Computers & Geosciences最新文献

{"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":"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":"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}

{"title":"Enhancing machine learning thermobarometry for clinopyroxene-bearing magmas","authors":"","doi":"10.1016/j.cageo.2024.105707","DOIUrl":"10.1016/j.cageo.2024.105707","url":null,"abstract":"<div><p>In this study, we proposed a general workflow that aims to enhance the ML-based geothermobarometer modelling. Our workflow focuses on three key areas. Firstly, we developed a robust pre-processing pipeline that addresses data imbalance, feature engineering, and data augmentation. Secondly, we assessed modelling errors using a Monte Carlo approach to quantify the impact of analytical uncertainties on the final pressure and temperature estimates. Thirdly, we implemented a robust strategy to validate and test the ML models to avoid over- and under-fitting issues while correcting biases associated with the application of specific ML models (i.e., tree-based ensembles).</p><p>To facilitate the use of our workflow, we have developed a web app (<span><span>https://bit.ly/ml-pt-web</span><svg><path></path></svg></span>) and a Python module (<span><span>https://bit.ly/ml-pt-py</span><svg><path></path></svg></span>). The robustness of this strategy has been tested on two calibrations: clinopyroxene (cpx) and clinopyroxene-liquid (cpx-liq). Our results show a significant reduction in errors compared to the baseline model, as well as good generalization ability on an independent external dataset. The Root Mean Squared Errors are 57 °C and 2.5 kbar for the cpx calibration, and 36 °C and 2.1 kbar for the cpx-liq calibration. Finally, our models show improved outcomes on the external dataset compared to existing ML and classical cpx and cpx-liq thermobarometers.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098300424001900/pdfft?md5=35a76aa189a72d9015dd976686c4e57f&pid=1-s2.0-S0098300424001900-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173049","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":"Research on microseismic signal identification through data fusion","authors":"","doi":"10.1016/j.cageo.2024.105708","DOIUrl":"10.1016/j.cageo.2024.105708","url":null,"abstract":"<div><p>The present study proposes a double-branch classification network, DPNet (Double Path Net), for the classification and identification of microseismic and blasting signals based on multimodal feature extraction. The vibration signals’ one-dimensional spectrogram and two-dimensional wavelet time–frequency graph are inputted into the double branch network. Subsequently, convolutional neural networks and ResNet are employed to extract the one-dimensional frequency features and two-dimensional time–frequency features of the vibration signals, respectively. Experimental results demonstrate that our proposed method achieves outstanding classification performance with an accuracy of 97.34% for microseismic signals and blasting signals. This research not only provides innovative solutions to practical problems but also introduces a novel idea of multimodal feature extraction at a theoretical level. By successfully applying it to efficiently classify complex signals in mining engineering, we offer a feasible solution that holds promising prospects for practical applications in this field.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122340","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":"Massively parallel modeling of electromagnetic field in conductive media: An MPI-CUDA implementation on Multi-GPU computers","authors":"","doi":"10.1016/j.cageo.2024.105710","DOIUrl":"10.1016/j.cageo.2024.105710","url":null,"abstract":"<div><p>Numerical modeling of electromagnetic (EM) fields in a conductive marine environment is crucial for marine EM data interpretation. During marine controlled-source electromagnetic (MCSEM) surveys, a variety of transmitter locations are used to introduce electric currents. The resulting electric and magnetic fields are then concurrently logged by a network of receivers. The forward simulation of MCSEM data for a subsea structure whose electrical properties vary in all three dimensions is computationally intensive. We demonstrate how such computations may be substantially accelerated by adapting algorithms to operate efficiently on modern GPUs with many core architectures. The algorithm we present features a hybrid MPI-CUDA programming model suitable for multi-GPU computers and consists of three levels of parallelism. We design the optimal kernels for different components to minimize redundant memory accesses. We have tested the algorithm on NVIDIA Kepler architecture and achieved up to 105 × speedup compared with the serial code version. We further showcased the algorithm's performance advantages through its application to a realistic marine model featuring complex geological structures. Our algorithm's significant efficiency increase opens the possibility of 3D MCSEM data interpretation based on probabilistic or machine learning approaches, which require tens of thousands of forward simulations for every survey.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142157755","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":"Combining deep neural network and spatio-temporal clustering to automatically assess rockburst and seismic hazard – Case study from Marcel coal mine in Upper Silesian Basin, Poland","authors":"","doi":"10.1016/j.cageo.2024.105709","DOIUrl":"10.1016/j.cageo.2024.105709","url":null,"abstract":"<div><p>Mine induced seismic events are a major safety concern in mining and require careful monitoring and management to reduce their effects. Therefore, an essential step in assessing seismic and rock burst hazards is the analysis of mine seismicity. Recently, deep neural networks have been used to automatically determine seismic wave arrival times, surpassing human performance and allowing their use in seismic data analysis such as seismic event location and seismic energy calculation. In order to properly automate the rockburst and seismic hazard assessment deep neural network phase picker and a spatio-temporal clustering method were utilized. Seismic and rockburst hazards were statistically quantified using two-way contingency tables for two categorical variables: seismic energy level of mine tremors and number of clusters. Correlations between several spatio-temporal clusters and a statistical association between two categorical variables: seismic energy level and cluster number indicate an increase of seismic hazard in the Marcel hard coal mine in Poland. A new automated tool has been elaborated to automatically identify high-stress areas in mines in the form of spatio-temporal clusters.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098300424001924/pdfft?md5=2bf4c7ce15a9d7979aa62ba8147334ed&pid=1-s2.0-S0098300424001924-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136832","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":"DRRGlobal: Uncovering the weak phases from global seismograms using the damped rank-reduction method","authors":"","doi":"10.1016/j.cageo.2024.105687","DOIUrl":"10.1016/j.cageo.2024.105687","url":null,"abstract":"<div><p>Some target seismic signals in the earthquake data can be very weak compared with interfering phases, and are thus difficult to detect, which further hinders the effective usage of these weak phases for subsequent high-resolution imaging of earth interiors. The strong ambient noise makes this situation even more troublesome since the weak signals can be mostly buried in the noise. Here, we present an open-source package for uncovering the weak phases from global seismograms. We adopt a two-step scheme to reconstruct and denoise array data. The first step is weighted average interpolation which puts the data into irregular grids. The second step adopts the weighted projection-onto-convex sets based on damped rank-reduction to further interpolate and denoise for the binned data. Taking the complexity of the weak signal into consideration, we adopt the automatic strategy to select an appropriate rank in different localized windows. We conduct several synthetic tests to carefully investigate the performance regarding effectiveness, robustness, and efficiency, and compare the algorithm with the frequency–wavenumber-domain projection onto convex sets method that is already used in the global seismology literature. Finally, the proposed framework is validated via a recorded array data set of the 1995 May 5 Philippines earthquake.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050260","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":"Forward modeling of single-sided magnetic resonance and evaluation of T2 fitting error based on geometric analytical method","authors":"","doi":"10.1016/j.cageo.2024.105705","DOIUrl":"10.1016/j.cageo.2024.105705","url":null,"abstract":"<div><p>Single-sided magnetic resonance (SSMR) offers advantages of portability and noninvasive measurement for water detection, with significant potential applications in groundwater exploration, petroleum well logging, and soil moisture monitoring. However, the inherent highly inhomogeneous static magnetic field and radiofrequency (RF) field in SSMR necessitate the utilization of the Carr–Purcell–Meiboom–Gill (CPMG) sequence measurement scheme. To accelerate forward modeling during pulse excitation, we introduce a Geometric Analysis Method (GAM) and assess <span><math><msub><mrow><mi>T</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> error using its primary parameters. The GAM involves applying spatial geometric rotations on the magnetization vector, leading to an analytical solution to the Bloch equation that disregards relaxation effects. Compared with the rotation matrix (RM) method, the GAM demonstrates high accuracy and reduces computational time by approximately 20.9%. By analyzing the primary parameters governing the magnetization vector in the analytical formula, we evaluated their impact on the transverse relaxation time (<span><math><msub><mrow><mi>T</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>) obtained through fitting the SE signal. Ultimately, the forward modeling results of the CPMG sequence within the region of interest (ROI) of a single-sided Halbach magnet array are validated. The <span><math><msub><mrow><mi>T</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> fitting error increases as the primary parameters deviate from the ideal values, highlighting their significant role in the <span><math><msub><mrow><mi>T</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> fitting results. This study provides a theoretical foundation for optimizing the design of SSMR magnets and RF coils.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044498","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}