Dynamics of water injection in an oil-wet reservoir rock at subsurface conditions: Invasion patterns and pore-filling events

Analysis of Microscopic Remaining Oil Based on the Fluorescence Image and Deep Learning

Fossil fuels like oil and natural gas continue to be the primary sources of global energy. Enhancing hydrocarbon recovery from exploited reservoirs has been a major scientific concern in the petroleum industry. Following extended exploitation, the reservoir's oil-water dynamics become intricate, thereby complicating petroleum and natural gas extraction. Pore-scale analysis of microscopic remaining oil (micro-remaining oil) offers theoretical underpinning for enhancing production from high-water-cut oil reservoirs. Fluorescence thin-section analysis allows for the direct evaluation of reservoir oil-bearing properties using oil-containing samples, providing insights into the occurrence and distribution patterns of micro-remaining oil without requiring time-consuming core displacement experiments. The high resolution of fluorescence images further establishes this technique as a representative method for studying micro-remaining oil. However, conventional fluorescence image analysis methods are often subjective and labor-intensive. To address this limitation, we trained four deep learning networks-U-Net, ResU-Net, ScSEU-Net, and Unet++-and applied them innovatively to automate fluorescence image segmentation. Evaluation of network performance via statistical metrics and visual observation indicated that all four networks achieved high segmentation accuracy, particularly ResU-Net, which showed robustness against over-segmentation, under-segmentation, and image noise. Finally, leveraging optimal segmentation results, we conducted quantitative analyses of oil saturation, micro-remaining oil patterns, and pore occupancy. The study demonstrated that ternary composite agents substantially decreased the presence of cluster, film, and adsorbed oils by reducing the oil-water mobility ratio and lowering oil-water interfacial tension. Primarily, these agents displaced crude oil from pores larger than 60 micrometers in an equivalent radius, leading to a significant reduction in their content. Nevertheless, substantial quantities of micro-remaining oil are still confined in pores smaller than 50 micrometers in an equivalent radius, emphasizing the need for attention during subsequent development adjustments. Our research has notably improved the efficiency and accuracy of fluorescence image analysis, effectively supporting the enhancement of recovery in high-water-cut oil reservoirs.

Percolation without trapping: How Ostwald ripening during two-phase displacement in porous media alters capillary pressure and relative permeability

Conventional measurements of two-phase flow in porous media often use completely immiscible fluids, or are performed over time scales of days to weeks. If applied to the study of gas storage and recovery, these measurements do not properly account for Ostwald ripening, significantly overestimating the amount of trapping and hysteresis. When there is transport of dissolved species in the aqueous phase, local capillary equilibrium is achieved: this may take weeks to months on the centimeter-sized samples on which measurements are performed. However, in most subsurface applications where the two phases reside for many years, equilibrium can be achieved. We demonstrate that in this case, two-phase displacement in porous media needs to be modeled as percolation without trapping. A pore network model is used to quantify how to convert measurements of trapped saturation, capillary pressure and relative permeability made ignoring Ostwald ripening to account for this effect. We show that conventional measurements overestimate the amount of capillary trapping by 20-25%.

Pore-scale modeling of two-phase flow: A comparison of the generalized network model to direct numerical simulation

Despite recent advances in pore-scale modeling of two-phase flow through porous media, the relative strengths and limitations of various modeling approaches have been largely unexplored. In this work, two-phase flow simulations from the generalized network model (GNM) [Phys. Rev. E 96, 013312 (2017)2470-004510.1103/PhysRevE.96.013312; Phys. Rev. E 97, 023308 (2018)2470-004510.1103/PhysRevE.97.023308] are compared with a recently developed lattice-Boltzmann model (LBM) [Adv. Water Resour. 116, 56 (2018)0309-170810.1016/j.advwatres.2018.03.014; J. Colloid Interface Sci. 576, 486 (2020)0021-979710.1016/j.jcis.2020.03.074] for drainage and waterflooding in two samples-a synthetic beadpack and a micro-CT imaged Bentheimer sandstone-under water-wet, mixed-wet, and oil-wet conditions. Macroscopic capillary pressure analysis reveals good agreement between the two models, and with experiments, at intermediate saturations but shows large discrepancy at the end-points. At a resolution of 10 grid blocks per average throat, the LBM is unable to capture the effect of layer flow which manifests as abnormally large initial water and residual oil saturations. Critically, pore-by-pore analysis shows that the absence of layer flow limits displacement to invasion-percolation in mixed-wet systems. The GNM is able to capture the effect of layers, and exhibits predictions closer to experimental observations in water and mixed-wet Bentheimer sandstones. Overall, a workflow for the comparison of pore-network models with direct numerical simulation of multiphase flow is presented. The GNM is shown to be an attractive option for cost and time-effective predictions of two-phase flow, and the importance of small-scale flow features in the accurate representation of pore-scale physics is highlighted.

A Closed-Form Equation for Capillary Pressure in Porous Media for All Wettabilities

A saturation–capillary pressure relationship is proposed that is applicable for all wettabilities, including mixed-wet and oil-wet or hydrophobic media. This formulation is more flexible than existing correlations that only match water-wet data, while also allowing saturation to be written as a closed-form function of capillary pressure: we can determine capillary pressure explicitly from saturation, and vice versa. We propose $$P_{{\text{c}}} = A + B\tan \left( {\frac{\pi }{2} - \pi S_{e}^{C} } \right)\,{\text{for}}\,0 \le S_{{\text{e}}} \le 1,$$ P c = A + B tan π 2 - π S e C for 0 ≤ S e ≤ 1 , where $$S_{{\text{e}}}$$ S e is the normalized saturation. A indicates the wettability: $$A>0$$ A > 0 is a water-wet medium, $$A<0$$ A < 0 is hydrophobic while small A suggests mixed wettability. B represents the average curvature and pore-size distribution which can be much lower in mixed-wet compared to water-wet media with the same pore structure if the menisci are approximately minimal surfaces. C is an exponent that controls the inflection point in the capillary pressure and the asymptotic behaviour near end points. We match the model accurately to 29 datasets in the literature for water-wet, mixed-wet and hydrophobic media, including rocks, soils, bead and sand packs and fibrous materials with over four orders of magnitude difference in permeability and porosities from 20% to nearly 90%. We apply Leverett J-function scaling to make the expression for capillary pressure dimensionless and discuss the behaviour of analytical solutions for spontaneous imbibition.

Examination of Haines Jump in Microfluidic Experiments via Evolution Graphs and Interface Tracking

This work examines a type of rapid pore-filling event in multiphase flow through permeable media that is better known as Haines Jump. While existing microfluidic experiments on Haines Jump mostly seek to maintain quasi-steady states through very low bulk flow rates over long periods of time, this work explores the combined use of a highly structured microscale transport network, high-speed fluorescent microscopy, displacement front segmentation algorithms, and a tracking algorithm to build evolution graphs that track displacement fronts as they evolve through high-speed video recording. The resulting evolution graph allows the segmentation of a high-speed recording in both space and time, potentially facilitating topology-cognitive computation on the transport network. Occurrences of Haines Jump are identified in the microfluidic displacement experiments and their significance in bulk flow rates is qualitatively analyzed. The bulk flow rate has little effect on the significance of Haines Jump during merging and splitting, but large bulk flow rates may obscure small bursts at the narrowest part of the throat.

Prediction of Pressure Increase during Waste Water Injection to Prevent Seismic Events

A considerable increase of seismicity has occurred in the USA in the last decade (2009–2020) with an annual average of 345 M3+ earthquakes. Numerous field cases have shown that excessive well pressure due to a high injection rate may have triggered seismic events. This study defines conditions for inducing a seismic event by excessive injection in the well’s pressure that may cause geomechanical damage to the rock. Introduced here is an analytical model and method for predicting pressure increase during injection of produced water contaminated with oil. The model calculates time-dependent advancement of the captured oil saturation causing the well’s injectivity damage and pressure increase. Critical conditions for a seismic event are set by defining rock failure when well pressure exceeds the fracturing pressure of the wellbore or when the increased pore pressure reduces the effective normal stress at the “weak” interface inside the rock, computed with a geomechanical model. This concept is demonstrated in three field case studies using data from geological formations in areas of petroleum operations. The results confirm field observations of the initial rapid increase of oil invasion and injection pressure that could only be controlled by reducing the rate of injection to assure continuing long-time operation.

Pore-by-Pore Modelling, Validation and Prediction of Waterflooding in Oil-Wet Rocks Using Dynamic Synchrotron Data

We predict waterflood displacement on a pore-by-pore basis using pore network modelling. The pore structure is captured by a high-resolution image. We then use an energy balance applied to images of the displacement to assign an average contact angle, and then modify the local pore-scale contact angles in the model about this mean to match the observed displacement sequence. Two waterflooding experiments on oil-wet rocks are analysed where the displacement sequence was imaged using time-resolved synchrotron imaging. In both cases the capillary pressure in the model matches the experimentally obtained values derived from the measured interfacial curvature. We then predict relative permeability for the full saturation range. Using the optimised contact angles distributed randomly in space has little effect on the predicted capillary pressures and relative permeabilities, indicating that spatial correlation in wettability is not significant in these oil-wet samples. The calibrated model can be used to predict properties outside the range of conditions considered in the experiment.

Three-phase flow displacement dynamics and Haines jumps in a hydrophobic porous medium

We use synchrotron X-ray micro-tomography to investigate the displacement dynamics during three-phase—oil, water and gas—flow in a hydrophobic porous medium. We observe a distinct gas invasion pattern, where gas progresses through the pore space in the form of disconnected clusters mediated by double and multiple displacement events. Gas advances in a process we name three-phase Haines jumps, during which gas re-arranges its configuration in the pore space, retracting from some regions to enable the rapid filling of multiple pores. The gas retraction leads to a permanent disconnection of gas ganglia, which do not reconnect as gas injection proceeds. We observe, in situ, the direct displacement of oil and water by gas as well as gas–oil–water double displacement. The use of local in situ measurements and an energy balance approach to determine fluid–fluid contact angles alongside the quantification of capillary pressures and pore occupancy indicate that the wettability order is oil–gas–water from most to least wetting. Furthermore, quantifying the evolution of Minkowski functionals implied well-connected oil and water, while the gas connectivity decreased as gas was broken up into discrete clusters during injection. This work can be used to design CO₂ storage, improved oil recovery and microfluidic devices.