The morphological approach is a computationally attractive method for calculating relative permeability and capillary pressure saturation functions. In the corresponding workflow, morphological operations are used to calculate the fluid phase distribution in the pore space of a digital twin. Once the pore space is occupied, the conductivity of the individual fluid phases and thus the relative permeability can be calculated by direct flow simulations. It therefore combines computationally favorable geometric operations with direct flow simulations. In contrast to pore network modeling, all calculations are directly performed on the digital twin without abstraction of the pore space. While the morphological operations conceptually correctly describe primary drainage processes and delivers good results, the method so far failed to describe imbibition processes and the influence of wettability. In this work, we implement contact angle distributions in a deterministic and stochastic way. In this manner, we extend the simulated saturation range from purely spontaneous to forced imbibition, resulting in a full-range imbibition relative permeability. Furthermore, by introducing stochastic contact angle distributions, different fluid phase distributions are obtained, which now allow for an uncertainty analysis. To verify the simulation results, we check (a) whether the simulation results agree with SCAL measurements and (b) compare morphologically and experimentally derived results on the pore scale. With the newly introduced concepts, the imbibition process behaves as physically expected, and shows a good agreement with experimentally derived relative permeability curves and microscopic fluid-phase distributions.

Pore-scale properties can be obtained by building a reliable digital twin of porous media through the digital rock physics (DRP) workflow. The two prerequisites of DRP are reliable imaging and computing power. Determining a proper image resolution that can reveal the actual pore-scale properties is challenging as there is a trade-off between the resolution and the representative elementary volume (REV). The REV is the smallest volume that reproduces the properties of the whole porous medium. The REV is a function of heterogeneities on the pore scale, the parameter of interest, and the scale range. Although the REV analysis for hydraulic properties is straightforward, it is computationally expensive. This study aims to estimate hydraulic pore-scale properties during REV evaluations by the geometric characterization of porous media using the Minkowski morphological functionals. Two sandstone and one carbonate rock samples were scanned at multiple imaging resolutions by both laboratory and synchrotron tomography. The REVs of various parameters of interest (porosity, permeability, surface area, tortuosity, Minkowski functionals) were computed, and the effect of image resolution and artificial rebinning on the final REV values was examined. After reaching the REV for porosity, the REV for the integrals of mean and total curvature agreed well with the permeability REV for large-volume image sizes. At constant porosity, the Minkowski integrals were found to be indicators for pore throat sizes. We also showed that the properties obtained from the rebinned (or coarsened) images differ entirely from that of actual scans at the same resolution.

Summary In OMV's digitalization program, OMV reviewed the SCAL requirements for reservoir simulation models, and defined a viable business case to substitute parts of the process: using relative permeabilities derived from digital rock simulation, rock-fluid database predictions and probabilistic petrophysics. Several proof of concept and pilot projects had the objective to shape OMV's 1st rock-fluid database using cross-industry practices for data mining, and mature data from raw mining to analytics, and business applications. In addition, OMV leveraged technology development partnerships with academia and industry partners for business-focussed minimal viable products (MVPs). The pilot projects integrated the rock-fluid data analytics, and the technology development streams, using agile methodologies, to further develop and apply the MVPs for water- and mixed-wet reservoirs. Within two years, OMV matured developed MVPs from proof of concept to pilot projects and proved technology applicability as well as business benefits. The results of two pilot projects, provided herein, show that the matured MVPs provide reliable, fast methods to simulate, and upscale pore-scale multi-phase flow into the reservoir model. The piloted MVPs allowed to track and predict probabilistically, data-based, multi-phase flow in gas- and oil-bearing reservoirs. This enables better decisions for field developments and optionality for more favourable business cases. The dependency on time and cost intensive laboratory measurements was reduced by rock-fluid data-based modelling integrated with digital rock simulations. Considerable business benefits were provided, besides an overall improved HSSE performance due to the non-destructive nature of digital rock simulation.

Image segmentation remains the most critical step in Digital Rock Physics (DRP) workflows, affecting the analysis of physical rock properties. Conventional segmentation techniques struggle with numerous image artifacts and user bias, which lead to considerable uncertainty. This study evaluates the advantages of using the random forest (RF) algorithm for the segmentation of fractured rocks. The segmentation quality is discussed and compared with two conventional image processing methods (thresholding-based and watershed algorithm) and an encoder–decoder network in the form of convolutional neural networks (CNNs). The segmented images of the RF method were used as the ground truth for CNN training. The images of two fractured rock samples are acquired by X-ray computed tomography scanning (XCT). The skeletonized 3D images are calculated, providing information about the mean mechanical aperture and roughness. The porosity, permeability, flow fields, and preferred flow paths of segmented images are analyzed by the DRP approach. Moreover, the breakthrough curves obtained from tracer injection experiments are used as ground truth to evaluate the segmentation quality of each method. The results show that the conventional methods overestimate the fracture aperture. Both machine learning approaches show promising segmentation results and handle all artifacts and complexities without any prior CT-image filtering. However, the RF implementation has superior inherent advantages over CNN. This method is resource-saving (e.g., quickly trained), does not need an extensive training dataset, and can provide the segmentation uncertainty as a measure for evaluating the segmentation quality. The considerable variation in computed rock properties highlights the importance of choosing an appropriate segmentation method.

Hypothesis: While surfactant solutions mobilize residual oil under optimal conditions by lowering the water-oil interfacial tension, emulsion phases outside of the optimum tend to be immobile. How are mobility and texture of such phases related, and how can the stability of these phases be understood? Can non-optimized surfactant solutions improve displacement processes through mobility control? Experiment: Emulsification and miscibility during surfactant flooding were investigated in microfluidics with generic oil and surfactant solutions. The salt concentration was varied in an exceptionally wide range across the optimal displacement conditions. The resulting emulsion textures were characterized in situ by optical and fluorescence microscopy and ex situ visually and by Small-Angle X-ray Scattering. Findings: During displacement, oil is increasingly solubilized and transported in a phase with a foam-like texture that develops from a droplet traffic flow. The extent and stability of these emulsion phases depend on the salinity and surfactant efficiency. The similarity with textures of classic foam phases is used to hypothesize the mechanisms that stabilize such macroemulsions in porous media. The observed microscopic displacement mechanisms can be traced back to foam formation, quality and transport. The resulting phases are of particular interest for mobility control during surfactant flooding, which, however, requires further investigation. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Accumulation of microbial biomass and its influence on porous media flow were investigated under saturated flow conditions. Microfluidic experiments were performed with model organisms, and their accumulation was observed in the pore space and on the sub-pore scale. Time-lapse optical imaging revealed different modes of biomass accumulation through primary colonization, secondary growth, and filtration events, showing the formation of preferential flow pathways in the flooding domain as result of the increasing interstitial velocity. Navier–Stokes–Brinkmann flow simulations were performed on the segmented images—a digital-twin approach—considering locally accumulated biomass as impermeable or permeable based on optical biomass density. By comparing simulation results and the experimental responses, it was shown that accumulated biomass can be considered as a permeable medium. The average intra-biomass permeability was determined to be 500 ± 200 mD, which is more than a factor of 10 larger than previously assumed in modeling studies. These findings have substantial consequences: (1) a remaining interstitial permeability, as a result of the observed channel formation and the intra-biomass permeability, and (2) a potential advective nutrient supply, which can be considered more efficient than a purely diffusive supply. The second point may lead to higher metabolic activity and substrate conversion rates which is of particular interest for geobiotechnological applications.

Chemical compositions determine the properties of fluid-fluid and fluid-mineral interfaces and hence the efficiency of multiphase displacement processes in porous media. These interactions are reflected in the microscopic fluid configuration in the pore space, which may be described by topological means. Fluid-phase topology is a promising and currently emerging field of research; however, it still has only a weak link to multiphase displacement physics. We show how the combination of topological and statistical information can be linked to displacement physics and be used for fingerprinting of displacement efficiency and hence to optimize the chemical injection-water composition. We study displacements of crude oil by alkaline injection water in microfluidics, exemplified by other water-based chemical methods. A complex coupling of fluid flow and fluid-phase behavior has been observed with the formation of emulsion phases during displacements. Oleic phases were analyzed by statistical and topological means, showing a systematic change as a function of alkali concentration linked to emulsification. In particular, Lorenz diagrams and a scaled Euler characteristic have been linked to physical properties and fluid-phase behavior and have been found to be sensitive to changes in injection-water chemistry.