Computational Geosciences最新文献

{"title":"Less-Intrusive Consistent Discretization Methods for Reservoir Simulation on Cut-cell Grids – Algorithms, Implementation, and Testing","authors":"Faruk O. Alpak, Mohamad Jammoul, Mary F. Wheeler, Kachi Onyeagoro","doi":"10.1007/s10596-024-10299-6","DOIUrl":"https://doi.org/10.1007/s10596-024-10299-6","url":null,"abstract":"<p>Consistent discretization methods are a natural fit for the novel cut-cell gridding technique for reservoir simulation, which preserves the orthogonality characteristic in the lateral direction. Both uniform (global) and novel hybrid (local) variants of consistent discretization methods are implemented and tested in the vicinity of fault representations on cut-cell grids. Novel consistent discretization methods, which do not require major intrusive changes to the solver structure of industrial-grade reservoir simulators, are investigated in this work. Cell-centered methods such as multi-point flux approximation (MPFA), average multi-point flux approximation (AvgMPFA), and nonlinear two-point flux approximation (NTPFA) methods fit naturally into the framework of existing industrial-grade simulators. Thus, cut-cell compatible variants of AvgMPFA and NTPFA and their novel hybridizations with TPFA are implemented and tested. An implementation of the relatively more computationally expensive MPFA is also made to serve as accuracy reference to AvgMPFA and NTPFA. AvgMPFA and NTPFA multiphase simulation results are compared in terms of accuracy and computational performance against the ones computed with reference MPFA and TPFA methods on a set of synthetic cut-cell grid models of varying complexity including conceptual models and a field-scale model. It is observed that AvgMPFA consistently yields more accurate and computationally efficient simulations than NTPFA on cut-cell grids. Moreover, AvgMPFA-TPFA hybrids run faster than NTPFA-TPFA hybrids when compared on the same problem for the same hybridization strategy. On the other hand, the computational performance of AvgMPFA degrades more rapidly compared to NTPFA with increasing “rings” of orthogonal blocks around cut-cells. Auspiciously, only one or two “rings” of orthogonal blocks around cut cells are sufficient for AvgMPFA to deliver high accuracy.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"66 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772077","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":"Unsupervised segmentation for sandstone thin section image analysis","authors":"Rayan T. C. M. Barbosa, E. L. Faria, Matheus Klatt, Thais C. Silva, Juliana. M. Coelho, Thais F. Matos, Bernardo C. C. Santos, J. L. Gonzalez, Clécio R. Bom, Márcio P. de Albuquerque, Marcelo P. de Albuquerque","doi":"10.1007/s10596-024-10304-y","DOIUrl":"https://doi.org/10.1007/s10596-024-10304-y","url":null,"abstract":"<p>The study of thin sections provides crucial information about the structure of sedimentary rocks. Different properties, such as mineral composition, texture, grain morphology, presence of clay minerals, and porosity level, can be derived from thin section analysis. These features directly determine the quality of crude reservoirs. In this context, manual grain identification from petrographic thin sections usually demands considerable time and effort, so machine learning and image processing techniques have become more frequent in the last few years. Obtaining large and reliable labeled data sets for supervised learning workflows is a complex and critical process. We devise a completely unsupervised approach for granulometric classification using thin section images. The introduced workflow first pre-processes the thin section image by denoising and dividing it into different image patches. In the second stage, the image patches are used to train an unsupervised convolutional neural network. Then, the trained network segments the grains in each patch of the pre-processed image. The training strategy uses transfer learning to guarantee the same initialization parameters of the neural network while processing the image patches. Next, a watershed transform is applied to recover the borders of the segmented grains. Finally, a granulometric calculation and classification process is performed by considering the grain contours restored through the implemented methodology. The results obtained with the proposed algorithm are concordant with those obtained from the analysis of sieved thin sections derived from controlled experiments in the laboratory.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"43 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772078","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":"Characterizing competitive adsorption and diffusion of methane and carbon dioxide in kerogen type-III slit model","authors":"Jihong Shi, Tao Zhang, Xiaoping Xie, Wei Wei, Liang Gong, Shuyu Sun","doi":"10.1007/s10596-024-10295-w","DOIUrl":"https://doi.org/10.1007/s10596-024-10295-w","url":null,"abstract":"<p>Clarifying methane’s adsorption and diffusion properties in kerogen contributes to efficiently exploiting shale gas reservoirs. We refined the kerogen III-series model to construct the kerogen III-B and kerogen III-C molecular structures. In contrast to the traditional simplified slit model containing small organic matter, a mixed kerogen-quartz slit model was further proposed. These inorganic-organic models, including kerogen II-D, kerogen III-B, and kerogen III-C, provide a realistic reservoir environment for the study of the shale gas adsorption and diffusion characteristics in shale. Based on these models, we investigated the competitive adsorption of methane and carbon dioxide using the grand canonical Monte Carlo (GCMC) method. We then studied the diffusion characteristics of methane molecules throughout the model area and in the different adsorption blocks classified as the inner slit zone, surface zone, and matrix zone using the molecular dynamics (MD) method. The results showed that carbon dioxide gradually replaces methane molecules as the injection pressure of carbon dioxide increases, causing desorption and diffusion of methane. The order of the overall diffusion capability of methane in the kerogen slit models is kerogen II-D &gt;kerogen III-C &gt;kerogen III-B. In addition, the diffusion capability of methane molecules in the different zones is ordered as inner slit zone &gt;surface zone &gt;matrix zone. This work is a step towards more effective exploitation of shale gas.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"149 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772080","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":"Application of a dispersive wave hydro-sediment-morphodynamic model in the Ria Formosa lagoon","authors":"Kazbek Kazhyken, Eirik Valseth, Juha Videman, Clint Dawson","doi":"10.1007/s10596-024-10305-x","DOIUrl":"https://doi.org/10.1007/s10596-024-10305-x","url":null,"abstract":"<p>Results of an application of a dispersive wave hydro-sediment-morphodynamic model in the western circulation cell of the Ria Formosa lagoon located in the Algarve region of the southern Portugal are presented. This area of interest has a couple of features that complicate the application of the dispersive wave model: (1) the area has a complex irregular geometry with a number of barrier islands that separate the lagoon from the Atlantic Ocean, artificial and naturally occurring tidal inlets, and a number of curling channels inside the lagoon that interconnect the inlets and serve as waterways between the lagoon settlements; (2) the tidal range in the area can reach up to 3.5 m; therefore, the terrain inside the lagoon is characterized by vast salt marshes and tidal flats, and the wetting-drying process is a key component of any hydrodynamic simulation in this area. A model representation of the area has been developed by generating an unstructured finite element mesh of the circulation cell, and collecting data on parameters that characterize the tidal waves in the area, and bottom friction and sediment transport models used in the simulations. The results of the simulations indicate that the dispersive wave model can be applied in coastal areas with nontrivial underlying physical processes, and complex irregular geometries. Moreover, the dispersive term of the model is capable of capturing additional flow characteristics that are otherwise not present in hydrodynamic simulations that involve the nonlinear shallow water equations; and these additional flow features can, in their turn, affect the resulting sediment transport and bed morphodynamic process simulations.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"3 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141586791","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":"Contact algorithm of the material point method and comparison with the finite element method","authors":"Peng Huang, Dong-huan Liu, Hu Guo, Ke Xie, Qing-ping Zhang, Zhi-fang Deng","doi":"10.1007/s10596-024-10302-0","DOIUrl":"https://doi.org/10.1007/s10596-024-10302-0","url":null,"abstract":"<p>Being a fully Lagrangian particle method, the material point method (MPM) discretizes a material domain by using a collection of material points. The momentum equations in MPM are solved on a predefined regular background grid, so that the grid distortion and entanglement are completely avoided in MPM. The contact algorithm of MPM is developed via the background grid and the impenetrability condition between bodies. The contact algorithm of MPM is applied to solve some impact and perforation problems. This study concerns the validation of the contact algorithm of MPM. Solutions from MPM with the contact algorithm are compared to those from the finite element method (FEM) with the penalty method. For two impact problems, the results from MPM with the contact algorithm are in good agreement with those obtained with the FEM penalty method. For the perforation problem of aluminum plate, the results obtained using MPM with the contact algorithm are better than those from the FEM penalty method. We think that for impact problems without extreme large deformations, it is better to use the FEM penalty method. For impact problems with extreme large deformations, it is better to use the contact algorithm of MPM.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"28 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141576950","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":"Determining optimal controls placed on injection/production wells during waterflooding in heterogeneous oil reservoirs using artificial neural network models and multi-objective genetic algorithm","authors":"Onyebuchi Ivan Nwanwe, Nkemakolam Chinedu Izuwa, Nnaemeka Princewill Ohia, Anthony Kerunwa, Nnaemeka Uwaezuoke","doi":"10.1007/s10596-024-10300-2","DOIUrl":"https://doi.org/10.1007/s10596-024-10300-2","url":null,"abstract":"<p>The objective of this study is to propose a computationally inexpensive and effective approach that addresses the challenges faced with computationally expensive and time-consuming trial-and-error and direct optimization methods in well-control optimization. This approach involves combining proxy models such as artificial neural network (ANN) models with optimization algorithms to determine an optimal solution much faster. It was implemented in a heterogeneous oil reservoir undergoing waterflooding. The controllable parameters of the reservoir simulation model were identified as bottom-hole pressure for the producers and water injection rate for the injectors. Minimum and maximum values of each input parameter were defined based on reservoir conditions and used with a Box Behnken design (BBD) method to generate realizations for conducting reservoir simulations to obtain cumulative oil produced (COP) and cumulative water produced (CWP). The input and output data were normalized before being used for model development such that 70:15:15% of data was used for training, validation, testing, and all of the ANN model in which a coefficient of correlation (R) of 0.99756, 0.94354, 0.95813, and 0.98589 were obtained respectively. This indicates the accuracy, validity, and reliability of the model. The coefficient of determination (R²) for training, validation, testing, and all datasets as well as statistical error and trend analysis were used to validate the model. R² values for each case were not less than 0.80, and the responses were reproduced by the ANN model with average relative error and root mean square error of not more than 0.7%. Weights and biases were extracted from the trained and validated ANN model to aid in outputting a visible ANN model that can be used for optimization studies. A multi-objective genetic algorithm was used to determine an optimal solution that maximized COP and minimized CWP. Average and optimized input data were used to run the developed ANN model. Results revealed that the optimized case outperformed the case for which average input values were used evidenced by the production of 34.198 MSm³ more oil and 14.297 MMSm³ less water. The findings of this study showed that using an ANN-MOGA approach will eliminate the computationally expensive, time-consuming, and inefficient trial-and-error approach for well-control optimization. Oil recovery was improved while water production was reduced resulting in low expenditure on treatment and disposal of produced water.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"24 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141576951","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":"Study on the microscopic pore permeability behavior of granite under multiple cycles of cold-hot alternating damage effects","authors":"Li Yu, Haonan Li, Yue Wu, Weihao Wang, Xinyuan Zhang","doi":"10.1007/s10596-024-10303-z","DOIUrl":"https://doi.org/10.1007/s10596-024-10303-z","url":null,"abstract":"<p>In the process of harnessing geothermal energy, the enduring effects of thermal cycling on granite within the geothermal reservoir led to alterations in rock permeability. This, in turn, directly impacts the efficiency of thermal energy extraction. Hence, delving into the micro-permeability dynamics of granite is imperative to understand the characteristics of prevalent fractures. Employing micro-CT technology, we meticulously extract and analyze the pores of granite samples, unveiling the distribution patterns of pores and micro-permeability variations under successive thermal cycles. The resultant three-dimensional pore model vividly showcases the evolving pore structures during both heating and cooling cycles. Notably, the distribution curve of granite pore volume adheres to a chi-square distribution. Through the utilization of pore volume distribution curves, we categorize rock pores into four distinct types: micropores, mesopores, macropores, and fractures. Both quantitatively and visually, micropores and mesopores predominate, while a fraction of pores gradually transitions into sizable fractures. By employing suitable representative elements to construct the flow field within the large pore model and subsequently calculating permeability, we observe a positive correlation between porosity, permeability, and cyclic temperature-induced damage. Notably, the estimated permeability closely aligns with the measured values, exhibiting an acceptable margin of error. Furthermore, under the influence of thermal cycle-induced damage, the flow simulation demonstrates a noticeable increase in the number of flow lines, consequently resulting in enhanced permeability. This effectively validates the accuracy of the flow simulation based on micro-CT results.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"13 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548825","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":"Effect of fracture structure on heat transfer in heat pipes in a submarine hydrothermal reservoir","authors":"Gaowei Yi, Yan Li, Da Zhang, Shiqiao Zhou","doi":"10.1007/s10596-024-10301-1","DOIUrl":"https://doi.org/10.1007/s10596-024-10301-1","url":null,"abstract":"<p>Complex geological structures like pores, fractures and faults in submarine hydrothermal reservoirs have significant but unclear effects on internal hydrothermal flow and heat transfer, which hinders reservoir exploitation. This study establishes a heat transfer model of a buried pipe coupled in fracture-porous media based on the reservoir characteristics. The model is verified through experiments using fractured porous media test rigs and computational fluid dynamics simulations. Simulations are performed to investigate the effects of fracture flow velocity, width, cornerstone porosity on the heat transfer efficiency of the buried pipe. Results show that optimizing fracture flow velocity, fracture width and cornerstone porosity can substantially improve the heat transfer performance of the buried pipe. Increasing fracture flow velocity from 10^–4 m/s to 10^–3 m/s, results in a 161.92% increase of Nusselt number. When the fracture width increases to 5 times the pipe diameter, Nusselt number rises by 35.52%. The heat transfer is optimal at a porosity of 0.3. This study provides theoretical guidance for exploiting submarine hydrothermal resources and designing fracture-porous couplings to enhance buried pipe heat transfer.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"38 2 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548836","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":"An integrated approach to derive relative permeability from capillary pressure","authors":"Nathan Moodie, Brian McPherson","doi":"10.1007/s10596-024-10297-8","DOIUrl":"https://doi.org/10.1007/s10596-024-10297-8","url":null,"abstract":"<p>Surface tension affects all aspects of fluid flow in porous media. Through measurements of surface tension interaction under multiphase conditions, a relative permeability curve can be determined. Relative permeability is a numerical description of the interaction between two or more fluids and the porous media. It is a critical parameter for various tools that characterize subsurface multiphase flow systems, such as numerical simulation for carbon sequestration, oil and gas development, and groundwater contamination remediation. Therefore, it is critical to get a good statistical distribution of relative permeability in the porous media under study. Empirical formula for determining relative permeability from capillary pressure are already well established but do not provide the needed flexibility that is required to match laboratory-derived relative permeability curves. By expanding the existing methods for calculating relative permeability from capillary pressure data, it is possible to create both two and three-phase relative permeability curves. Mercury intrusion capillary pressure (MICP) data from the Morrow 'B' Sandstone coupled with interfacial tension and contact angle measurements were used to create a suite of relative permeability curves. These curves were then calibrated to a small sample of existing laboratory curves to elucidate common fitting parameters for the formation that were then used to create relative permeability curves from MICP data that does not have an associated laboratory-measured relative permeability curve.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"85 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548827","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":"Stochastic pix2vid: A new spatiotemporal deep learning method for image-to-video synthesis in geologic CO $$_2$$ storage prediction","authors":"Misael M. Morales, Carlos Torres-Verdín, Michael J. Pyrcz","doi":"10.1007/s10596-024-10298-7","DOIUrl":"https://doi.org/10.1007/s10596-024-10298-7","url":null,"abstract":"<p>Numerical simulation of multiphase flow in porous media is an important step in understanding the dynamic behavior of geologic CO<span>(_2)</span> storage (GCS). Scaling up GCS requires fast and accurate high-resolution modeling of the storage reservoir pressure and saturation plume migration; however, such modeling is challenging due to the high computational costs of traditional physics-based simulations. Deep learning models trained with numerical simulation data can provide a fast and reliable alternative to expensive physics-based numerical simulations. We propose a Stochastic pix2vid neural network architecture for solving multiphase fluid flow problems with significant speed, accuracy, and efficiency. The Stochastic pix2vid model is designed based on the principles of computer vision and video synthesis and is able to generate dynamic spatiotemporal predictions of fluid flow from static reservoir models, closely mimicking the performance of traditional numerical simulation. We apply the Stochastic pix2vid model to a highly-complex CO<span>(_2)</span>-water multiphase problem with a wide range of reservoir models in terms of porosity and permeability heterogeneity, facies distribution, and injection configurations. The Stochastic pix2vid method is first-of-its-kind in static-to-dynamic prediction of reservoir behavior, where a single static input is mapped to its dynamic response with a fixed number of timesteps. The Stochastic pix2vid method provides notable performance in highly heterogeneous geologic formations and complex estimation such as CO<span>(_2)</span> saturation and pressure buildup plume determination. The trained model can serve as a general-purpose, static-to-dynamic (image-to-video) alternative to traditional numerical reservoir simulation of 2D CO<span>(_2)</span> injection problems with up to 6,500<span>(times )</span> speedup compared to traditional numerical simulation using the MATLAB Reservoir Simulation Toolbox.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"126 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548828","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}