under two- and three-phase flow conditions taking place within a core sample in the presence of preferential flow of the kind that can be associated with the presence of a system of fractures. We do so considering various modeling strategies based on (spatially homogeneous or heterogeneous) single- and dual-continuum formulations of black-oil computational models and relying on a global sensitivity-driven stochastic parameter calibration. The latter is constrained through a set of data collected under a water alternating gas scenario implemented in laboratory-scale coreflooding experiments. We set up a collection of Monte Carlo (MC) numerical simulations while considering uncertainty encompassing (a) rock attributes (i.e., porosity and absolute permeability), as well as (b) fluid–fluid/ fluid–solid interactions, as reflected through characteristic parameters of relative permeability and capillary pressure formulations. Modern moment-based global sensitivity indices are evaluated on the basis of the MC model responses, with the aim of (i) quantifying sensitivity of the coreflooding simulation results to variations of the input uncertain model parameters and (ii) assessing the possibility of reducing the dimensionality of model parameter spaces. We then rest on a stochastic inverse modeling approach grounded on the acceptance–rejection sampling (ARS) algorithm to obtain probability distributions of the key model parameters (as identified through our global sensitivity analyses) conditional to the available experimental observations. The relative skill of the various candidate models to represent the system behavior is quantified upon relying on the deviance information criterion. Our findings reveal that amongst all tested models, a dual-continuum formulation provides the best performance considering the experimental observations available. Only a few of the parameters embedded in the dual-continuum formulation are identified as major elements significantly affecting the prediction (and associated uncertainty) of model outputs, petrophysical attributes and relative permeability model parameters having a stronger effect than parameters related to capillary pressure.

Effective Characterization of Fractured Media With PEDL: A Deep Learning‐Based Data Assimilation Approach

“…In this study, we did not address the selection of the best numerical model for specific problems. However, in practical applications, it is crucial to assess the validity of various candidate models available (Ranaee et al., 2022). This process entails integrating model selection with DA to identify the most skillful model capable of accurately representing the processes occurring in fractured media.…”

Simultaneous Determination of Relative Permeability and Capillary Pressure from an Unsteady-State Core Flooding Experiment?

“…The interpretation of unsteady-state experiments by inverse modelling (Maas & Schulte 1997, Masalmeh et al 2014, Sorop 2015) can be performed in a manual fashion i.e., by tuning relative permeability and capillary pressuresaturation functional manually to match the experimentally measured quantities such as production curve and pressure drop (Masalmeh et al 2014, Sorop et al 2015. However, performing the inverse modelling in an assisted fashion using optimization methods (Maas et al 2011, Lenormand et al 2016, Maas et al 2019, Taheriotaghsara 2020a, Taheriotaghsara et al 2020b, Manasipov & Jenei 2020, Berg et al 2020, Amrollahinasab et al 2022, Rezaei et al 2022 or Markov-chain Monte Carlo approaches (Valdez et al 2020, Valdez et al 2021, Ranaee et al 2022, Amrollahinasab et al 2022 provides also the uncertainty ranges of the relative permeability and capillary pressure-saturation functions. This additional insight allows for better clarification of the question under which conditions and protocols we can expect to determine relative permeability and capillary pressure-saturation functions from an unsteady-state experiment, and when that attempt is associated with unacceptably large uncertainty ranges.…”