Advances in Water Resources最新文献

{"title":"Fully implicit bound-preserving discontinuous Galerkin algorithm with unstructured block preconditioners for multiphase flows in porous media","authors":"Jiali Tu ,&nbsp;Haijian Yang ,&nbsp;Rongliang Chen ,&nbsp;Li Luo","doi":"10.1016/j.advwatres.2024.104844","DOIUrl":"10.1016/j.advwatres.2024.104844","url":null,"abstract":"<div><div>Massively parallel simulation of multiphase flows in porous media is challenging due to the highly nonlinear governing equations with the complexity of various modeling features and the significant heterogeneity of material coefficients. In this paper, we introduce a fully implicit discontinuous Galerkin (DG) reservoir simulator designed for parallel computers to handle large-scale multiphase flow simulations. Our approach formulates a fully implicit DG scheme on 3D unstructured grids to discretize the nonlinear governing equations, which take into account capillary effects, permeability heterogeneity, gravity, and injection/production wells. A vertex-based slope limiter within the fully implicit DG framework is adopted to suppress oscillations near discontinuities and ensure the boundedness of the solution. We address the resulting nonlinear systems from the fully implicit discretization using a family of inexact Newton–Krylov algorithms with an analytic Jacobian. Moreover, we employ an unstructured block-type preconditioner with an efficient overlapping additive Schwarz approximation to accelerate the convergence of iterations and enhance the scalability of the simulator. Large-scale simulations of both benchmark and realistic reservoir problems on 3D unstructured grids are conducted to demonstrate the efficiency and scalability of the proposed reservoir simulator.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"194 ","pages":"Article 104844"},"PeriodicalIF":4.0,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling air sparging analysis in flow cells through the continuum approach","authors":"Ilan Ben-Noah","doi":"10.1016/j.advwatres.2024.104841","DOIUrl":"10.1016/j.advwatres.2024.104841","url":null,"abstract":"<div><div>We present various analytical solutions for a two-dimensional steady air source that demonstrate classical flow physics’ usefulness in air-sparging evaluation. These solutions capture different flow geometry and setup effects while offering accuracy, speed, and a deeper understanding of governing physics. Compared to empirical models, they excel with fewer physical parameters. We validate their accuracy by comparing these solutions to experimental air-sparging data from two-dimensional flow cells. This comparison underscores the applicability of the physical model and establishes a relationship between grain size and a key model parameter. Furthermore, this analysis enables predictive capabilities for scaled-up systems with diverse setup geometries.</div><div><strong>Plain Language Summary</strong> Air-sparging refers to the injection of air below the groundwater table, that is, to the water-saturated section of the aquifer. Air-sparging is used to facilitate the volatilization of organic pollutants (e.g., solvents, gasoline) and their extraction to the soil surface. However, evaluating and modeling the flow and distribution of air is limited by the complicated physics of unstable multiphase flow. These complexities drive researchers to search for empirical relations and rules of thumb to design air-sparging systems. In this research, we use previously published experimental data to demonstrate the capabilities and discuss the limitations of the classical physical solutions of steady single-phase flow to evaluate air flow through wet porous media such as aquifers.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"194 ","pages":"Article 104841"},"PeriodicalIF":4.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sub-daily precipitation returns levels in ungauged locations: Added value of combining observations with convection permitting simulations","authors":"Giuseppe Formetta ,&nbsp;Eleonora Dallan ,&nbsp;Marco Borga ,&nbsp;Francesco Marra","doi":"10.1016/j.advwatres.2024.104851","DOIUrl":"10.1016/j.advwatres.2024.104851","url":null,"abstract":"<div><div>Extreme rainfall events trigger natural hazards, including floods and debris flows, posing serious threats to society and the economy. Accurately quantifying extreme rainfall return levels in ungauged locations is crucial for improving flood protection infrastructure and mitigating water-related risks. This paper quantifies the added value of combining rainfall observations with Convection Permitting Model (CPM) simulations to estimate sub-daily extreme rainfall return levels in ungauged locations. We assess the performance of CPM-informed estimates of extreme return level against a traditional interpolation techniques. We find that kriging methods with external drift outperform inverse distance weighting for both traditional and CPM-informed approaches. We then assess the effectiveness of the two methods under different scenarios of station density. At the highest station density (1/196 km²), traditional interpolation methods outperform the CPM-informed method for durations under 6 h. The performance becomes comparable between 6 and 24 h. For lower station densities (1/400 and 1/800 km²), the CPM-informed method outperforms the traditional method, with average reductions in fractional standard error of 24 %, 13 %, and 8 % for return periods of 2, 10 and 50 years, respectively for a rain gauge density of 1/800 km², and 16 %, 8 %, and 3 % for density of 1/400 km². Information from CPM simulations can thus be useful for estimating sub-daily extreme rainfall events in ungauged sites, particularly in data-scarce areas in which the density of rain gauges is low.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"194 ","pages":"Article 104851"},"PeriodicalIF":4.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach","authors":"Ashlin Ann Alexander ,&nbsp;D. Nagesh Kumar","doi":"10.1016/j.advwatres.2024.104842","DOIUrl":"10.1016/j.advwatres.2024.104842","url":null,"abstract":"<div><div>Hydrological model calibration plays a crucial role in estimating optimal parameters for accurate simulation. Estimation of parameters is inevitable in hydrological modeling due to the challenge of directly measuring them, as most parameters are conceptual descriptions of physical processes. Modelers commonly employ optimization algorithms for calibrating hydrological models. However, these algorithms often pose computational challenges, especially when dealing with complex physics-based and distributed models. To address these challenges, our study introduces a novel approach called hydroCNN+DDS. By leveraging the strengths of Convolutional Neural Networks (CNN) and the Dynamically Dimensioned Search (DDS) algorithm, hydroCNN+DDS simplifies the model calibration process in complex physics-based models. This approach enables to capture the general patterns and relationships between discharge time series and parameters without compromising the underlying physics. We use hydroCNN+DDS to estimate parameters in the highly parameterized hydrological model, Structure for Unifying Multiple Modeling Alternatives (SUMMA) using hourly observed discharge. Notably, hydroCNN quickly generates sub-optimal parameters, serving as a good initial solution for DDS. This initialization aids DDS in converging faster towards an optimal solution. One of the notable advantages of the hydroCNN+DDS approach is its potential for spatial and temporal transferability. This feature proves valuable in dynamic systems and regions with limited historical data, expanding the applicability of the methodology. Furthermore, our proposed methodology is versatile and can be applied to any simple or complex models, accommodating any variables of interest. The best practices of good model calibration are followed in our approach.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"194 ","pages":"Article 104842"},"PeriodicalIF":4.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards improved understanding of spontaneous imbibition into dry porous media using pore-scale direct numerical simulations","authors":"Luka Malenica,&nbsp;Zhidong Zhang,&nbsp;Ueli Angst","doi":"10.1016/j.advwatres.2024.104840","DOIUrl":"10.1016/j.advwatres.2024.104840","url":null,"abstract":"<div><div>Traditional approaches to mathematically describe spontaneous imbibition are usually based on either macro-scale models, such as Richards equation, or simplified pore-scale models, such as the bundle of capillary tubes (BCTM) or pore-network modeling (PNM). It is well known that such models cannot provide full microscopic details of the multiphase flow processes and that many pore-scale mechanisms still lack proper mathematical descriptions. To improve the predictive capabilities of traditional models, a fundamental understanding of pore-scale dynamics is needed. The focus of this paper is obtaining detailed insight and consistent explanation of particular processes during capillary-controlled water imbibition into dry porous media. We use two-dimensional model geometries and perform fully dynamic volume-of-fluid based direct numerical simulations of air–water multiphase flow at the pore-scale, to study processes that generally are not considered in traditional models. More specifically, we investigate differences between converging and diverging geometries, dynamic pressure and meniscus reconfiguration during pore-filling events, and the influence of inertia and pore size on imbibition dynamics and the occurrence of capillary barriers. Furthermore, we perform a detailed comparison between non-interacting and interacting BCTM and study the impact of the narrow contractions on imbibition dynamics and the trapping of the non-wetting phase. Obtained knowledge can be used to improve predictive models, which are broadly relevant considering the importance of spontaneous imbibition in many different natural and industrial processes.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"194 ","pages":"Article 104840"},"PeriodicalIF":4.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boosting the reconstruction performance of 3D Multi-porous media using double generative adversarial networks","authors":"Xiaoxiang Yin,&nbsp;Mingliang Gao,&nbsp;Ai Luo,&nbsp;Geling Xu","doi":"10.1016/j.advwatres.2024.104843","DOIUrl":"10.1016/j.advwatres.2024.104843","url":null,"abstract":"<div><div>With the continuous improvement of mathematical modeling technology, reconstructing the three-dimensional structure of media from two-dimensional reference images has become an important research method for the three-dimensional modeling of multi-porous media. Deep-learning-based methods are currently popular and form the focus of this research field. However, the performance of deep learning in reconstructing the three-dimensional structure of media from two-dimensional reference images still requires improvement. To enhance the diversity and generalization of network-generated three-dimensional models, this study proposed a preprocessing method that correlated two-dimensional reference images with Gaussian noise, a three-orthogonal random section constraint method, and a dual generative adversarial network (DGAN)-based model. Multiple sets of core samples, a set of building materials, and a set of battery-material samples were used to verify the performance of the proposed network. Both intuitive morphological and statistical feature comparisons showed that the DGAN model solved the problem of insufficient diversity and generalization when reconstructing three-dimensional porous media from a single image using deep-learning-based methods. The morphological and statistical features of the reconstructed three-dimensional structure also exhibited good consistency with the reference two-dimensional image, and the training efficiency of the network was greatly improved.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"194 ","pages":"Article 104843"},"PeriodicalIF":4.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An extended two-parameter mixed-dimensional model of fractured porous media incorporating entrance flow and boundary-layer transition effects","authors":"Marcio A. Murad ,&nbsp;Luciane A. Schuh ,&nbsp;Igor Mozolevski ,&nbsp;Josue Barroso","doi":"10.1016/j.advwatres.2024.104838","DOIUrl":"10.1016/j.advwatres.2024.104838","url":null,"abstract":"<div><div>We develop an enhanced reduced model for single-phase flow in fractured porous media capable of incorporating more realistic interface conditions at the fracture terminations. In addition to the traditional dimensional model reduction, where the elements of the discrete fracture network are treated as lower dimensional manifolds embedded in the porous matrix, we explore the microscale behavior of the boundary layer flow at the entrances of a fracture bounded by two parallel plates to construct a new set of interface conditions of Robin-type, giving rise to localized pressure jumps at the fracture edges. Within this enriched description, sharper reduced flow and tracer transport mixed-dimensional models are constructed in the asymptotic limit ruled by two small parameters related to the ratio between fracture aperture and entrance developing length and a macroscopic length scale. The discrete flow/transport mixed-dimensional model is discretized by a new discontinuous Galerkin(dG)-based formulation. An adequate version of the Galerkin-Newton method is developed for the numerical treatment of the non-linear Robin interface condition. Considering several fracture arrangements, numerical results illustrate the sharper description of the model proposed herein in predicting flow and tracer transport patterns in fractured media.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"193 ","pages":"Article 104838"},"PeriodicalIF":4.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computationally efficient and error aware surrogate construction for numerical solutions of subsurface flow through porous media","authors":"Aleksei G. Sorokin ,&nbsp;Aleksandra Pachalieva ,&nbsp;Daniel O’Malley ,&nbsp;James M. Hyman ,&nbsp;Fred J. Hickernell ,&nbsp;Nicolas W. Hengartner","doi":"10.1016/j.advwatres.2024.104836","DOIUrl":"10.1016/j.advwatres.2024.104836","url":null,"abstract":"<div><div>Limiting the injection rate to restrict the pressure below a threshold at a critical location can be an important goal of simulations that model the subsurface pressure between injection and extraction wells. The pressure is approximated by the solution of Darcy’s partial differential equation for a given permeability field. The subsurface permeability is modeled as a random field since it is known only up to statistical properties. This induces uncertainty in the computed pressure. Solving the partial differential equation for an ensemble of random permeability simulations enables estimating a probability distribution for the pressure at the critical location. These simulations are computationally expensive, and practitioners often need rapid online guidance for real-time pressure management. An ensemble of numerical partial differential equation solutions is used to construct a Gaussian process regression model that can quickly predict the pressure at the critical location as a function of the extraction rate and permeability realization. The Gaussian process surrogate analyzes the ensemble of numerical pressure solutions at the critical location as noisy observations of the true pressure solution, enabling robust inference using the conditional Gaussian process distribution.</div><div>Our first novel contribution is to identify a sampling methodology for the random environment and matching kernel technology for which fitting the Gaussian process regression model scales as <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mi>n</mi><mo>log</mo><mi>n</mi><mo>)</mo></mrow></mrow></math></span> instead of the typical <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>3</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> rate in the number of samples <span><math><mi>n</mi></math></span> used to fit the surrogate. The surrogate model allows almost instantaneous predictions for the pressure at the critical location as a function of the extraction rate and permeability realization. Our second contribution is a novel algorithm to calibrate the uncertainty in the surrogate model to the discrepancy between the true pressure solution of Darcy’s equation and the numerical solution. Although our method is derived for building a surrogate for the solution of Darcy’s equation with a random permeability field, the framework broadly applies to solutions of other partial differential equations with random coefficients.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"193 ","pages":"Article 104836"},"PeriodicalIF":4.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A physics-constraint neural network for CO2 storage in deep saline aquifers during injection and post-injection periods","authors":"Mengjie Zhao ,&nbsp;Yuhang Wang ,&nbsp;Marc Gerritsma ,&nbsp;Hadi Hajibeygi","doi":"10.1016/j.advwatres.2024.104837","DOIUrl":"10.1016/j.advwatres.2024.104837","url":null,"abstract":"<div><div>CO₂ capture and storage is a viable solution in the effort to mitigate global climate change. Deep saline aquifers, in particular, have emerged as promising storage options, owing to their vast capacity and widespread distribution. However, the task of proficiently monitoring and simulating CO₂ behavior within these formations poses significant challenges. To address this, we introduce the physics-constraint neural network for CO₂ storage (CO₂PCNet), a model specifically designed for simulating and monitoring CO₂ storage in deep saline aquifers during injection and post-injection periods. Recognizing the significant challenges in accurately modeling the distribution and movement of CO₂ under varying permeability conditions, the CO₂PCNet integrates the principles of physics with the robustness of deep learning, serving as a powerful surrogate model. The architecture of CO₂PCNet starts with an encoder that adeptly processes spatial features from overall mole fraction (<span><math><msub><mrow><mi>z</mi></mrow><mrow><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></mrow></msub></math></span>) and pressure fields (<span><math><msub><mrow><mi>P</mi></mrow><mrow><mi>l</mi></mrow></msub></math></span>), capturing the complex dynamics of a CO₂ trajectory. By incorporating permeability information through a conditioning step, the network ensures a faithful representation of the influences on CO₂ behavior in subsurface conditions. A ConvLSTM module subsequently discerns temporal evolutions, reflecting the real-world progression of CO₂ plumes within the reservoir. Lastly, the decoder precisely reconstructs the predictive spatial profile of CO₂ distribution. CO₂PCNet, with its integration of convolutional layers, recurrent mechanisms, and physics-informed constraints, offers a refined approach to CO₂ storage simulation. This model offers the potential of utilizing advanced computational methods in advancing CCS practices.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"193 ","pages":"Article 104837"},"PeriodicalIF":4.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-temporal drought rarity curves—A yearly classification of meteorological drought severity in France","authors":"Juliette Blanchet ,&nbsp;Baptiste Ainési ,&nbsp;Sandra Rome ,&nbsp;Jean-Dominique Creutin","doi":"10.1016/j.advwatres.2024.104829","DOIUrl":"10.1016/j.advwatres.2024.104829","url":null,"abstract":"<div><div>Droughts are recurrent phenomena that present a large variety of space and time patterns making rather difficult the assessment of their rarity and the comparison between events.</div><div>Our study focuses on the “memory effect” of meteorological drought over France using the daily raingauge network maintained by Météo-France over 1950–2022. The proposed easy tool of rarity curves analyses how drought events build and persist across time. The approach is purely statistic, assuming that drought consequences depend on the probability of non exceedance (“rarity”) of antecedent rainfall accumulations. In order to cover a large spectrum of “memory effects”, we consider a continuum of accumulation periods ranging from a few weeks to several years. The rarity curve of a given year displays the most severe rarity values encountered during the year as a function of the various accumulation periods (256 values ranging from 4 to 260 weeks in our case).</div><div>Over the study period of 1950–2022 we show how the shape of rarity curves discriminate short- and long-term historical droughts. A k-means algorithm proves to be useful to classify the different years into typical drought situations from continuous drying to continuous wetting over the antecedent five years. Looking at the chronology of the found clusters also allows studying how droughts build and persist across time.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"193 ","pages":"Article 104829"},"PeriodicalIF":4.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}