Effects of Wettability and Minerals on Residual Oil Distributions Based on Digital Rock and Machine Learning

Abstract

The wettability of mineral surfaces has significant impacts on transport mechanisms of two-phase flow, distribution characteristics of fluids, and the formation mechanisms of residual oil during water flooding. However, few studies have investigated such effects of mineral type and its surface wettability on rock properties in the literature. To unravel the dependence of hydrodynamics on wettability and minerals distribution, we designed a new experimental procedure that combined the multiphase flow experiments with a CT scan and QEMSCAN to obtain 3D digital models with multiple minerals and fluids. With the aid of QEMSCAN, six mineral components and two fluids in sandstones were segmented from the CT data based on the histogram threshold and watershed methods. Then, a mineral surface analysis algorithm was proposed to extract the mineral surface and classify its mineral categories. The in situ contact angle and pore occupancy were calculated to reveal the wettability variation of mineral surface and distribution characteristics of fluids. According to the shape features of the oil phase, the self-organizing map (SOM) method, one of the machine learning methods, was used to classify the residual oil into five types, namely, network, cluster, film, isolated, and droplet oil. The results indicate that each mineral’s contribution to the mineral surface is not proportional to its relative content. Feldspar, quartz, and clay are the main minerals in the studied sandstones and play a controlling role in the wettability variation. Different wettability samples show various characteristics of pore occupancy. The water flooding front of the weakly water-wet to intermediate-wet sample is uniform, and oil is effectively displaced in all pores with a long oil production period. The water-wet sample demonstrates severe fingering, with a high pore occupancy change rate in large pores and a short oil production period. The residual oil patterns gradually evolve from networks to clusters, isolated, and films due to the effects of snap-off and wettability inversion. This paper reveals the effects of wettability of mineral surface on the distribution characteristics and formation mechanisms of residual oil, which offers us an in-deep understanding of the impacts of wettability and minerals on multiphase flow and helps us make good schemes to improve oil recovery.

Flow Patterns and Pore Structure Effects on Residual Oil during Water and CO2 Flooding: In Situ CT Scanning

“…Pore occupancy is the ratio by volume of the oil, gas, and water phases within a single pore space. Its quantitative analysis illuminates the law of fluid occupation in pores . This study relied on a pore network model derived from open-source algorithms, , which extracted the radius and pixel positions of all spheres.…”

“…Its quantitative analysis illuminates the law of fluid occupation in pores. 46 This study relied on a pore network model derived from open-source algorithms, 72,73 which extracted the radius and pixel positions of all spheres. To simplify calculations, each sphere's minimum bounding box served as the calculation area where the number of pore voxels, water phase voxels, oil phase voxels, and gas phase voxels were tallied separately.…”

“…The displacement experiment is an effective method for exploring both flow patterns and the efficiency of utilization by employing microfluidic models, − real sandstone models, ,,, and technologies such as nuclear magnetic resonance, − laser confocal techniques, − and CT scanning technology. − CT scanning technology has emerged as a significant method used in various fields, including but not limited to enhanced oil recovery (EOR), − carbon dioxide storage, , environmental remediation, , and reservoir modeling at the pore scale. − The nondestructive and high-accuracy evaluation offered by CT scanning technology places it at the forefront of the choices for the study of the flow pattern and distribution of residual oil. For example, Zhang et al utilized in situ CT scanning and QEMSCAN to acquire multicomponent 3D digital models and investigate the impact of wetting on microscale residual oil. Wu et al developed the axis-aligned bounding box algorithm for identifying oil droplets present in individual pores.…”

“…42−45 The nondestructive and high-accuracy evaluation offered by CT scanning technology places it at the forefront of the choices for the study of the flow pattern and distribution of residual oil. For example, Zhang et al 46 utilized in situ CT scanning and QEMSCAN to acquire multicomponent 3D digital models and investigate the impact of wetting on microscale residual oil. Wu et al 47 developed the axis-aligned bounding box algorithm for identifying oil droplets present in individual pores.…”