Inference of microporosity phase properties in heterogeneous carbonate rock with data assimilation techniques

TL;DR

This paper introduces the use of the ensemble smoother multiple data assimilation (ESMDA) algorithm to infer microporosity phase properties in heterogeneous carbonate rocks, enhancing digital rock modeling with limited high-resolution data.

Contribution

The study demonstrates the application of ESMDA for inferring microporosity properties from experimental data, providing a new approach for digital rock modeling of heterogeneous carbonate formations.

Findings

ESMDA improves prediction accuracy with more observation data

The method outperforms recent alternative techniques

ESMDA can serve as a diagnostic tool for model consistency

Abstract

Accurate digital rock modeling of carbonate rocks is limited by the difficulty in acquiring morphological information on small-scale pore structures. Defined as microporosity phases in computed tomography (micro-CT) images, these small-scale pore structures may provide crucial connectivity between resolved pores (macroporosity). However, some carbonate rocks are heterogeneous, and high-resolution scans are resource-intensive, impeding comprehensive sampling of microporosity phases. In this context, we propose the usage of the ensemble smoother multiple data assimilation (ESMDA) algorithm to infer the multiphase flow properties of microporosity phases from experimental observations for digital rock modeling. The algorithm's effectiveness and compatibility are validated through a case study on a set of mm-scale Estaillades drainage image data. The case study applies ESMDA to two capillary pressure models to infer the multiphase flow properties of microporosity phases. The capillary pressure curve and saturation map were used as observations to predict wetting phase saturation at six capillary pressure steps during iterative data assimilation. The ESMDA algorithm demonstrates improved performance with increasingly comprehensive observation data inputs, achieving better prediction than recently published alternative techniques. Additionally, ESMDA can assess the consistency between various forward physical models and experimental observations, serving as a diagnostic tool for future characterization. Given the diverse application conditions, we propose that ESMDA can be a general method in the characterization workflow of carbonate rocks.