Computational Geosciences最新文献

{"title":"A multi-aggregator graph neural network for backbone exaction of fracture networks","authors":"Tianji Zheng, Chengcheng Sun, Jian Zhang, Jiawei Ye, Xiaobin Rui, Zhixiao Wang","doi":"10.1007/s10596-024-10281-2","DOIUrl":"https://doi.org/10.1007/s10596-024-10281-2","url":null,"abstract":"<p>Accurately analyzing the flow and transport behavior in a large discrete fracture network is computationally expensive. Fortunately, recent research shows that most of the flow and transport occurs within a small backbone in the network, and identifying the backbone to replace the original network can greatly reduce computational consumption. However, the existing machine learning based methods mainly focus on the features of the fracture itself to evaluate the importance of the fracture, the local structural information of the fracture network is not fully utilized. More importantly, these machine learning methods can neither control the identified backbone’s size nor ensure the backbone’s connectivity. To solve these problems, a deep learning model named multi-aggregator graph neural network (MA-GNN) is proposed for identifying the backbone of discrete fracture networks. Briefly, MA-GNN uses multiple aggregators to aggregate neighbors’ structural features and thus generates an inductive embedding to evaluate the criticality score of each node in the entire fracture network. Then, a greedy algorithm, which can control the backbone’s size and connectivity, is proposed to identify the backbone based on the criticality score. Experimental results demonstrate that the backbone identified by MA-GNN can recover the transport characteristics of the original network, outperforming state-of-the-art baselines. In addition, MA-GNN can identify influential fractures with higher Kendall’s <span>(tau )</span> correlation coefficient and Jaccard similarity coefficient. With the ability of size control, our proposed MA-GNN can provide an effective balance between accuracy and computational efficiency by choosing a suitable backbone size.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"105 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mineral precipitation model based on the volume of fluid method","authors":"Ziyan Wang, Ilenia Battiato","doi":"10.1007/s10596-024-10280-3","DOIUrl":"https://doi.org/10.1007/s10596-024-10280-3","url":null,"abstract":"<p>A novel volume of fluid method is presented for mineral precipitation coupled with fluid flow and reactive transport. The approach describes the fluid-solid interface as a smooth transitional region, which is designed to provide the same precipitation rate and viscous drag force as a sharp interface. Specifically, the governing equation of mineral precipitation is discretized by an upwind scheme, and a rigorous effective viscosity model is derived around the interface. The model is validated against analytical solutions for mineral precipitation in channel and ring-shaped structures. It also compares well with interface tracking simulations of advection-diffusion-reaction problems. The methodology is finally employed to model mineral precipitation in fracture networks, which is challenging due to the low porosity and complex geometry. Compared to other approaches, the proposed model has a concise algorithm and contains no free parameters. In the modeling, only the pore space requires meshing, which improves the computational efficiency especially for low-porosity media.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"34 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conditional stochastic simulation of fluvial reservoirs using multi-scale concurrent generative adversarial networks","authors":"Ting Zhang, Mengkai Yin, Hualin Bai, Anqin Zhang, Yi Du","doi":"10.1007/s10596-024-10279-w","DOIUrl":"https://doi.org/10.1007/s10596-024-10279-w","url":null,"abstract":"<p>To accurately grasp the comprehensive geological features of fluvial reservoirs, it is necessary to exploit a robust modelling approach to visualize and reproduce the realistic spatial distribution that exhibits apparent and implicit depositional trends of fluvial regions. The traditional geostatistical modelling methods using stochastic modelling fail to capture the complex features of geological reservoirs and therefore cannot reflect satisfactory realistic patterns. Generative adversarial network (GAN), as one of the mainstream generative models of deep learning, performs well in unsupervised learning tasks. The concurrent single image GAN (ConSinGAN) is one of the variants of GAN. Based on ConSinGAN, conditional concurrent single image GAN (CCSGAN) is proposed in this paper to perform conditional simulation of fluvial reservoirs, through which the output of the model can be constrained by conditional data. The results show that ConSinGAN, with the introduction of conditional data, not only preserves the model and parameters for future use but also improves the quality of the simulation results compared to other modeling methods.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"70 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140301427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new method based on multiresolution graph-based clustering for lithofacies analysis of well logging","authors":"","doi":"10.1007/s10596-024-10277-y","DOIUrl":"https://doi.org/10.1007/s10596-024-10277-y","url":null,"abstract":"<h3>Abstract</h3> <p>The lithofacies analysis of logging data is an essential step in reservoir evaluation. Multiresolution graph-based clustering (MRGC) is a commonly used methodology that provides information on the best number of clusters and cluster fitting results for geological understanding. However, the cluster fusion approach of MRGC often leads to an overemphasis of the boundary constraints among clusters. MRGC neglects the global cluster distribution relationship, which limits its practical application effectiveness. This paper proposes a new methodology, named kernel multiresolution graph-based clustering (KMRGC), to improve the merging part of clustering in MRGC, and it can give more weight to the spatial relationship characteristics among clusters. The clustering performance of K-means, Gaussian Mixture Model(GMM), fuzzy c-means(FCM), Density-Based Spatial Clustering of Applications with Noise(DBSCN), spectral clustering, MRGC and KMRGC algorithm was evaluated on a publicly available training set and noisy dataset, and the best results in terms of the adjusted Rand coefficients and normalized mutual information(NMI) coefficients on most of the datasets were obtained using KMRGC algorithm. Finally, KMRGC was used for logging data lithofacies clustering in cased wells, and the clustering effect of KMRGC algorithm was much better than that of the K-means, GMM, FCM, DBSCN, spectral clustering and MRGC algorithms, and the accuracy and stability were better.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"162 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rigid transformations for stabilized lower dimensional space to support subsurface uncertainty quantification and interpretation","authors":"Ademide O. Mabadeje, Michael J. Pyrcz","doi":"10.1007/s10596-024-10278-x","DOIUrl":"https://doi.org/10.1007/s10596-024-10278-x","url":null,"abstract":"&lt;p&gt;Subsurface datasets commonly are big data, i.e., they meet big data criteria, such as large data volume, significant feature variety, high sampling velocity, and limited data veracity. Large data volume is enhanced by the large number of necessary features derived from the imposition of various features derived from physical, engineering, and geological inputs, constraints that may invoke the curse of dimensionality. Existing dimensionality reduction (DR) methods are either linear or nonlinear; however, for subsurface datasets, nonlinear dimensionality reduction (NDR) methods are most applicable due to data complexity. Metric-multidimensional scaling (MDS) is a suitable NDR method that retains the data's intrinsic structure and could quantify uncertainty space. However, like other NDR methods, MDS is limited by its inability to achieve a stabilized unique solution of the low dimensional space (LDS) invariant to Euclidean transformations and has no extension for inclusions of out-of-sample points (OOSP). To support subsurface inferential workflows, it is imperative to transform these datasets into meaningful, stable representations of reduced dimensionality that permit OOSP without model recalculation.&lt;/p&gt;&lt;p&gt;We propose using rigid transformations to obtain a unique solution of stabilized Euclidean invariant representation for LDS. First, compute a dissimilarity matrix as the MDS input using a distance metric to obtain the LDS for &lt;span&gt;(N)&lt;/span&gt;-samples and repeat for multiple realizations. Then, select the base case and perform a rigid transformation on further realizations to obtain rotation and translation matrices that enforce Euclidean transformation invariance under ensemble expectation. The expected stabilized solution identifies anchor positions using a convex hull algorithm compared to the &lt;span&gt;(N+1)&lt;/span&gt; case from prior matrices to obtain a stabilized representation consisting of the OOSP. Next, the loss function and normalized stress are computed via distances between samples in the high-dimensional space and LDS to quantify and visualize distortion in a 2-D registration problem. To test our proposed workflow, a different sample size experiment is conducted for Euclidean and Manhattan distance metrics as the MDS dissimilarity matrix inputs for a synthetic dataset.&lt;/p&gt;&lt;p&gt;The workflow is also demonstrated using wells from the Duvernay Formation and OOSP with different petrophysical properties typically found in unconventional reservoirs to track and understand its behavior in LDS. The results show that our method is effective for NDR methods to obtain unique, repeatable, stable representations of LDS invariant to Euclidean transformations. In addition, we propose a distortion-based metric, stress ratio (SR), that quantifies and visualizes the uncertainty space for samples in subsurface datasets, which is helpful for model updating and inferential analysis for OOSP. Therefore, we recommend the workflow's integration as an invariant ","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"59 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140074309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved spatial understanding of induced seismicity hazard from the discretization of a curved fault surface","authors":"Kevin L. McCormack, Philip J. Smith","doi":"10.1007/s10596-024-10276-z","DOIUrl":"https://doi.org/10.1007/s10596-024-10276-z","url":null,"abstract":"<p>In some geomechanical treatments of induced seismicity, the fault surface is idealized to be a plane. We depart from this assumption by comparing a discretization model and a kriging model, both of which allow the incorporation of rugosity, roughness, and curvature into the fault surface and subsequent geomechanical models of hazard. We test the Hogback Flexural Faults of the San Juan Basin, which could potentially pose a problem for induced seismicity in a carbon sequestration project in the northwestern portion of the basin. The discretization model emmeshes data about the location of the fault surface in three-dimensional space into hexagonally close-packed spheres. Each sphere that contains enough data is termed a region and Bayes’ Law is used to find a distribution of strikes and dips that describe the data within the region. The kriging model uses Gaussian processes to interpolate and extrapolate a surface through all data points. The results show that the discretized regions possess, in general, lower Coulomb failure functions, but the uncertainty in the distributions, i.e., the ranges, becomes greater as the discretization increases due to overfitting. The majority of the uncertainty in both the discretization model and the kriging model is contained in the geomechanical priors. Finally, the discretization and kriging of the fault surface elucidates locations with higher Coulomb failure functions.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"10 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Derivative-free search approaches for optimization of well inflow control valves and controls","authors":"Mathias C. Bellout, Thiago L. Silva, Jan Øystein Haavig Bakke, Carl Fredrik Berg","doi":"10.1007/s10596-024-10270-5","DOIUrl":"https://doi.org/10.1007/s10596-024-10270-5","url":null,"abstract":"<p>Decisions regarding problem conceptualization, search approach, and how best to parametrize optimization methods for practical application are key to successful implementation of optimization approaches within georesources field development projects. This work provides decision support regarding the application of derivative-free search approaches for concurrent optimization of inflow control valves (ICVs) and well controls. A set of state-of-the-art approaches possessing different search features is implemented over two reference cases, and their performance, resource requirements, and specific method configurations are compared across multiple problem formulations for completion design. In this study, problem formulations to optimize completion design comprise fixed ICVs and piecewise-constant well controls. The design is optimized by several derivative-free methodologies relying on parallel pattern-search (<b>t</b>APPS), population-based stochastic sampling (<b>t</b>PSO) and trust-region interpolation-based models (<b>t</b>DFTR). These methodologies are tested on a heterogeneous two-dimensional case and on a realistic case based on a section of the Olympus benchmark model. Three problem formulations are applied in both cases, i.e., one formulation optimizes ICV settings only, while two joint configurations also treat producer and injector controls as variables. Various method parametrizations across the range of cases and problem formulations exploit the different search features to improve convergence, achieve final objectives and infer response surface features. The scope of this particular study treats only deterministic problem formulations. Results outline performance trade-offs between parallelizable algorithms (<b>t</b>APPS, <b>t</b>PSO) with high total runtime search efficiency and the local-search trust-region approach (<b>t</b>DFTR) providing effective objective gains for a low number of cost function evaluations. <b>t</b>APPS demonstrates robust performance across different problem formulations that can support exploration efforts, e.g., during a pre-drill design phase while multiple independent <b>t</b>DFTR runs can provide local tuning capability around established solutions in a time-constrained post-drill setting. Additional remarks regarding joint completion design optimization, comparison metrics, and relative algorithm performance given the varying problem formulations are also made.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"23 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139762559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mortar method for the coupled Stokes-Darcy problem using the MAC scheme for Stokes and mixed finite elements for Darcy","authors":"Wietse M. Boon, Dennis Gläser, Rainer Helmig, Kilian Weishaupt, Ivan Yotov","doi":"10.1007/s10596-023-10267-6","DOIUrl":"https://doi.org/10.1007/s10596-023-10267-6","url":null,"abstract":"<p>A discretization method with non-matching grids is proposed for the coupled Stokes-Darcy problem that uses a mortar variable at the interface to couple the marker and cell (MAC) method in the Stokes domain with the Raviart-Thomas mixed finite element pair in the Darcy domain. Due to this choice, the method conserves linear momentum and mass locally in the Stokes domain and exhibits local mass conservation in the Darcy domain. The MAC scheme is reformulated as a mixed finite element method on a staggered grid, which allows for the proposed scheme to be analyzed as a mortar mixed finite element method. We show that the discrete system is well-posed and derive a priori error estimates that indicate first order convergence in all variables. The system can be reduced to an interface problem concerning only the mortar variables, leading to a non-overlapping domain decomposition method. Numerical examples are presented to illustrate the theoretical results and the applicability of the method.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"5 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139763091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A benchmark study on reactive two-phase flow in porous media: Part II - results and discussion","authors":"Etienne Ahusborde, Brahim Amaziane, Stephan de Hoop, Mustapha El Ossmani, Eric Flauraud, François P. Hamon, Michel Kern, Adrien Socié, Danyang Su, K. Ulrich Mayer, Michal Tóth, Denis Voskov","doi":"10.1007/s10596-024-10269-y","DOIUrl":"https://doi.org/10.1007/s10596-024-10269-y","url":null,"abstract":"<p>This paper presents and discusses the results obtained by the participants to the benchmark described in de Hoop et al, Comput. Geosci. (2024). The benchmark uses a model for CO₂ geological storage and focuses on the coupling between two-phase flow and geochemistry. Several test cases of various levels of difficulty are proposed, both in one and two spatial dimensions. Six teams participated in the benchmark, each with their own simulation code, though not all teams attempted all the cases. The codes used by the participants are described, and the results obtained on the various test cases are compared, as well as the performance of the codes. It is shown that the results obtained are widely consistent, giving a good level of confidence in the outcome of the benchmark. The general complexity of two-phase flow coupled with chemical reactions altering porous media means that some differences between the codes remain. Besides, from the convergence study, it is clear that the two-dimensional problem has a relatively high sensitivity to a spatial resolution which adds to the complexity.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"38 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139665493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling low saline carbonated water flooding including surface complexes","authors":"","doi":"10.1007/s10596-024-10274-1","DOIUrl":"https://doi.org/10.1007/s10596-024-10274-1","url":null,"abstract":"<h3>Abstract</h3> <p>Carbonated water flooding (CWI) increases oil production due to favorable dissolution effects and viscosity reduction. Accurate modeling of CWI performance requires a simulator with the ability to capture the true physics of such process. In this study, compositional modeling coupled with surface complexation modeling (SCM) are done, allowing a unified study of the influence in oil recovery of reduction of salt concentration in water. The compositional model consists of the conservation equations of total carbon, hydrogen, oxygen, chloride and decane. The coefficients of such equations are obtained from the equilibrium partition of chemical species that are soluble both in oleic and the aqueous phases. SCM is done by using the PHREEQC program, which determines concentration of the master species. Estimation of the wettability as a function of the Total Bound Product (TBP) that takes into account the concentration of the complexes in the aqueous, oleic phases and in the rock walls is performed. We solve analytically and numerically these equations in <span> <span>(1-)</span> </span>D in order to elucidate the effects of the injection of low salinity carbonated water into a reservoir containing oil equilibrated with high salinity carbonated water.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"193 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139665328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}