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  • Journal article
    Alarouj M, Jackson MD, 2022,

    Experimental measurement of the exclusion-diffusion potential in sandstone and shaly sand samples at full and partial water saturation

    , GEOPHYSICS, Vol: 87, Pages: M235-M246, ISSN: 0016-8033
  • Journal article
    Bahlali ML, Salinas P, Jackson MD, 2022,

    Efficient Numerical Simulation of Density-Driven Flows: Application to the 2-and 3-D Elder Problem

    , WATER RESOURCES RESEARCH, Vol: 58, ISSN: 0043-1397
  • Journal article
    Hamzehloo A, Bahlali ML, Salinas P, Jacquemyn C, Pain CC, Butler AP, Jackson MDet al., 2022,

    Modelling saline intrusion using dynamic mesh optimization with parallel processing

    , ADVANCES IN WATER RESOURCES, Vol: 164, ISSN: 0309-1708
  • Journal article
    Alarouj M, Jackson MD, 2022,

    Numerical modeling of self-potential in heterogeneous reservoirs

    , GEOPHYSICS, Vol: 87, Pages: E103-E120, ISSN: 0016-8033
  • Journal article
    Collini H, Jackson MD, 2022,

    Relationship between zeta potential and wettability in porous media: insights from a simple bundle of capillary tubes model

    , Journal of Colloid and Interface Science, Vol: 608, Pages: 605-621, ISSN: 0021-9797

    Hypothesis & MotivationExperimental data suggest a relationship between the macroscopic zeta potential measured on intact rock samples and the sample wettability. However, there is no pore-scale model to quantify this relationship.MethodsWe consider the simplest representation of a rock pore space: a bundle of capillary tubes of varying size. Equations describing mass and charge transfer through a single capillary are derived and the macroscopic zeta potential and wettability determined by integrating over capillaries. Model predictions are tested against measured data yielding a good match.FindingsMixed- and oil-wet models return a macro-scale zeta potential that is a combination of the micro-scale zeta potential of mineral-brine and oil-brine interfaces and the relationship between macro-scale zeta potential and water saturation exhibits hysteresis. The model predicts a similar relationship between zeta potential and wettability to that observed in experimental data but does not provide a perfect match. Fitting the model to experimental data allows the oil-brine zeta potential to be estimated at conditions where it cannot be measured directly. Results suggest that positive values of the oil-brine zeta potential may be more common than previously thought with implications for surface complexation models and the design of controlled salinity waterflooding of oil reservoirs.
  • Journal article
    Sparks RSJ, Blundy JD, Cashman K, Jackson M, Rust A, Wilson CJNet al., 2022,

    Large silicic magma bodies and very large magnitude explosive eruptions

    , BULLETIN OF VOLCANOLOGY, Vol: 84, ISSN: 0258-8900
  • Journal article
    Alarouj M, Collini H, Jackson MD, 2021,

    Positive Zeta Potential in Sandstones Saturated With Natural Saline Brine

    , GEOPHYSICAL RESEARCH LETTERS, Vol: 48, ISSN: 0094-8276
  • Journal article
    Silva VLS, Salinas P, Jackson MD, Pain CCet al., 2021,

    Machine learning acceleration for nonlinear solvers applied to multiphase porous media flow

    , Computer Methods in Applied Mechanics and Engineering, Vol: 384, Pages: 1-17, ISSN: 0045-7825

    A machine learning approach to accelerate convergence of the nonlinear solver in multiphase flow problems is presented here. The approach dynamically controls an acceleration method based on numerical relaxation. It is demonstrated in a Picard iterative solver but is applicable to other types of nonlinear solvers. The aim of the machine learning acceleration is to reduce the computational cost of the nonlinear solver by adjusting to the complexity/physics of the system. Using dimensionless parameters to train and control the machine learning enables the use of a simple two-dimensional layered reservoir for training, while also exploring a wide range of the parameter space. Hence, the training process is simplified and it does not need to be rerun when the machine learning acceleration is applied to other reservoir models. We show that the method can significantly reduce the number of nonlinear iterations without compromising the simulation results, including models that are considerably more complex than the training case.
  • Journal article
    Titus Z, Heaney C, Jacquemyn C, Salinas P, Jackson MD, Pain Cet al., 2021,

    Conditioning surface-based geological models to well data using artificial neural networks

    , Computational Geosciences: modeling, simulation and data analysis, Vol: 26, Pages: 779-802, ISSN: 1420-0597

    Surface-based modelling provides a computationally efficient approach for generating geometrically realistic representations of heterogeneity in reservoir models. However, conditioning Surface-Based Geological Models (SBGMs) to well data can be challenging because it is an ill-posed inverse problem with spatially distributed parameters. To aid fast and efficient conditioning, we use here SBGMs that model geometries using parametric, grid-free surfaces that require few parameters to represent even realistic geological architectures. A neural network is trained to learn the underlying process of generating SBGMs by learning the relationship between the parametrized SBGM inputs and the resulting facies identified at well locations. To condition the SBGM to these observed data, inverse modelling of the SBGM inputs is achieved by replacing the forward model with the pre-trained neural network and optimizing the network inputs using the back-propagation technique applied in training the neural network. An analysis of the uncertainties associated with the conditioned realisations demonstrates the applicability of the approach for evaluating spatial variations in geological heterogeneity away from control data in reservoir modelling. This approach for generating geologically plausible models that are calibrated with observed well data could also be extended to other geological modelling techniques such as object- and process-based modelling.
  • Journal article
    Kampitsis AE, Kostorz WJ, Muggeridge AH, Jackson MDet al., 2021,

    The life span and dynamics of immiscible viscous fingering in rectilinear displacements

    , PHYSICS OF FLUIDS, Vol: 33, ISSN: 1070-6631
  • Journal article
    Salinas P, Regnier G, Jacquemyn C, Pain CC, Jackson MDet al., 2021,

    Dynamic mesh optimisation for geothermal reservoir modelling

    , Geothermics, Vol: 94, Pages: 1-13, ISSN: 0375-6505

    Modelling geothermal reservoirs is challenging due to the large domain and wide range of length- and time-scales of interest. Attempting to represent all scales using a fixed computational mesh can be very computationally expensive. Application of dynamic mesh optimisation in other fields of computational fluid dynamics has revolutionised the accuracy and cost of numerical simulations. Here we present a new approach for modelling geothermal reservoirs based on unstructured meshes with dynamic mesh optimisation. The resolution of the mesh varies during a simulation, to minimize an error metric for solution fields of interest such as temperature and pressure. Efficient application of dynamic mesh optimisation in complex subsurface reservoirs requires a new approach to represent geologic heterogeneity and we use parametric spline surfaces to represent key geological features such as faults and lithology boundaries. The resulting 3D surface-based models are mesh free; a mesh is created only when required for numerical computations. Dynamic mesh optimisation preserves the surfaces and hence geologic heterogeneity. The governing equations are discretised using a double control volume finite element method that ensures heat and mass are conserved and provides robust solutions on distorted meshes. We apply the new method to a series of test cases that model sedimentary geothermal reservoirs. We demonstrate that dynamic mesh optimisation yields significant performance gains, reducing run times by up to 8 times whilst capturing flow and heat transport with the same accuracy as fixed meshes.
  • Journal article
    Kostorz WJ, Muggeridge AH, Jackson MD, 2021,

    Non-intrusive reduced order modeling: Geometrical framework, high-order models, and a priori analysis of applicability

    , INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Vol: 122, Pages: 2545-2565, ISSN: 0029-5981
  • Journal article
    Lyu Z, Lei Q, Yang L, Heaney C, Song X, Salinas P, Jackson M, Li G, Pain Cet al., 2021,

    A novel approach to optimising well trajectory in heterogeneous reservoirs based on the fast-marching method

    , Journal of Natural Gas Science and Engineering, Vol: 88, Pages: 1-12, ISSN: 1875-5100

    To achieve efficient recovery of subsurface energy resources, a suitable trajectory needs to be identified for the production well. In this study, a new approach is presented for automated identification of optimum well trajectories in heterogeneous oil/gas reservoirs. The optimisation procedures are as follows. First, a productivity potential map is generated based on the site characterisation data of a reservoir (when available). Second, based on the fast-marching method, well paths are generated from a number of entrance positions to a number of exit points at opposite sides of the reservoir. The well trajectory is also locally constrained by a prescribed maximum curvature to ensure that the well trajectory is drillable. Finally, the optimum well trajectory is selected from all the candidate paths based on the calculation of a benefit-to-cost ratio. If required, a straight directional well path, may also be derived through a linear approximation to the optimised non-linear trajectory by least squares analysis. Model performance has been demonstrated in both 2D and 3D. In the 2D example, the benefit-to-cost ratio of the optimised well is much higher than that of a straight well; in the 3D example, laterals of various curvatures are generated. The applicability of the method is tested by exploring different reservoir heterogeneities and curvature constraints. This approach can be applied to determine the entrance/exit positions and the well path for subsurface energy system development, which is useful for field applications.
  • Journal article
    Jacquemyn C, Pataki MEH, Hampson GJ, Jackson MD, Petrovskyy D, Geiger S, Marques CC, Machado Silva JD, Judice S, Rahman F, Costa Sousa Met al., 2021,

    Sketch-based interface and modelling of stratigraphy and structure in three dimensions

    , Journal of the Geological Society, Vol: 178, Pages: 1-17, ISSN: 0016-7649

    Geological modelling is widely used to predict resource potential in subsurface reservoirs. However, modelling is often slow, requires use of mathematical methods that are unfamiliar to many geoscientists, and is implemented in expert software. We demonstrate here an alternative approach using sketch-based interface and modelling, which allows rapid creation of complex three-dimensional (3D) models from 2D sketches. Sketches, either on vertical cross-sections or in map-view, are converted to 3D surfaces that outline geological interpretations. We propose a suite of geological operators that handle interactions between the surfaces to form a geologically realistic 3D model. These operators deliver the flexibility to sketch a geological model in any order and provide an intuitive framework for geoscientists to rapidly create 3D models. Two case studies are presented, demonstrating scenarios in which different approaches to model sketching are used depending on the geological setting and available data. These case studies show the strengths of sketching with geological operators. Sketched 3D models can be queried visually or quantitatively to provide insights into heterogeneity distribution, facies connectivity or dynamic model behaviour; this information cannot be obtained by sketching in 2D or on paper.
  • Journal article
    Alarouj M, Ijioma A, Graham MT, MacAllister DJ, Jackson MDet al., 2021,

    Numerical modelling of self-potential in subsurface reservoirs

    , Computers & Geosciences, Vol: 146, Pages: 1-19, ISSN: 0098-3004

    We report a new, open-source, MATLAB-based 3D code for numerically simulating the self-potential (SP) in subsurface reservoirs. The code works as a post-processor, using outputs from existing reservoir flow and transport simulators at a selected timestep to calculate the SP throughout the reservoir model. The material properties required to calculate the SP are user defined and may be constant or vary in each cell. The code solves the equations governing flow and transport of electrical charge and global charge conservation using a control-volume-finite-difference scheme. Electrical currents associated with the SP may spread beyond the reservoir model domain, and the code allows for the domain to be extended vertically and laterally to account for this. Here, we present the governing equations and the numerical method used and demonstrate application of the code using an example in which we predict the SP signals associated with oil production from a subsurface reservoir supported by water injection.
  • Conference paper
    Costa Sousa M, Silva J, Silva C, De Carvalho F, Judice S, Rahman F, Jacquemyn C, Pataki M, Hampson G, Jackson M, Petrovskyy D, Geiger Set al., 2020,

    Smart modelling of geologic stratigraphy concepts using sketches

    , Smart Tools and Applications in computer Graphics (STAG) 2020, Publisher: The Eurographics Association, Pages: 89-100

    Several applications of Earth Science require geologically valid interpretation and visualization of complex physical structures in data-poor subsurface environments. Hand-drawn sketches and illustrations are standard practices used by domain experts for conceptualizing their observations and interpretations. These conceptual geo-sketches provide rich visual references for exploring uncertainties and helping users formulate ideas, suggest possible solutions, and make critical decisions affecting the various stages in geoscience studies and modelling workflows. In this paper, we present a sketch-based interfaces and modelling (SBIM) approach for the rapid conceptual construction of stratigraphic surfaces, which are common to most geologic modelling scales, studies, and workflows. Our SBIM approach mirrors the way domain users produce geo-sketches and uses them to construct 3D geologic models, enforcing algorithmic rules to ensure geologically-sound stratigraphic relationships are generated, and supporting different scales of geology being observed and interpreted. Results are presented for two case studies demonstrating the flexibility and broad applicability of our rule-based SBIM approach for conceptual stratigraphy.
  • Journal article
    Yekta A, Salinas P, Hajirezaie S, Amooie MA, Pain CC, Jackson MD, Jacquemyn C, Soltanian MRet al., 2020,

    Reactive transport modeling in heterogeneous porous media with dynamic mesh optimization

    , Computational Geosciences: modeling, simulation and data analysis, Vol: 25, Pages: 357-372, ISSN: 1420-0597

    This paper presents a numerical simulator for solving compositional multiphase flow and reactive transport. The simulator was developed by effectively linking IC-FERST (Imperial College Finite Element Reservoir SimulaTor) with PHREEQCRM. IC-FERST is a next-generation three-dimensional reservoir simulator based on the double control volume finite element method and dynamic unstructured mesh optimization and is developed by the Imperial College London. PHREEQCRM is a state-of-the-art geochemical reaction package and is developed by the United States Geological Survey. We present a step-by-step framework on how the coupling is performed. The coupled code is called IC-FERST-REACT and is capable of simulating complex hydrogeological, biological, chemical, and mechanical processes occurring including processes occur during CO2 geological sequestration, CO2 enhanced oil recovery, and geothermal systems among others. In this paper, we present our preliminary work as well as examples related to CO2 geological sequestration. We performed the model coupling through developing an efficient application programming interface (API). IC-FERST-REACT inherits high-order methods and unstructured meshes with dynamic mesh optimization from IC-FERST. This reduces the computational cost by placing the mesh resolution where and when necessary and it can better capture flow instabilities if they occur. This can have a strong impact on reactive transport simulations which usually suffer from computational cost. From PHREEQCRM the code inherits the ability to efficiently model geochemical reactions. Benchmark examples are used to show the capability of IC-FERST-REACT in solving multiphase flow and reactive transport.
  • Journal article
    Kostorz WJ, Muggeridge AH, Jackson MD, 2020,

    An efficient and robust method for parameterized nonintrusive reduced-order modeling

    , INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Vol: 121, Pages: 4674-4688, ISSN: 0029-5981
  • Journal article
    Li S, Jackson MD, Agenet N, 2020,

    Role of the calcite-water interface in wettability alteration during low salinity waterflooding

    , FUEL, Vol: 276, ISSN: 0016-2361
  • Journal article
    Osman H, Graham GH, Moncorge A, Jacquemyn C, Jackson MDet al., 2020,

    Is cell-to-cell scale variability necessary in reservoir models?

    , Mathematical Geosciences, Vol: 53, Pages: 271-296, ISSN: 1573-8868

    Reservoir models typically contain hundreds-of-thousands to millions of grid cells in which petrophysical properties such as porosity and permeability vary on a cell-to-cell basis. However, although the petrophysical properties of rocks do vary on a point-to-point basis, this variability is not equivalent to the cell-to-cell variations in models. We investigate the impact of removing cell-to-cell variability on predictions of fluid flow in reservoir models. We remove cell-to-cell variability from models containing hundreds of thousands of unique porosity and permeability values to yield models containing just a few tens of unique porosity and permeability values grouped into a few internally homogeneous domains. The flow behavior of the original model is used as a reference. We find that the impact of cell-to-cell variability on predicted flow is small. Cell-to-cell variability is not necessary to capture flow in reservoir models; rather, it is the spatially correlated variability in petrophysical properties that is important. Reservoir modelling effort should focus on capturing correlated geologic domains in the most realistic and computationally efficient manner.

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