Computers & Geosciences最新文献

{"title":"A new efficient approach of DFN modelling constrained with fracture occurrence and spatial location","authors":"","doi":"10.1016/j.cageo.2024.105729","DOIUrl":"10.1016/j.cageo.2024.105729","url":null,"abstract":"<div><p>Fractures or faults in the subsurface exert a significant impact on fluid flow and engineering activities in that environment. Fracture modelling is one of the crucial techniques, providing essential insights into the mechanisms underlying these impacts. As a useful tool, the Discrete Fracture Network (DFN) method is often utilized to simulate fracture networks and to integrate fracture statistics into 3D numerical models. However, the current DFN modeling technology suffers from low operational efficiency, particularly when handling a substantial quantity of fractures in 3D models. This paper proposes two ways to improve the efficiency and accuracy of modelling fractures: the matrix-based random sampling method (for faster generation of fracture loactions) and the quaternion method (for more accurate description of fractures). These proposed approaches simplify the management of large number of fractures within 3D models. The paper provides a comprehensive description of the proposed methods, accompanied by pseudo-code for the algorithms. The effectiveness of the proposed approach is validated through a practical case study, demonstrating superior computational efficiency and enhanced applicability for large-scale fracture modeling.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142270962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 exploring the extraction of geological elements from 3D geological models within the constraints of geological knowledge","authors":"","doi":"10.1016/j.cageo.2024.105726","DOIUrl":"10.1016/j.cageo.2024.105726","url":null,"abstract":"<div><p>During the process of visualization, format exchange, and spatial analysis, the 3D geological model tends to emphasize its geometric features, thereby diminishing its geological significance to some extent. However, extracting corresponding geological elements directly from the model based solely on the pure geometric features of geologic bodies proves to be difficult and few studies have focused on related problems. This research aims to extract geological elements from existing geological models under the constraints of geological knowledge to enhance the reusability of existing models and the efficacy of their applications in subsequent research. Firstly, each stratum is assigned its geological significance under the constraints of geological knowledge. Then, the study introduces extraction methods for the topographic interface, eroded interface, stratigraphic top and bottom interfaces, and various constraint boundaries. Furthermore, the potential importance of the studies presented in this paper and their application scenarios are analyzed and explored. Finally, the feasibility and effectiveness of the method for extracting geological elements are validated through a case study. This method holds significant scientific importance for efficiently updating and conducting fine application analyses of geological models. Additionally, this research provides valuable insights that enhance the efficiency of model updating, property model construction, and the splicing of block models across extensive areas.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 phase-state identification bypass approach of the hydrocarbons-CO2-H2O system for compositional reservoir simulation","authors":"","doi":"10.1016/j.cageo.2024.105725","DOIUrl":"10.1016/j.cageo.2024.105725","url":null,"abstract":"<div><p>CO₂ injection is a highly effective technique to enhance oil recovery, achieved through continuous or alternative injection. However, the intricate interactions between different phases within porous media present significant challenges when predicting the performance of CO₂ injection. To address this, it is crucial to employ compositional simulation, which accounts for the multiphase multicomponent transport. Nonetheless, conventional multiphase flash calculations can be computationally inefficient for large-scale reservoir simulations. Therefore, it is necessary to accelerate the Equation-of-State (EoS)-based compositional simulation, given the widespread use of CO₂ enhanced oil recovery (CO₂-EOR) in recent years. The phase-state identification bypass method has proven to be superior to other methods in terms of efficiency. However, this approach struggles with regions near phase boundaries, resulting in reduced computational efficiency in those areas.</p><p>In this study, an enhanced phase-state identification bypass approach is developed to address this limitation. The first step involves discretising the pressure-temperature space using rectangular grids. Additionally, the tie-simplexes, which represent regions defined by the maximum number of phases formed by the fluid under consideration, are discretized in the phase-fraction space at the pressure and temperature of each discretization node. Subsequently, the discretization grid associated with the given point (the overall composition, pressure, and temperature) is located, and the phase states of the grid nodes are determined using the conventional multiphase flash method. If all nodes exhibit the same phase state, that phase state is assigned to the given point. However, if multiple phase states are obtained, a novel process is proposed to determine the phase state of the given point. To validate this improvement to the phase-state identification bypass method, phase diagram calculations and simulation cases are conducted, and the results demonstrate the robustness of the proposed method and its superior computational efficiency compared to the previous method.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning CO2 plume migration in faulted reservoirs with Graph Neural Networks","authors":"","doi":"10.1016/j.cageo.2024.105711","DOIUrl":"10.1016/j.cageo.2024.105711","url":null,"abstract":"<div><p>Deep-learning-based surrogate models provide an efficient complement to numerical simulations for subsurface flow problems such as CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> geological storage. Accurately capturing the impact of faults on CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> plume migration remains a challenge for many existing deep learning surrogate models based on Convolutional Neural Networks (CNNs) or Neural Operators. We address this challenge with a graph-based neural model leveraging recent developments in the field of Graph Neural Networks (GNNs). Our model combines graph-based convolution Long-Short-Term-Memory (GConvLSTM) with a one-step GNN model, MeshGraphNet (MGN), to operate on complex unstructured meshes and limit temporal error accumulation. We demonstrate that our approach can accurately predict the temporal evolution of gas saturation and pore pressure in a synthetic reservoir with impermeable faults. Our results exhibit a better accuracy and a reduced temporal error accumulation compared to the standard MGN model. We also show the excellent generalizability of our algorithm to mesh configurations, boundary conditions, and heterogeneous permeability fields not included in the training set. This work highlights the potential of GNN-based methods to accurately and rapidly model subsurface flow with complex faults and fractures.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142242002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced petrographic thin section segmentation through deep learning-integrated adaptive GLFIF","authors":"","doi":"10.1016/j.cageo.2024.105713","DOIUrl":"10.1016/j.cageo.2024.105713","url":null,"abstract":"<div><p>In geological research, precise segmentation of sandstone thin sections is crucial for detailed subsurface material analysis. Traditional methods often fall short in accurately capturing the complexities of these samples. This study presents an innovative segmentation approach that integrates an adaptive Global and Local Fuzzy Image Fitting (GLFIF) algorithm with Otsu's thresholding, significantly enhancing segmentation accuracy and efficiency. Our method combines deep learning and traditional image processing techniques. The adaptive GLFIF algorithm, powered by deep learning, automates parameter tuning, thereby reducing manual intervention and improving precision. Unlike conventional methods that learn fixed parameters, our model dynamically adjusts the segmentation process to achieve accurate results. The dual-phase segmentation strategy effectively isolates small features and handles intricate boundaries, ensuring high-quality outcomes. Experimental results demonstrate that our approach improves segmentation accuracy by 11.2% (from 82.6% to 93.8%), the Jaccard index by 15.4% (from 76.8% to 92.2%), and the Dice coefficient by 9% (from 86.9% to 95.9%) compared to traditional methods. This technique bridges the gap between conventional image analysis and deep learning, combining precise segmentation with the automation and computational power of advanced algorithms. Our segmentation algorithm represents a significant advancement in automated petrographic thin section analysis. Traditional image processing methods, such as thresholding and level sets, excel in handling small objects and complex boundaries but require significant manual intervention and cannot achieve full automation. Recent deep learning methods, particularly semantic segmentation, offer end-to-end automation but struggle with small targets and intricate boundaries. Our approach effectively combines the strengths of both methodologies, providing a comprehensive and efficient solution for geological image analysis that ensures both high accuracy and full automation.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142168506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 lattice Boltzmann flux solver with the 1D-link interpolation scheme for simulating fluid flow and heat transfer in fractured porous media","authors":"","doi":"10.1016/j.cageo.2024.105715","DOIUrl":"10.1016/j.cageo.2024.105715","url":null,"abstract":"<div><p>In this study, we propose an improved lattice Boltzmann flux solver (LBFS) to simulate the thermal-hydraulic (TH) processes within fractured porous media. In LBFS, the flux at cell interfaces is calculated using a locally reconstructed lattice Boltzmann model (LBM). Unlike conventional methods that use direct mathematical approximations, LBFS can suppress the oscillation of solutions and has better accuracy. However, when simulating two-dimensional fractured porous media problems, the rock matrix is divided into surface cells, while fractures are usually divided into line cells. This increases the complexity of implementing the LBFS, as the reconstruction of interface flux in different dimensions requires the use of discrete velocity models (DmQn) in different dimensions. To address this challenge, we introduce an innovative interpolation scheme based on the improved D1Q3 model, thereby establishing a dimensionally independent approach for the reconstruction of the interface flux. This approach greatly reduces the complexity of applying the LBFS to hybrid dimensional problems and simplifies the computational process. The present method is validated by simulating three typical cases and the results show good agreement with the reference solutions. Finally, the improved LBFS is applied to analyze the TH coupling behavior in fractured porous media with a single fracture and a more complex scenario involving two intersected fractures.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imputation of missing values in well log data using k-nearest neighbor collaborative filtering","authors":"","doi":"10.1016/j.cageo.2024.105712","DOIUrl":"10.1016/j.cageo.2024.105712","url":null,"abstract":"<div><p>Well log data provide key subsurface information, which is crucial for lithology evaluation and reservoir characterization. However, due to technical issues, well log data may contain missing values at certain depth intervals, which can be detrimental for data analysis. The best method is to reacquire the missing data by relogging, but this increases operational costs. Thus, a cost-efficient method for restoring the lost data is needed to overcome this issue. We propose an imputation method for missing well log data using collaborative filtering, a widely used algorithm for making new item recommendations to users. Although collaborative filtering is mainly used in recommendation systems, its fundamental principle allows us to utilize it to help make predictions for missing log data. The method is applied to a well log dataset obtained from the North Sea near Norway. The results show that the collaborative filtering algorithm has the potential to be a powerful imputation method for missing well log data, but there are some limitations that need to be addressed.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142242006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Petro NLP: Resources for natural language processing and information extraction for the oil and gas industry","authors":"","doi":"10.1016/j.cageo.2024.105714","DOIUrl":"10.1016/j.cageo.2024.105714","url":null,"abstract":"<div><p>Most companies struggle to find and extract relevant information from their technical documents. In particular, the Oil and Gas (O&amp;G) industry faces the challenge of dealing with large amounts of data hidden within old and new geoscientific reports collected over decades of operation. Making this information available in a structured format can unlock valuable information among these <em>mountains</em> of data, which is crucial to support a wide range of industrial and academic applications. However, most natural language processing resources were built from general domain corpora extracted from the Internet and primarily written in English. This paper presents <span>Petro NLP</span>, a comprehensive set of natural language processing and information extraction resources for the oil and gas industry in Portuguese.</p><p>We connected an interdisciplinary team of geoscientists, linguists, computer scientists, petroleum engineers, librarians, and ontologists to build a knowledge graph and several annotated corpora. The <span>Petro NLP</span> resources comprise: (i) <span>Petro KGraph</span>– a knowledge graph populated with entities and relations commonly found on technical reports; and (ii) <span>Petrolês</span>, <span>PetroGold</span>, <span>PetroNER</span>, and <span>PetroRE</span>– sets of corpora containing raw text and documents annotated with morphosyntactic labels, named entities, and relations. These resources are fundamental infrastructure for future research in natural language processing and information extraction in the oil industry. Our ongoing research uses these datasets to train and enhance pre-trained machine learning models that automatically extract information from geoscientific technical documents.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142242005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"THEPORE: A software package for modeling THErmo-PORo-elastic displacements","authors":"","doi":"10.1016/j.cageo.2024.105716","DOIUrl":"10.1016/j.cageo.2024.105716","url":null,"abstract":"<div><p>THEPORE (THErmo-POro-Elastic solutions) is an open source software to perform forward and inverse modeling of ground displacements induced by thermo-poro-elastic sources. The software, implemented in MATLAB, offers a library of analytical and semi-analytical solutions to compute ground displacements induced by thermo-poro-elastic deformation sources of different geometries, embedded in an elastic, homogeneous and isotropic half-space. The solutions have been verified against finite-element simulations. THEPORE includes also an inversion procedure of the deformation data to constrain the source parameters that better fit the observed signals.</p><p>The software's functionality is showcased by inverting the GPS deformation data recorded on Vulcano Island at the onset of the 2021 unrest, in order to estimate the position and volume change of the source responsible for the observed deformations. The results encourage to consider THEPORE as a practical tool suitable for a fast preliminary estimation of the deformation source during a volcanic crisis.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098300424001997/pdfft?md5=21b8a591b1181c37092880b66377dcc3&pid=1-s2.0-S0098300424001997-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142150799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Borehole lithology modelling with scarce labels by deep transductive learning","authors":"","doi":"10.1016/j.cageo.2024.105706","DOIUrl":"10.1016/j.cageo.2024.105706","url":null,"abstract":"<div><p>Geophysical logging is a geo-scientific instrument that detects information such as electric, acoustic, and radioactive properties of a well. Its data plays a vital role in interpreting subsurface geology. However, since logging data is an indirect reflection of rocks, it requires the construction of a logging interpretation model in combination with core samples. Obtaining and analysing all core samples in a well is not practical due to their enormous cost, leading to the problem of scarce core sample labels. This problem can be addressed using semi-supervised learning. Existing studies on lithology identification using logging data mostly utilize graph-based semi-supervised learning, which requires known features to establish a graph Laplacian matrix. Therefore, these methods often use logging values at certain depths to construct feature vectors and cannot learn the shape information of logging curves. In this paper, we propose a semi-supervised learning method with feature learning capability based on semi-supervised generative adversarial network (SSGAN) to learn the shape information of logging curves while utilizing unlabelled logging curves. Additionally, considering the problem of insufficient use of labels when dividing a validation set in extremely scarce-label situations, we propose a strategy of weighted averaging of three sub-models, which effectively improves model performance. We verify the effectiveness of our proposed method on five wells and demonstrate the mechanism of semi-supervised learning using adversarial learning through extensive visualization methods.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}