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• A pore-level numerical investigation and a subgrid scale lubrication model for studying thin-films is presented. • The water salinity and solid roughness effects are studied for the thin-film stability. • A physical map quantifying the dependency of thin-film stability on salinity and roughness is presented. • Novelty of the work is at capturing the electric double layer effects at the pore-scale. • This work can well serve as a physical basis for studying water salinity effects at larger scales. The interfacial dynamics in natural porous media are affected not only by the interplay between viscous and capillary forces but also the solid surface wettability. It has been hypothesized that the wettability alteration induced by changes in the water salinity is primarily caused by electric double-layer force expansion, which strongly affects the multiphase flow dynamics. We investigate the effect of water ionic composition and surface roughness on pore-scale wettability alteration. Multiphase hydrodynamics is numerically captured by a lubrication approximation describing the evolution of thin-films coupled with a multiscale level-set approach. An oil blob mobilized by water within a single pore is considered as a case study. The effect of brine ionic composition is accounted for by an electric double-layer through the water ionic strength and zeta-potential parameters. We demonstrate that high-salinity water thin-films collapse to an adsorbed nanometer layer, leading to a large pressure drop during mobilization of the blob induced by the attractive surface forces. However, low-salinity water thin-films are stable due to the repulsive electric double-layer forces, leading to less pressure drop during mobilization of the blob. The novelty of this work lies in efficiently capturing the nanoscale effects of the electric double-layer in pore-scale multiphase flow displacements. Our quantitative investigations provide fundamental insights into the efficiency of low-salinity waterflooding. [ABSTRACT FROM AUTHOR]

Abstract Here, through a systematic methodology and the use of high performance computing, we calculate the optimum shape for a wave energy converter under the action of incident waves of (i) monochromatic unidirectional, (ii) monochromatic directional, (iii) polychromatic unidirectional and (iv) polychromatic directional (with both directional symmetry and asymmetry). As a benchmark for our study, without loss of generality, we consider a submerged planar pressure differential wave energy converter, and use the Genetic Algorithm to search through a wide range of shapes. A new parametric description of absorber shape based on Fourier decomposition of geometrical shapes is introduced, and for each shape hydrodynamic coefficients are calculated, optimum power take-off parameters are obtained, and overall efficiency is determined. We show that an optimum geometry of the absorber plate can absorb significantly higher energy (in some cases few times higher) when compared to a circular shape of the same area. Specifically, for a unidirectional incident wave, the optimum shape, as expected, is obtained to be the most elongated shape. For directional incident waves, a butterfly-shape is the optimum geometry whose details depend on not only the amplitude and direction of incident wave components, but also the relative phases of those components. For the latter effect, we find an optimally averaged profile through a statistical analysis. [ABSTRACT FROM AUTHOR]

• A multiscale numerical framework has been developed to simulate the multiphase flow in complex porous media. • This model includes a subgrid lubrication approximation for capturing the evolution of thin films on curved solid surfaces. • A Navier-Stokes Level-set solver is coupled with an immersed boundary approach for resolving the multiphase interactions. Complex solid geometries, subgrid thin films, and interfacial deformations are critical for studying multiphase flows in porous media. However, it is challenging to accurately predict the flow evolution in porous media because of the difficulties in capturing the fluid-solid and fluid-fluid interactions in complex-shaped confinements at the pore-scale. In this work, we propose a multiscale continuum framework enabling the accurate simulation of the multiphase flow physics in complex porous media. Within this framework, we couple an efficient and accurate incompressible Navier-Stokes solver developed on a fixed Cartesian grid with a level-set method designed for capturing the interface between immiscible two-phase flows at low Capillary numbers. To capture the effects of complex solid boundaries on the flow dynamics, we employ an immersed boundary method based on a direct forcing approach. Most importantly, we develop a subgrid-scale thin-film model on a cylindrical coordinate around the curved solid surfaces to resolve the thin liquid films below the grid resolution and capture their effects on the fluid-fluid interfaces. The proposed multiscale framework illustrates how the embedded subroutines such as the level-set approach, the immersed boundary method, and the thin-film model can be coupled in order to capture the crucial physics involved in multiphase flow within porous media at different length scales. [ABSTRACT FROM AUTHOR]

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. A novel and rigorous non-local formulation for flow in porous media is presented, in which the reservoir is represented by an adjacency matrix describing the connectivities of comprising compartments. We automatically divide the reservoir into a number of distinct compartments, in which the direction-dependent multiphase flow communication is a function of non-local phase potential differences. Our proposed clustering framework begins with a mixed-type raw dataset which can be categorical/numerical, spatial/temporal, and discrete/continuous. The dataset can contain noisy/missing values of different data types including but not limited to well production/injection history, well location, well type, geological features, PVT measurements, perforation data, etc. Unsupervised clustering techniques suited to the input data types (e.g. k-prototypes, spectral, Gaussian Mixtures, and hierarchical clustering), and appropriate distance measures (such as Euclidean distance, soft dynamic time warping, and mode) are used. The input data is standardized, and upon convergence check, the best clustering representation is obtained. Finally, Support-Vector-Machine technique is utilized in the kernel space to trace a demarcating hyperplane for the clusters. In a specific case study reported here, the proposed workflow is applied to a major field with a couple of hundreds of wells with more than 40 years of production history. Leveraging the fast forward model, an efficient ensemble-based history matching framework is applied to reduce the uncertainty of the global reservoir parameters such as inter-blocks and aquifer-reservoir communications, fault transmissibilities, and block-based oil-in-place. The ensemble of history matched models are then used to provide a probabilistic forecast for different field development scenarios. In addition, the clustering framework enables us to treat missing data and use the augmented dataset for improving the clustering accuracy. In summary, in this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. • This work provides a fast and accurate multiscale modeling of compartmentalized reservoirs. • This work adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies. • This work includes an efficient ensemble-based history matching framework to reduce the uncertainty of the global reservoir parameters. • In this work the ensemble of history matched models are used to provide a probabilistic forecast. [ABSTRACT FROM AUTHOR]

Low salinity water injection (LSWI) is the process of injecting modified salinity brine with controlled ionic composition to achieve increased oil recovery compared to conventional waterflooding. This paper reviews the most recent advances in proposed low salinity mechanisms, but specifically emphasizes the role of thin liquid films in crude oil/brine/rock systems. Importantly, thin water films on rock surfaces affect hydraulic resistance of pore channels as well as phase-trapping mechanisms. As films become thicker, they provide greater lubrication of oil droplets and hence, flow resistance decreases. Consequently, films dictate oil and water distribution in porous media and determine the wettability of crude oil/brine/rock systems under static and dynamic conditions. The stability of the thin water films depends on the interactions between the oil/brine and the calcite/brine interfaces through van der Waals, electrostatic, and structural forces. Image 1 [ABSTRACT FROM AUTHOR]

Wave energy technologies have the potential to play a significant role in the supply of renewable energy worldwide. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this paper, we explore the optimisation of WEC arrays consisting of three-tether buoys. Such arrays can be optimised for total energy output by adjusting the relative positions of buoys in a wave farm. As there are complex hydrodynamic interactions among WECs, the evaluation of each parameter setting is computationally expensive and thus limits the feasible number of full model evaluations that can be made. Furthermore, these WEC interactions make up a non-convex, multi-modal (with multiple local-optima), continuous and constrained optimisation problem. This problem is challenging to solve using optimisation methods. To tackle the challenge of optimising the positions of WECs in a wave farm, we propose a novel multi-swarm cooperative co-evolution algorithm which consists of three meta-heuristics: the multi verse optimiser (MVO) algorithm, the equilibrium optimisation (EO) method, and the moth flame optimisation (MFO) approach with a backtracking strategy, we introduce a fast, effective new surrogate model to speed up the process of optimisation. To assess the effectiveness of our proposed approach, 11 state-of-the-art bio-inspired algorithms and three recent hybrid heuristic techniques were compared in six real wave situations located on the coasts of Australia, with two wave farm sizes (four and nine WECs). The experimental study presented in this paper shows that our hybrid cooperative framework exhibited the best performance in terms of the quality of obtained solutions, computational efficiency, and convergence speed compared with other 14 state-of-the-art meta-heuristics. Furthermore, we found that the power output of the best-found 9-buoy arrangements were higher than that of perpendicular layouts at at 4.15 % , 3.29 % , 3.62 % , 9.2 % , 5.74 % , and 2.43 % for the Perth, Adelaide, Sydney, Tasmania, Brisbane, and Darwin wave sites, respectively. Our investigations reveal that the best-found arrangement at the Tasmania wave site was able to absorb the highest level of wave power relative to the other locations. • A new Multi-swarm Cooperative optimisation framework is proposed to optimise wave farms performance. • A new fast surrogate model is developed to speed up expensive optimisation process of wave farm. • To improve the placement of converters, a symmetrical backtracking search algorithm is proposed. • To handle the infeasible wave converters layout during the optimisation, a new and effective repair function is developed. • The proposed optimiser enhances significantly the total power output at six wave farms compared with previous methods. [ABSTRACT FROM AUTHOR]