Forward Simulation Method Research Articles

A Deep Learning Structure Correction Method Under the Influence of Special Lithology Body

Abstract In the process of precise exploration and development of oil and gas reservoirs, the accurate implementation of the target layer structure plays a vital role in the increase of oil reserves and the success of drilling. However, with the gradual deepening of the research, it is found that in the overlying strata of oil and gas reservoirs, special lithological bodies with strong heterogeneity and abnormal velocity are usually locally developed, which directly affects the construction of seismic data migration velocity, resulting in structural distortion at the local position of the reservoir in the process of data interpretation. Focusing on the structural change caused by the abnormal velocity, the deep learning algorithm is used to accurately predict the velocity of the special lithologic body and realize the accurate correction of the structure. Firstly, based on the combination of seismic and geological understanding, the geological model of the target layer and the overlying special lithologic body is constructed, and the distribution law of the special lithologic body is identified by the forward simulation method. Then use the well-side seismic trace and P-wave velocity curve to train the velocity data through the deep learning method and apply the training results to the post-stack seismic data to obtain the accurate P-wave velocity body. Finally, the real formation velocity over the target layer is restored to achieve reservoir structure correction. Based on this method, the research experiment was carried out in the M block of the Tarim Basin, and the spatial distribution range of the special lithology body was carved. The data error between the migration velocity body and the abnormal high-velocity body was calculated, and the target layer structure in the study area was corrected.

Measurement of Diffusion Coefficients and Assessment of Atomic Mobilities in HCP Mg-Ag-Sn Alloys

Reliable thermodynamic and kinetic databases are essential for designing and developing novel magnesium (Mg) alloys for elevated-temperature applications in the automotive and aerospace industries. This study investigated diffusion behaviors of HCP Mg-Ag-Sn alloys by four sets of three diffusion couples annealed at 450, 500, and 550 °C, respectively. Interdiffusion and impurity diffusion coefficients at the three annealing temperatures were extracted using the forward-simulation method. The determined diffusion coefficients, combined with prior results in the literature, were then used to assess atomic mobilities for the HCP phase of the Mg-Ag-Sn system. This assessment was conducted using the DICTRA and the TCMG5 thermodynamic database within the Thermo-Calc software. Comprehensive comparisons between calculated and experimental diffusion coefficients yielded an excellent agreement. The atomic mobilities were also further validated by appropriate predictions of concentration profiles and diffusion paths observed in independent diffusion couple experiments performed at 525 °C for 18 hrs. It has been found that the addition of Sn and Ag both increase the diffusivity of Ag in HCP Mg and Sn in HCP Mg in the stable temperature range of HCP Mg, respectively. This improved understanding of the kinetics in the Mg-Ag-Sn ternary system is essential for controlling diffusion-dependent properties such as coarsening and, therefore, the mechanical properties of Mg-Ag-Sn alloys at elevated temperatures.

A velocity evaluation method for in-pipe metallic flow through inversion of magnetic field perturbation measured surrounding the pipe

It is important to measure the global and/or local velocity of an in-pipe metallic flow to control its running state in applications such as a Tokamak fusion reactor. The magnetic field outside the pipe wall will be perturbed by the motion induced eddy current when the liquid metal flows across an applied static magnetic field. This phenomenon gives a possibility to evaluate the in-pipe velocity from the measured magnetic field perturbation signals. In this paper, a non-intrusive velocity evaluation method is proposed accordingly for measuring the velocity of liquid metal through measurement and inversion of the magnetic field surrounding the pipe. An efficient forward simulation method to calculate the magnetic field near a metallic flow in a static environmental magnetic field is developed at first. An inversion scheme based on the singular value decomposition and the L-curve method is then proposed to reconstruct the velocity distribution at a pipe cross-section with the linear equations correlating the flow velocity and the magnetic field regulated using the Tikhonov method. The reconstruction results of pipe flows of different velocity modes verified the feasibility and efficiency of the proposed velocity measurement method for in-pipe metallic flows.

Interactive Forward and Inversion Scheme‐Based Anisotropic Correction for Shale Gas Horizontal Well Sonic Logs and Its Application

Well logging interpretation and evaluation of reservoirs in horizontal wells plays an important role in shale gas exploration and development. The accuracy of the interpreted petrophysical parameters is directly affected by well logging curves. This study focuses on a horizontal well targeting deep shale gas layers in the Luzhou block, Southern Sichuan Basin. It employs an interactive forward and inversion scheme to precisely reconstruct the borehole trajectory using a comprehensive dataset encompassing well logs, mud logs, and seismic data. The research introduces a method for anisotropic correction of sonic log, crucial for calculating reservoir parameters, and assess payzone targeting in the horizontal well. The research results show that the interactive forward and inverse simulation method can achieve the recovery of horizontal well borehole trajectory and finely delineate the sublayers, improving the evaluation accuracy of platinum target drilling encounter ratio by over 6%. Numerically simulated acoustic wave can be used to carry out the anisotropy correction of acoustic wave in horizontal wells and calculate reservoir parameters, reducing the relative error in computed porosity to 1.72%. The comprehensive logging evaluation results from both geological and engineering aspects provide the data basis for planning the fracturing program of the shale gas horizontal well. This research has established a new method and idea for well logging evaluation in shale gas horizontal wells, marking a significant step toward integrating geological and engineering aspects in shale gas exploration.

Inversion of the Seepage Parameters of Earth/Rockfill Dams Considering the Coupling Effect of Seepage and Thermal Transfer

The estimation of inversion parameters for seepage models is very important for ensuring the safety of earth/rockfill dams. In most existing studies, only hydraulic characteristics have been determined based on seepage pressure or discharge observation information, which may result in inversion results that do not accurately characterize the true properties of dam materials. In this paper, an advanced parameter inversion method considering the coupling effect of seepage and thermal transfer is proposed. A seepage–thermal transfer coupled finite element model is established, and hydraulic head and temperature data of monitoring points are obtained through the forward simulation method. The entropy weight (EW) method is used to preprocess the hydraulic head and temperature data, and a universal kriging (UK) surrogate model is established to replace the time-consuming finite element simulations. The multi-island genetic algorithm (MIGA) is applied to find the optimal solution, thereby obtaining the inversion results. The effectiveness and accuracy of the proposed method are verified through laboratory tests involving seepage and thermal transfer monitoring of a core rockfill dam. Joint inversion based on temperature and hydraulic head observation information can result in improvement in the accuracy of the inversion results.

Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies

The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. “Superspreading,” or “over-dispersion,” is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities given data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

Progressive Simulation for Cloth Quasistatics

The trade-off between speed and fidelity in cloth simulation is a fundamental computational problem in computer graphics and computational design. Coarse cloth models provide the interactive performance required by designers, but they can not be simulated at higher resolutions ("up-resed") without introducing simulation artifacts and/or unpredicted outcomes, such as different folds, wrinkles and drapes. But how can a coarse simulation predict the result of an unconstrained, high-resolution simulation that has not yet been run? We propose Progressive Cloth Simulation (PCS), a new forward simulation method for efficient preview of cloth quasistatics on exceedingly coarse triangle meshes with consistent and progressive improvement over a hierarchy of increasingly higher-resolution models. PCS provides an efficient coarse previewing simulation method that predicts the coarse-scale folds and wrinkles that will be generated by a corresponding converged, high-fidelity C-IPC simulation of the cloth drape's equilibrium. For each preview PCS can generate an increasing-resolution sequence of consistent models that progress towards this converged solution. This successive improvement can then be interrupted at any point, for example, whenever design parameters are updated. PCS then ensures feasibility at all resolutions, so that predicted solutions remain intersection-free and capture the complex folding and buckling behaviors of frictionally contacting cloth.

Simulation of Rock Complex Resistivity Using an Inversion Method

The complex resistivity of coal and related rocks contains abundant physical property information, which can be indirectly used to study the lithology and microstructure of these materials. These aspects are closely related to the fluids inside the considered coal rocks, such as gas, water and coalbed methane. In the present analysis, considering different lithological structures, and using the Cole-Cole model, a forward simulation method is used to study different physical parameters such as the zero-frequency resistivity, the polarizability, the relaxation time, and the frequency correlation coefficient. Moreover, using a least square technique, a complex resistivity “inversion” algorithm is written. The comparison of the initial model parameters and those obtained after inversion is used to verify the stability and accuracy of such approach. The method is finally applied to primary-structure coal considered as the experimental sample for complex resistivity measurements.

Computational inverse design of surface-based inflatables

We present a computational inverse design method for a new class of surface-based inflatable structure. Our deployable structures are fabricated by fusing together two layers of inextensible sheet material along carefully selected curves. The fusing curves form a network of tubular channels that can be inflated with air or other fluids. When fully inflated, the initially flat surface assumes a programmed double-curved shape and becomes stiff and load-bearing. We present a method that solves for the layout of air channels that, when inflated, best approximate a given input design. For this purpose, we integrate a forward simulation method for inflation with a gradient-based optimization algorithm that continuously adapts the geometry of the air channels to improve the design objectives. To initialize this non-linear optimization, we propose a novel surface flattening algorithm. When a channel is inflated, it approximately maintains its length, but contracts transversally to its main direction. Our algorithm approximates this deformation behavior by computing a mapping from the 3D design surface to the plane that allows for anisotropic metric scaling within the bounds realizable by the physical system. We show a wide variety of inflatable designs and fabricate several prototypes to validate our approach and highlight potential applications.

Empirical Models of Industry Dynamics with Endogenous Market Structure

This article reviews recent developments in the study of firm and industry dynamics, with a special emphasis on the econometric endogeneity of market structure. The endogeneity of market structure follows from the presence of serially correlated unobservable shocks to the profitability of firms’ dynamic decisions, a feature common to many empirical settings. Methods that ignore endogeneity can lead to misleading parameter estimates and misleading counterfactual results. We pay particular attention to extensions of standard two-step methods that leverage instrumental variables to address endogeneity in both single-agent and oligopoly models. A first step set-identifies dynamic policy functions together with serial correlation parameters, and a second step quickly solves for profit function parameters using an extension of existing forward-simulation methods. We discuss how these new methods provide a general solution to initial-conditions problems and how they can yield practical estimation strategies.

Boosting evolutionary algorithm configuration

Algorithm configuration has emerged as an essential technology for the improvement of high-performance solvers. We present new algorithmic ideas to improve state-of-the-art solver configurators automatically by tuning. Particularly, we introduce 1. a forward-simulation method to improve parallel performance, 2. an improvement to the configuration process itself, and 3. a new technique for instance-specific solver configuration. Extensive experimental results show that the new solver configurator compares very favorably with the state-of-the-art in automatic configuration for combinatorial solvers.

Empirical Models of Industry Dynamics with Endogenous Market Structure

This article reviews recent developments in the study of firm and industry dynamics, with a special emphasis on the econometric endogeneity of market structure. The endogeneity of market structure follows from the presence of serially correlated unobservable shocks to the profitability of firms’ dynamic decisions, a feature common to many empirical settings. Methods that ignore endogeneity can lead to misleading parameter estimates and misleading counterfactual results. We pay particular attention to extensions of standard two-step methods that leverage instrumental variables to address endogeneity in both single-agent and oligopoly models. A first step set-identifies dynamic policy functions together with serial correlation parameters, and a second step quickly solves for profit function parameters using an extension of existing forward-simulation methods. We discuss how these new methods provide a general solution to initial-conditions problems and how they can yield practical estimation strategies.

Unbiased simulation method with the poisson kernel method for stochastic differential equations with reflection

We consider unbiased simulation methods for one-dimensional stochastic differential equations with reflection at zero. In particular, we propose improvements of the forward unbiased simulation method provided by Alfonsi et al. (Parametrix methods for one-dimensional reflected SDEs. Modern problems of stochastic analysis and statistics: selected contributions in honor of Valentin Konakov. Springer, pp 43–66, 2017). In this paper, we will apply the Poisson kernel method to improve the negativity and high variance problems of the associated simulation method. We also discuss some choices for the behavior of the approximation process near the boundary. This improvement is demonstrated through some numerical experiments.

Reconstruction of Remotely Sensed Snow Albedo for Quality Improvements Based on a Combination of Forward and Retrieval Models

Snow albedo plays an important role in the global climate system. There are notable missing data and error uncertainties in the current remote sensing snow albedo products that are attributed to the limits of remote-sensing technology. Due to the uncertainties of meteorological factors and the differences in various forward model simulation methods, snow albedo forward simulations also have considerable uncertainties. This paper suggests a long-time-series reconstruction of snow albedo utilizing a forward radiation-transferring model and a remote-sensing retrieval model together with multisource remotely sensed data and meteorological data. The key to this paper is to estimate snow information for areas lacking data utilizing a forward model for snow albedo with clear physical mechanisms. The estimated snow information can be used as reliable data for snow albedo reconstructions. The results indicate that the long time series of snow albedo data obtained by coupling the snow albedo retrieval model and forward simulation model is highly accurate. The mean absolute error, root mean square error, Pearson’s correlation coefficient (R), and Nash–Sutcliffe efficiency coefficient of the observed and reconstructed snow albedos are 0.11, 0.14, 0.79, and 0.69, respectively. The reconstructed snow albedo data are underestimated by only 11% relative to the in situ snow surface albedo measurements. In the alpine mountain regions, the proposed method has a simulation accuracy that is 6% greater than that of the MOD10A1 SAD. This paper provides an effective reconstruction solution that improves the accuracy of estimations of snow albedo and fills gaps in the data.

S 1 New methods for electrode optimization for high-definition transcranial electric stimulation (hd-tES)

Background/problem description tES is a non-invasive brain stimulation technique which modifies neural excitability by providing weak currents through scalp electrodes. The aim of my presentation is to introduce and analyze new forward and inverse simulation methods for safe and well-targeted multi-electrode tES. Methods Continuous Galerkin Finite Element Method (CG-FEM) simulations will be reviewed as current standard tES forward simulation approach ( Wagner et al., 2014 ). The tES forward problem is then related to the Electroencephalography (EEG) forward problem using Helmholtz principle of reciprocity ( Wagner et al., 2016 ) and a new Unfitted Discontinuous Galerkin FEM (UDG-FEM) approach for both EEG and tES forward problem will be presented ( Nusing et al., 2016 ). UDG-FEM combines the advantages of a FEM approach on a structured hexahedral mesh with implicit surface representations given by level set functions. Finally, a new multi-electrode tES optimization approach is presented that uses the alternating direction method of multipliers (ADMM) for optimizing the focality, orientation and intensity of current density at the target location, while minimizing current density in the remaining brain ( Wagner et al., in press ). Results CG-FEM simulations show that standard bipolar electrode montages induce a very widespread current flow field with the strongest intensities often located in non-target brain regions. The results will also point out the limitations of such standard CG-FEM simulation approaches with regard to so-called skull leakage problems, which are alleviated using UDG-FEM to appropriately model smooth tissue surfaces. The electrode optimization results in a highly realistic six-compartment geometry-adapted hexahedral head model reveal that the optimized current flow fields show significantly higher focality and, in most cases, higher directional agreement to the target vector in comparison to standard bipolar electrode montages. The new ADMM optimization approach will also be compared to other optimization approaches from the literature. Discussion/conclusion The new ADMM hd-tES electrode optimization approach based on UDG-FEM realistic head modeling offers an innovative new approach to calculate individually optimized electrode currents. In combination with hd-tES hardware, it bears the potential to significantly increase effects of non-invasive tES stimulation.

A quantitative method for assessing home envelope's energy performance using measured data

This research develops a unique inverse method for assessing a residential home envelope's energy performance based on measured data. The method is built on the balancing principle that the heat that is generated by the heating equipment equals the heat that is lost to the outdoor environment through the building's envelope. This heat generation vs. loss balancing process continues over time, being characterized by the heating equipment's operating cycles. An actual home in the cold climate zone is used as a case study with outdoor temperature varying between 12.9 and 57.6°F. The obtained results show that the given home's heat loss is linearly correlated to the indoor‒outdoor temperature difference. They also indicate that measured data can provide reliable information for assessing and benchmarking a building envelope's energy performance. In comparison to a forward building energy modelling and simulation method, the proposed inverse method can provide accurate results, with much less effort needed for an existing building.

Measurement of interdiffusion and impurity diffusion coefficients in the bcc phase of the Ti–X (X = Cr, Hf, Mo, Nb, V, Zr) binary systems using diffusion multiples

The design and development of novel titanium alloys for structural and biomedical applications require reliable thermodynamic and kinetic databases. In this study, diffusion behaviors of six Ti–X (X = Cr, Hf, Mo, Nb, V, Zr) binary systems were systematically investigated at temperatures from 800 to 1200 °C using a set of five Ti–TiAl–Cr–Hf–Mo–Nb–V–Zr diffusion multiples. Concentration profiles of the six Ti–X binary systems were collected from binary regions of the diffusion multiples using electron probe microanalysis (EPMA). Both interdiffusion and impurity (dilute) diffusion coefficients in the Ti-rich bcc phase of these systems were extracted from the concentration profiles using the forward-simulation method. Twenty impurity diffusion coefficients of all the six elements in bcc Ti as well as Ti in bcc Zr at different temperatures obtained from this study are in excellent agreement with the literature data. The interdiffusion coefficients obtained from this study are also in good agreement with previous literature results. The large amount of new experimental data obtained from this study will be essential for establishing the mobility databases for the design and development of advanced titanium alloys.

Inverse modeling of thin layer flow cells for detection of solubility, transport and reaction coefficients from experimental data

Thin layer flow cells are used in electrochemical research as experimental devices which allow to perform investigations of electrocatalytic surface reactions under controlled conditions using reasonably small electrolyte volumes. The paper introduces a general approach to simulate the complete cell using accurate numerical simulation of the coupled flow, transport and reaction processes in a flow cell. The approach is based on a mass conservative coupling of a divergence-free finite element method for fluid flow and a stable finite volume method for mass transport. It allows to perform stable and efficient forward simulations that comply with important physical constraints, namely mass conservation and maximum principles for the involved species. In this context, several recent approaches to obtain divergence-free velocity approximations from finite element simulations are discussed. In order to perform parameter identification, the forward simulation method is coupled to standard optimization tools. After an assessment of the inverse modeling approach using hypothetical, but realistic data, first results of the identification of solubility and transport data for O2 dissolved in organic electrolytes are presented. A plausibility study for a more complex situation with surface reactions concludes the paper and shows possible extensions of the presented numerical tools.

An inverse method for flue gas shielded metal surface temperature measurement based on infrared radiation

The infrared temperature measurement technique has been applied in various fields, such as thermal efficiency analysis, environmental monitoring, industrial facility inspections, and remote temperature sensing. In the problem of infrared measurement of the metal surface temperature of superheater surfaces, the outer wall of the metal pipe is covered by radiative participating flue gas. This means that the traditional infrared measurement technique will lead to intolerable measurement errors due to the absorption and scattering of the flue gas. In this paper, an infrared measurement method for a metal surface in flue gas is investigated theoretically and experimentally. The spectral emissivity of the metal surface, and the spectral absorption and scattering coefficients of the radiative participating flue gas are retrieved simultaneously using an inverse method called quantum particle swarm optimization. Meanwhile, the detected radiation energy simulated using a forward simulation method (named the source multi-flux method) is set as the input of the retrieval. Then, the temperature of the metal surface detected by an infrared CCD camera is modified using the source multi-flux method in combination with these retrieved physical properties. Finally, an infrared measurement system for metal surface temperature is built to assess the proposed method. Experimental results show that the modified temperature is closer to the true value than that of the direct measured temperature.

Meandering channel sandstone architecture characterization based on architecture seismic forward simulation

To improve the accuracy of the meandering channel sandstone reservoir characterization in offshore oilfield, this article completes the meandering channel sandstone reservoir architecture characterization of the NmII-3 layer of Low Minghuazhen Group in the Qinhuangdao 32-6 oilfield by the architecture-guided seismic forward simulation based on the meandering river depositional mechanism. By the research mentioned above, the seismic forward simulation identification criteria of composited meandering channels boundaries are summarized that when the mud layer thickness is greater than or equal to 1 m and the channel sandstone thickness is between 8 and 9 m, the mud layer between composite meandering channels can be identified. The seismic forward simulation identification criteria of single meandering channels boundaries are summarized that the elevation difference which is greater than or equal to 5 m can be identified while the channel sandstone is greater than or equal to 7 m. And the seismic forward simulation waveform features of elevation difference of channels, small sandstone among channels, and abandoned channel are summarized. The seismic waveform of elevation difference has two side-by-side troughs. The seismic crest center position of large channel sandstone is lower than the seismic crest center position of the small sandstone. The seismic crest center falls slowly and rises quickly from convex bank to concave bank in abandoned channel laterally. Finally, the architecture-guided seismic forward simulation method is established and the applying criteria are summarized in this paper. And the accuracy of the meandering channel sandstone architecture characterization with well logs and seismic data is improved.