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- Parameterizing the fluid forces on limpet shells in unidirectional flow
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Abstract: Abstract Current parameterizations of the hydrodynamic forces on irregular particles consider some shape dependencies, but lack an explicit dependence on the orientation with respect to the flow. In this paper, we propose a new parameterization of the drag and lift forces acting on whole Limpet shells at arbitrary orientations with respect to the direction of flow through the linear regression of fluid forces against the velocity components in an object frame of reference. The fluid forces were estimated using boundary layer-resolving Reynolds-averaged Navier-Stokes (RANS) simulations. We verified the accuracy of the shear stress transport (SST) \(k-\omega \) turbulence model on flat plates with varying angles of attack, and we achieved coefficients of determination versus existing data of approximately 0.95 for both the drag and lift coefficients. From the linear regression of our simulated force data, we developed a model as a function of 3-dimensional orientations to predict the hydrodynamic forces acting on a Limpet shell with coefficients of determination of 0.80 for normal forces and 0.51 for longitudinal forces.
PubDate: 2023-11-25
- Comparative calibration of 1D+2D and 3D hydrogeological watershed models
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Abstract: Abstract In this work, we study the calibration of the parameters of a hydrogeological watershed model by comparing a 1D+2D approach that combines unsaturated 1D columns and a saturated 2D model, with a full 3D approach. In a first step, a heterogeneous permeability field is estimated by an inversion procedure for each model (2D saturated and 3D unsaturated). The fields obtained are similar but the calculation time is obviously much higher in the case of the 3D model: the 2D model seems therefore sufficient and more efficient to evaluate permeability fields using piezometric measurements in the case of vertically homogeneous aquifers. The second step focuses on the calibration of the hydraulic parameters by adjusting the hydraulic heights either derived from a 1D+2D reference model at several fictitious points distributed over the entire domain, or measured in a dozen real piezometers. Both approaches provide a good fit to the piezometric measurements, but the parameter values differ significantly: the van Genuchten alpha coefficient is unrealistic in the 1D+2D approach, reflecting a poorer consideration of the modeling unsaturated zone, while the porosity value is higher in the 3D approach, which can probably be remedied by developing a more suitable cost function.
PubDate: 2023-11-23
- The effects of dispersion and non-linearity on the simulation of
landslide-generated waves using the reduced two-layer non-hydrostatic
model-
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Abstract: Abstract This paper revisits the previously developed NH-2LR (reduced two-layer non-hydrostatic) model. The governing equations and numerical schemes are written in terms of normalized variables, with two dimensionless parameters representing dispersion and non-linearity. By utilizing analytical solutions and laboratory experiments, this study aims to validate the numerical NH-2LR model and investigate the effects of dispersion and non-linearity on the resulting waves. The first validation employs the analytical solution of the linear and fully dispersive model of a landslide moving with constant velocity on a flat bottom. The second validation involves a landslide hump sliding over a constant beach slope. A closer look at the run-up height reveals that this case is non-dispersive. Furthermore, we found that the dispersion effect was evident from the beginning of the wave formation process. Finally, we compare our numerical results to experiments on submarine landslides on sloping beaches. We found that dispersion is essential in the early generation and propagation of waves in off-shore regions. Moreover, non-linearity significantly influences the maximum run-up of landslide-generated waves.
PubDate: 2023-11-17
- Multi-asset closed-loop reservoir management using deep reinforcement
learning-
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Abstract: Abstract Closed-loop reservoir management (CLRM), in which history matching and production optimization are performed multiple times over the life of an asset, can provide significant improvement in the specified objective. These procedures are computationally expensive due to the large number of flow simulations required for history matching and optimization. Existing CLRM procedures are applied asset by asset, without taking advantage of similarities in geology or the temporal structure of well data across assets. Here, we develop a CLRM framework to treat multiple assets from related geological systems, which enables reductions in computational demands. Deep reinforcement learning is used to train a single global control policy that is applicable for all assets considered. The new framework is an extension of a recently introduced control policy methodology for individual assets. Embedding layers are incorporated into the representation to handle the different numbers of decision variables that arise for the different assets. Because the global control policy learns a unified representation of useful features from multiple related assets, it is less expensive to construct than asset-by-asset training (we observe about \(3\times \) speedup in our examples). The production optimization problem includes a relative-change constraint on the well settings, which renders the results suitable for practical use. We apply the multi-asset CLRM framework to 2D and 3D water-flooding examples. In both cases, four assets with different well counts, well configurations, and geostatistical descriptions are considered. Numerical experiments demonstrate that the global control policy provides objective function values, for both the 2D and 3D cases, that are nearly identical to those from control policies trained individually for each asset. This promising finding suggests that multi-asset CLRM may indeed represent a viable practical strategy.
PubDate: 2023-11-03
- Numerical simulation of multiscale fault systems with rate- and
state-dependent friction-
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Abstract: Abstract We consider the deformation of a geological structure with non-intersecting faults that can be represented by a layered system of viscoelastic bodies satisfying rate- and state-depending friction conditions along the common interfaces. We derive a mathematical model that contains classical Dieterich- and Ruina-type friction as special cases and accounts for possibly large tangential displacements. Semi-discretization in time by a Newmark scheme leads to a coupled system of nonsmooth, convex minimization problems for rate and state to be solved in each time step. Additional spatial discretization by a mortar method and piecewise constant finite elements allows for the decoupling of rate and state by a fixed point iteration and efficient algebraic solution of the rate problem by truncated nonsmooth Newton methods. Numerical experiments with a spring slider and a layered multiscale system illustrate the behavior of our model as well as the efficiency and reliability of the numerical solver.
PubDate: 2023-10-31
- A 3D organized point cloud clustering algorithm for seismic fault data
based on region growth-
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Abstract: Abstract Traditional classification methods for seismic fault 3D point cloud data rely on fault annotation data. Fault annotation data is usually stored in the data structure of a 3D array, and represented by organized point cloud data. The artificial fault annotation method analyses point data in each 2D slice respectively, without considering the 3D spatial distribution of all points, produces results without clustering and proper continuity in 3D space, and causes inconvenience for subsequent research work, such as calculation of the trend, inclination, and other information of each single fault. This paper presents a very simple but efficient clustering method for seismic fault annotation point cloud data to divide the points into each fault. To provide features as fundaments for this clustering method, we propose a normal direction estimation algorithm for seismic fault point cloud data. Tested by the experiments on synthetic data and field data, our method can divide the points with accuracy, reliability, and adaptability, thus providing a foundation for unified analysis, processing, and calculation for each part of the same fault, and analyzing fault displacement and low sequence faults, moreover, the clustering result could be used to fix 3D continuity of fault annotation data itself.
PubDate: 2023-10-26
- Implementation of Soreide and Whitson EoS in a GPU-based reservoir
simulator-
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Abstract: Abstract Reservoir simulation is traditionally based on the assumption that water is an inert phase, while hydrocarbon components split into oil and gas phases. This approach is usually reasonable when modeling conventional hydrocarbon recovery, but specific applications may require accounting for mass exchange between the water and hydrocarbon phases. We here present the extension of our Graphics Processing Units (GPUs) compositional reservoir simulator (Esler et al. SPE J. 27(01), 597–612, 2021) to support gas-water equilibrium. Specifically, the Søreide and Whitson equation of state (EoS) (Søreide and Whitson Fluid Phase Equilib. 77, 217–240, 1992) was implemented to compute mutual solubilities of hydrocarbon/brine mixtures. The impact of salinity on phase equilibrium is accounted for, with salt being treated as an active tracer. The simulator uses a mass-variables formulation, meaning that little modifications to the construction of transport equations and Jacobian assembly was required; most of the required code changes are localized in the EoS module for the computation of component fugacities, and phase properties such as partial molar fractions and partial molar volumes. Treating salt as an active tracer instead of defining a further pseudo-component has an important advantage with the Søreide and Whitson EoS. If salinity changes as in water vaporization processes, our choice ensures that flash iterations can still be cast as a Gibbs Minimization problem with salinity being a constant parameter. On the contrary, salinity would change as flash iterations progress, jeopardizing the thermodynamic consistency of the phase equilibria. The overall reservoir simulation system of equations is still accurate to first order in time, at the cost of possibly slight volume imbalances at the end of converged timesteps. In this paper, we focused on CO \(_2\) sequestration in saline aquifers, where solubility trapping is a key mechanism. The accuracy of our implementation with respect to conventional CPU ones is first demonstrated on a synthetic box model. We then select an open-access aquifer model (Gassnova 2016) to illustrate its applicability in an industrial setting. Finally, we show how being able to seamlessly run high resolution models allows for modeling of convective mixing. A key conclusion of this work is that the extreme performance of GPU-based reservoir simulation naturally transfers to new fields of study, which is critical when modeling saline aquifers whose extent is an order of magnitude larger than that of typical oil and gas fields.
PubDate: 2023-10-21
- Numerical modeling on high-temperature and high-pressure gas condensate
recovery considering the viscosity variation and dynamic relative
permeability-
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Abstract: Abstract The production evaluation of high-temperature and high-pressure gas condensate remains unsatisfactory in terms of precision due to the inadequate knowledge of viscosity variation and dynamic relative permeability. Here, we first conduct phase behavior experiments to clarify the mechanisms of viscosity variation followed by core flooding experiments to reveal the dynamic relative permeability. The viscosity variation responses in different regimes to pressure and temperature increases. Besides, the carbon number of the gas condensate exhibits three influencing scenarios on viscosity variation at different pressure and temperature conditions. Moreover, the variation of relative permeability is limited within 5% as temperature decreases together with pressure decreases, but the decline rate of pressure is expected to be higher than that of temperatures. We then theoretically model the production of the gas condensate reservoir by integrating the viscosity variation and dynamic relative permeability mechanisms obtained from the experiments. Modeling results well fit the field data. We adopt the model to analyze and elucidate the effects of temperature, pressure and fracture size on water flooding recovery of ultra-deep gas condensate reservoirs. We figure out that at high temperature, the gas condensate recovery can be enhanced by a rapid pressure decline which can further accelerate the oil thinning and gas condensate production. A high gas condensate production is obtained in the early stage at high pressure, but the oil with high viscosity consumes vast amounts of reservoir energy, leading to a rapid pressure drop in the early development stage. This work pioneers in revealing the interactive mechanisms of viscosity variation and dynamic permeability of ultra-deep reservoir during water flooding development, and brings insights into more adequate gas condensate recovery evaluation.
PubDate: 2023-10-16
- Impact of artificial topological changes on flow and transport through
fractured media due to mesh resolution-
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Abstract: Abstract We performed a set of numerical simulations to characterize the interplay of fracture network topology, upscaling, and mesh refinement on flow and transport properties in fractured porous media. We generated a set of generic three-dimensional discrete fracture networks at various densities, where the radii of the fractures were sampled from a truncated power-law distribution, and whose parameters were loosely based on field site characterizations. We also considered five network densities, which were defined using a dimensionless version of density based on percolation theory. Once the networks were generated, we upscaled them into a single continuum model using the upscaled discrete fracture matrix model presented by Sweeney et al. (2019). We considered steady, isothermal pressure-driven flow through each domain and then simulated conservative, decaying, and adsorbing tracers using a pulse injection into the domain. For each simulation, we calculated the effective permeability and solute breakthrough curves as quantities of interest to compare between network realizations. We found that selecting a mesh resolution such that the global topology of the upscaled mesh matches the fracture network is essential. If the upscaled mesh has a connected pathway of fracture (higher permeability) cells but the fracture network does not, then the estimates for effective permeability and solute breakthrough will be incorrect. False connections cannot be eliminated entirely, but they can be managed by choosing appropriate mesh resolution and refinement for a given network. Adopting octree meshing to obtain sufficient levels of refinement leads to fewer computational cells (up to a 90% reduction in overall cell count) when compared to using a uniform resolution grid and can result in a more accurate continuum representation of the true fracture network.
PubDate: 2023-10-13
- A preliminary model for optimal control of moisture content in unsaturated
soils-
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Abstract: Abstract In this paper we introduce an optimal control approach to Richards’ equation in an irrigation framework, aimed at minimizing water consumption while maximizing root water uptake. We first describe the physics of the nonlinear model under consideration, and then develop the first-order necessary optimality conditions of the associated boundary control problem. We show that our model provides a promising framework to support optimized irrigation strategies, thus facing water scarcity in irrigation. The characterization of the optimal control in terms of a suitable relation with the adjoint state of the optimality conditions is then used to develop numerical simulations on different hydrological settings, that support the analytical findings of the paper.
PubDate: 2023-10-09
- Fracture network flow prediction with uncertainty using physics-informed
graph features-
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Abstract: Abstract The inherent uncertainty of subsurface fracture characteristics requires an ensemble-based approach where multiple network realizations are generated to represent a single physical system. However, the computational cost of these simulations is often prohibitive for carrying out an adequate number of simulations to obtain stable statistics for many quantities of interest, including the first passage time distribution (FPTD) of particles passing through the system. We characterize how variability induced by stochastic representations of subsurface fracture networks propagates into the FPTD. We simulate flow and transport on a large ensemble of three-dimensional fracture networks, observe the quantiles of the first passage time distributions, and characterize the network structure using coarse graph-based representations. Through analysis of the first passage times and graphs, we identify key geostructural features which explain variation in the FPTD. These features integrate hydrological fracture properties (permeability) with topological attributes. Using these features, we fit both parametric and nonparametric regression models to predict FPTD with uncertainty, compare the relative performance of each model in terms of error and coverage, and discuss possible model extensions. Models are validated using a held-out set of networks independently generated under different parameter settings. These nonparametric regression models can flexibly account for nonlinear relationships while allowing prediction intervals to adjust accurately depending on input feature values.
PubDate: 2023-10-03
- The non-monotonicity of growth rate of viscous fingers in heterogeneous
porous media-
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Abstract: Abstract The paper presents a stochastic analysis of the growth rate of viscous fingers in miscible displacement in a heterogeneous porous medium. The statistical parameters characterizing the permeability distribution of a reservoir vary over a wide range. The formation of fingers is provided by the mixing of different-viscosity fluids — water and polymer solution. The distribution functions of the growth rate of viscous fingers are numerically determined and visualized. Careful data processing reveals the non-monotonic nature of the dependence of the front end of the mixing zone on the correlation length of the permeability of the reservoir formation. It is demonstrated that an increase in correlation length up to a certain value causes an expansion of the distribution shape and a shift of the distribution maximum to the region of higher velocities. In addition, an increase in the standard deviation of permeability leads to a slight change in the shape and characteristics of the density distribution of the growth rates of viscous fingers. The theoretical predictions within the framework of the transverse flow equilibrium approximation and the Koval model are contrasted with the numerically computed velocity distributions.
PubDate: 2023-10-01
- A new computational model for karst conduit flow in carbonate reservoirs
including dissolution-collapse breccias-
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Abstract: Abstract We construct a new computational model to describe coupled 3D/1D flow in carbonate rocks intertwined by a network of karst cave conduits. The proposed approach shows ability to incorporate pointwise velocity-dependent jumps in the pressure field arising from localized partial obstructions due to the presence of collapse-breccia within the discrete conduit network. At the microscale, we postulate single phase viscous flow governed by the Navier-Stokes equations in the conduit network coupled with Darcian flow in the rock matrix and supplemented by transmission conditions at the common interface. Subsequently, we proceed by constructing a sharper lower-dimensional reduced model wherein, in addition to the usual high geometric aspect ratio between the length and hydraulic diameter of the cave system, we introduce an additional small parameter containing the ratio between the localized width of the perturbed flow region, in the vicinity of each breccia, and characteristic length of the network. The asymptotic behavior gives rise to a coupled mixed-dimensional flow, where 1D sub-manifolds appear embedded in the 3D carbonate matrix with coupling ruled by a mass exchange line-source \(\delta \) -function, acting synergistically with discrete non-linear pressure jumps of Robin type at the discrete set of breccia locations. The mixed-dimensional flow equations are discretized by a locally conservative extended version of the mixed-hybrid finite element method, showing capability of incorporating the new non-linear discrete transmission jump conditions between elements adjacent to the breccias. Computational simulations are performed for particular configurations of well/karst conduit systems, illustrating the influence of the karst and breccia upon the flow regimes, streamline patterns and well productivity.
PubDate: 2023-10-01
- Global sensitivity analysis using multi-resolution polynomial chaos
expansion for coupled Stokes–Darcy flow problems-
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Abstract: Abstract Determination of relevant model parameters is crucial for accurate mathematical modelling and efficient numerical simulation of a wide spectrum of applications in geosciences. The conventional method of choice is the global sensitivity analysis (GSA). Unfortunately, at least the classical Monte-Carlo based GSA requires a high number of model runs. Response surfaces based techniques, e.g. arbitrary Polynomial Chaos (aPC) expansion, can reduce computational effort, however, they suffer from the Gibbs phenomena and high hardware requirements for higher accuracy. We introduce GSA for arbitrary Multi-Resolution Polynomial Chaos (aMR-PC) which is a localized aPC based data-driven polynomial discretization. The aMR-PC allows to reduce the Gibbs phenomena by construction and to achieve higher accuracy by means of localization also for lower polynomial degrees. We apply these techniques to perform the sensitivity analysis for the Stokes–Darcy problem which describes fluid flow in coupled free-flow and porous-medium systems. We consider the Stokes equations in the free-flow region, Darcy’s law in the porous-medium domain and the classical interface conditions across the fluid–porous interface including the conservation of mass, the balance of normal forces and the Beavers–Joseph condition for the tangential velocity. This coupled problem formulation contains four uncertain parameters: the exact location of the interface, the permeability, the Beavers–Joseph slip coefficient and the uncertainty in the boundary conditions. We carry out the sensitivity analysis of the coupled model with respect to these parameters using the Sobol indices on the aMR-PC expansion and conduct the corresponding numerical simulations.
PubDate: 2023-10-01
- An extended peridynamic bond-based constitutive model for simulation of
crack propagation in rock-like materials-
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Abstract: Abstract The stress of rock-like materials first increases and then decreases with an increase in the strain, and finally become damaged under tensile or compressive loads. It is not suitable to use the traditional bond-based peridynamics model to simulate the crack propagation of rock-like materials. Based on the bond-based peridynamics theory, a constitutive model has here been proposed that can reflect the characteristic of the stress with an increase in strain (i.e., that the stress of the rock-like materials first increases and then decreases, and finally fails). This makes up for the weakness with the traditional bond-based peridynamics theory, which fails to reflect the stress-strain change in rock-like materials. The strain energy density of the proposed constitutive model of rock-like materials has been derived and compared against the classical elasticity theory to obtain the model constants with respect to the elasticity moduli for plane stress conditions. Based on the here proposed constitutive model for rock-like materials, a numerical solution program for rock-like materials has been written using the Fortran language. For different loading conditions, the crack propagation process for an intact specimen has been simulated and compared with experimental results. This was also the situation for a specimen with a pre-existing flaw, a single non-straight flaw, and with three pre-existing flaws for plane stress conditions. The numerical simulation results were in good agreement with the corresponding experimental results. By using this proposed constitutive model, it was possible to simulate and predict the mechanical properties of rock-like materials, and the process of crack initiation, propagation, and coalescence under different loading conditions. The results from the present study can thus provide a reference for practical engineering.
PubDate: 2023-10-01
- Modeling 3-D anisotropic elastodynamics using mimetic finite differences
and fully staggered grids-
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Abstract: Abstract Accurate modeling of elastic wavefields in 3-D anisotropic media is important for many seismic processing and inversion applications. However, efficient wavefield simulation for tilted transversely isotropic (TTI) media and, especially, for orthorhombic and lower symmetries remains challenging. Finite-difference (FD) implementations using centered Taylor-series coefficients on singly staggered grids suffer from reduced numerical accuracy due to problems in computing the partial wavefield derivatives in TTI or tilted orthorhombic (TOR) media, as well as in enforcing the free-surface (zero-traction) boundary conditions. To address these issues, we develop a 3-D mimetic FD (MFD) algorithm for arbitrarily anisotropic media that uses a fully-staggered-grid strategy. This CUDA-based algorithm is implemented on graphics processing units (GPUs) to leverage the massive parallelism of this computer architecture. For multi-GPU parallelization, we employ the CUDA-aware message passing interface (MPI) library to exploit the remote direct memory access (RDMA) feature for buffer transfers. Weak- and strong-scaling tests on up to eight DGX NVIDIA A100 nodes (64 GPUs in total) demonstrate that the proposed multi-GPU implementation achieves a quasi-linear computational speedup with over 98% efficiency for large industrial-scale models of size in excess of 1.7 \(\times \) 10 \(^{10}\) grid points.
PubDate: 2023-10-01
- Hybrid Neural Network - Variational Data Assimilation algorithm to infer
river discharges from SWOT-like data-
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Abstract: Abstract Estimating discharges Q(x, t) from altimetric measurements only, for ungauged rivers (in particular, those with unknown bathymetry b(x)), is an ill-posed inverse problem. We develop here an algorithm to estimate Q(x, t) without prior flow information other than global open datasets. Additionally, the ill-posedness feature of this inverse problem is re-investigated. Inversions based on a Variational Data Assimilation (VDA) approach enable accurate estimation of spatio-temporal variations of the discharge, but with a bias scaling the overall estimate. This key issue, which was already highlighted in our previous studies, is partly solved by considering additional hydrological information (the drainage area, \(A\ (km^2)\) ) combined with a Machine Learning (ML) technique. Purely data-driven estimations obtained from an Artificial Neural Network (ANN) provide a reasonably good estimation at a large scale ( \(\approx 10^3\) m). This first estimation is then employed to define the first guess of an iterative VDA algorithm. The latter relies on the Saint-Venant flow model and aims to compute the complete unknowns (discharge Q(x, t), bathymetry b(x), friction coefficient K(x, t)) at a fine scale (approximately \(10^2\) m). The resulting complete inversion algorithm is called the H2iVDI algorithm for "Hybrid Hierarchical Variational Discharge Inference". Numerical experiments have been analyzed for 29 heterogeneous worldwide river portions. The obtained estimations present an overall bias (less than 30% for rivers with similar characteristics than those used for calibration) smaller than previous results, with accurate spatio-temporal variations of the flow. After a learning period of the observed rivers (e.g. one year), the algorithm provides two complementary estimators: a dynamic flow model enabling estimations at a fine scale and spatio-temporal extrapolations, and a low complexity estimator (based on a dedicated algebraic low Froude flow model). This last estimator provides reasonably accurate estimations (less than 30% for considered rivers) at a large scale from newly acquired WS measurements in real-time, therefore making it a potentially operational algorithm.
PubDate: 2023-10-01
- Hard enforcement of physics-informed neural network solutions of acoustic
wave propagation-
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Abstract: Abstract Simulating the temporal evolution of wavefield solutions through models with heterogeneous material properties is of practical interest for many scientific applications. The acoustic wave equation (AWE) is often used for studying wave propagation in both fluids and solids and is crucial for many applications including seismic imaging and inversion and non-destructive testing. Because analytical AWE solutions rarely exist for complex heterogeneous media, methods for generating numerical AWE solutions are very desirable. Traditional numerical solvers require discrete model representations with many restrictions placed on the shape and spacing of grid elements. This work uses a relatively new class of numerical solvers known as physics-informed neural networks (PINNs) that provide a mesh-free alternative for generating AWE solutions using a deep neural-network framework. We encapsulate a time-domain AWE formulation within a loss function that is used to train network parameters. The initial conditions are implemented by enforcing hard constraints on the neural network instead of including them as separate loss-function terms. We also use a Fourier neural network (FNN) to alleviate the spectral bias commonly observed when using fully connected neural network in the conventional PINN approach. Numerical tests on both 2D homogeneous and heterogeneous velocity models confirm the accuracy of our approach. We observe that using FNNs helps in the convergence of AWE solutions especially for heterogeneous models. We compare PINN-based solutions with those computed by the highly accurate conventional pseudo-spectral method, and observe that the normalized energy differences between the two sets of solutions were less than 4% for all numerical tests.
PubDate: 2023-10-01
- Binary well placement optimization using a decomposition-based
multi-objective evolutionary algorithm with diversity preservation-
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Abstract: Abstract In binary multi-objective well placement optimization, multiple conflicting objective functions must be optimized simultaneously in reservoir simulation models containing discrete decision variables. Although multi-objective algorithms have been developed or adapted to tackle this scenario, such as the derivative-free evolutionary algorithms, these methods are known to generate a high number of duplicated strategies in discrete problems. Duplicated strategies negatively impact the optimization process since they: (i) degrade the efficiency of recombination operators in evolutionary algorithms; (ii) slow the convergence speed as they require more iterations to find a well-distributed set of strategies; and (iii) perform unnecessary re-evaluations of previously seen strategies through reservoir simulation. To perform multi-objective well placement optimization while avoiding duplicated strategies, this paper investigates the application of a newly proposed algorithm named MOEA/D-NFTS, with a modified diversity preservation mechanism that incorporates prior knowledge of the problem, on a multi-objective well placement optimization problem. The proposed methodology is evaluated on the UNISIM-II-D benchmark case, a synthetic carbonate black-oil simulation model in a well placement optimization problem using a binary strategy representation, indicating the presence or absence of a given candidate well position in the final strategy. The objective functions are the maximization of the Net Present Value, the maximization of the Cumulative Oil Production, and the minimization of Cumulative Water Production. The modified MOEA/D-NFTS performance is compared with a baseline algorithm without diversity preservation, and the evidence shows that the MOEA/D-NFTS produces statistically significant superior results, and is suitable for binary multi-objective well placement optimization.
PubDate: 2023-10-01
- Adaptive mesh refinement in locally conservative level set methods for
multiphase fluid displacements in porous media-
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Abstract: Abstract Multiphase flow in porous media often occurs with the formation and coalescence of fluid ganglia. Accurate predictions of such mechanisms in complex pore geometries require simulation models with local mass conservation and with the option to improve resolution in areas of interest. In this work, we incorporate patch-based, structured adaptive mesh refinement capabilities into a method for local volume conservation that describes the behaviour of disconnected fluid ganglia during level set simulations of capillary-controlled displacement in porous media. We validate the model against analytical solutions for three-phase fluid configurations in idealized pores containing gas, oil, and water, by modelling the intermediate-wet oil layers as separate domains with their volumes preserved. Both the pressures and volumes of disconnected ganglia converge to analytical values with increased refinement levels of the adaptive mesh. Favourable results from strong and weak scaling tests emphasize that the number of patches per processor and the total number of patches are important parameters for efficient parallel simulations with adaptive mesh refinement. Simulations of two-phase imbibition and three-phase gas invasion on segmented 3D images of water-wet sandstone show that adaptive mesh refinement has the highest impact on three-phase displacements, especially concerning the behaviour of the conserved, intermediate-wet phase.
PubDate: 2023-10-01
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