Journal of Applied Geophysics最新文献

{"title":"Modified approach to estimate effective porosity using density and neutron logging data in conventional and unconventional reservoirs","authors":"Muhammad Abid ,&nbsp;Jing Ba ,&nbsp;Uti Ikitsombika Markus ,&nbsp;Zeeshan Tariq ,&nbsp;Syed Haroon Ali","doi":"10.1016/j.jappgeo.2024.105571","DOIUrl":"10.1016/j.jappgeo.2024.105571","url":null,"abstract":"<div><div>Porosity is a critical petrophysical parameter that governs storage capacity in reservoirs. Despite the introduction of various techniques to assess pore structure, the complexity of rock components and the wide range of pore types have led to limitations in accurately evaluating porosity, particularly in clay-dominant reservoirs. Discrepancies and inconsistencies remain among different analytical calculation methods. Determining porosity using neutron and density logs is especially challenging in the presence of clay minerals and hydrocarbon saturation, particularly gas. Gas saturation reduces rock density, while in clay-dominant formations, neutron logs often indicate excessively high porosity due to the water content in clays. The impact of clay-bound water on rock porosity is still not fully accounted for. This study proposes a modified method for estimating porosity in both conventional and unconventional reservoirs, addressing the effect of clay-bound water on porosity calculations. The proposed method incorporates the rock's composition through its response observed in the neutron and density logs. Analytical equations are formulated to account for the influence of clay-bound water on these logs, and porosity is estimated. To validate the methodology, it was applied to two wells in organic shale reservoirs and one well in a conventional reservoir. The proposed porosity estimation method produced results that closely aligned with previously established methods, demonstrating consistency across all three wells with minimal deviations. This method offers broad applicability for exploration and exploitation in both conventional and unconventional reservoirs.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105571"},"PeriodicalIF":2.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744929","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 powerline noise suppression scheme for the acquisition and processing of CSAMT data","authors":"Meng Wang ,&nbsp;Junlu Wang ,&nbsp;Jianhua Li ,&nbsp;Yuanman Zheng ,&nbsp;Pinrong Lin","doi":"10.1016/j.jappgeo.2024.105569","DOIUrl":"10.1016/j.jappgeo.2024.105569","url":null,"abstract":"<div><div>The recordings of controlled source audio electromagnetic (CSAMT) are invariably contaminated with powerline noise, which seriously impedes the application of CSAMT in densely populated areas. Based on the integer-period cancellation, a powerline noise suppression scheme is described for CSAMT data acquisition and processing. The essence of this scheme is to choose reasonable transmitting frequencies and window lengths of spectrum estimation. According to the specified power transmission standard, a set of CSAMT transmitting-receiving frequencies and sampling lengths can be designed. The effective amplitude and phase can be estimated through dividing the pre-filtered soundings into specified segments for spectrum estimation and Robust stacking. Without involving the magnetic field that is more sensitive to noise, the electric field component is directly converted into the full-field apparent resistivity directly to obtain geoelectric feature. Synthetic and field examples indicate that the nonstandard powerline noise can be effectively suppressed. This scheme can be easily embedded in most of the modern instrumentations, and extend application conditions to high cultural noise areas.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105569"},"PeriodicalIF":2.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Lippmann-Schwinger equation representation of Green's function and its preconditioned generalized over-relaxation iterative solution in wavelet domain","authors":"Yangyang Xu,&nbsp;Jianguo Sun,&nbsp;Huachao Sun","doi":"10.1016/j.jappgeo.2024.105570","DOIUrl":"10.1016/j.jappgeo.2024.105570","url":null,"abstract":"<div><div>The calculation of Green's function is the core of seismic forward and inverse methods based on integral operators. When the Lippmann-Schwinger (L-S) equation is used to calculate Green's function in strongly scattering media, both the Born scattering series and the numerical iterative method encounter issues of slow convergence or divergence. Although the renormalization method derived from quantum mechanics can effectively address the convergence problem of Born scattering series in strong scattering problems, it is acknowledgeed that the convergence conditions and rates of convergence of different reformulation series may vary, and no universal convergence reformulation scattering series exists. Numerical methods for solving integral equations tend to be more general and mathematically robust. In this work, we focus on the numerical solution method of L-S equations. By using a wavelet-domain preconditioner to a reformulated or equivalent Lippmann-Schwinger (L-S) equation, we present an iterative method for numerically solving the equivalent L-S equation aimed at improving the rate of convergence and iteration efficiency in strongly inhomogeneous media. Following Jakobsen et al. (2020), we first introduce a small imaginary component into the background wave number，then rewrite the L-S equation to derive the equivalent complex wave number L-S equation. This reformulation ensures that the coefficient matrix exhibits a banded structure after numerical discretization, allowing the wavelet coefficient matrix to maintain good sparsity. We employ a multi-level fill-in incomplete LU (ILU) factorization method along with a block ILU-based algebraic recursive multilevel solve (ARMS) method in the wavelet domain to generate sparse approximate inverses as preconditioning operators, thereby accelerating the convergence of the generalized successive over-relaxation (GSOR) iterative method. This method is applied to compute numerical Green's functions in strongly inhomogeneous media. Numerical results demonstrate that our method yields simulation outcomes consistent with those obtained from the direct method for solving the original real wave number L-S equation. By testing various preconditioners, we find that the ARMS preconditioner offers significant advantages in operator generation efficiency and non-zero element filling ratio, effectively accelerating the convergence of the GSOR iterative method while achieving higher computational efficiency.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"232 ","pages":"Article 105570"},"PeriodicalIF":2.2,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705899","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":"Deep learning-based geophysical joint inversion using partial channel drop method","authors":"Jongchan Oh ,&nbsp;Shinhye Kong ,&nbsp;Daeung Yoon ,&nbsp;Seungwook Shin","doi":"10.1016/j.jappgeo.2024.105554","DOIUrl":"10.1016/j.jappgeo.2024.105554","url":null,"abstract":"<div><div>Joint inversion stands as a critical technique for the precise determination of subsurface structures by mitigating the ill-posedness inherent in separate geophysical inversion procedures. Recently, the integration of deep learning (DL) into joint inversion has shown promise in achieving more precise interpretations. However, existing DL-based joint inversion approaches face challenges, particularly when survey configurations between training and test datasets vary, and are prone to overfitting towards specific types of data. In response to these limitations, we introduce the Partial Channel Drop (PCD) method applied to DL joint inversion, resulting in a DL-PCD joint inversion model. Our study utilizes gravity, magnetic, and direct current resistivity data as the multiple geophysical data sources and employs 3D U-Net for the DL joint inversion model. The PCD method is implemented during the DL joint inversion training process, yielding a robust and versatile DL-based joint inversion model that can adapt to differing data configurations and manage scenarios with missing data while preventing overfitting and consequent bias in inversion results. Our proposed approach demonstrates superior generalization performance and robustness compared to separate inversion and DL joint inversion without the PCD method, exhibiting resilience even when faced with added noise. The results validate the effectiveness of the PCD method in bolstering the generalization performance of DL joint inversion, laying the groundwork for transformative possibilities in future 3D joint inversion research.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105554"},"PeriodicalIF":2.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658463","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 improved goal-oriented adaptive finite-element method for 3-D direct current resistivity anisotropic forward modeling using nested tetrahedra","authors":"Lewen Qiu ,&nbsp;Jingtian Tang ,&nbsp;Zhengguang Liu","doi":"10.1016/j.jappgeo.2024.105555","DOIUrl":"10.1016/j.jappgeo.2024.105555","url":null,"abstract":"<div><div>We developed a novel adaptive finite element method (FEM) to address the problem of 3-D direct current (DC) resistivity forward modeling with complex surface topography and arbitrary conductivity anisotropy. The tetrahedra-based FEM and secondary virtual potential algorithm are first used to handle arbitrary complex geo-models. Then, to ensure the accuracy of the simulation solution, an improved goal-oriented adaptive mesh refinement (AMR) algorithm is proposed to realize an optimized mesh density distribution. To avoid the drawback of the traditional goal-oriented AMR algorithm for the DC forward modeling problem, we incorporate a volume-based weighting factor into the posterior error estimation procedure to further optimize the density distribution of the forward modeling grid. In addition, instead of traditional open source mesh generation software, we propose using the longest-edge bisection (LEB) algorithm to perform the mesh refinement process, which can preserve the topological structure between different-level meshes. Finally, the comprehensive test using a two-layered model and two complex 3-D models demonstrate the capability of our newly developed code to obtain highly accurate solutions even on relatively coarse initial grids. By incorporating the volume factor, our novel AMR algorithm achieves a more uniform and reasonable mesh density distribution during these experiments. The LEB refinement technique can generate a series of nested tetrahedral elements and provide fewer tetrahedral elements compared to the traditional Delaunay-based AMR method. The proposed 3-D DC forward modeling method has been implemented into an open source C++ code, which will contribute to the advancement of the 3-D DC resistivity imaging field.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105555"},"PeriodicalIF":2.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658353","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":"Advanced predictive modelling of electrical resistivity for geotechnical and geo-environmental applications using machine learning techniques","authors":"Soumitra Kumar Kundu ,&nbsp;Ashim Kanti Dey ,&nbsp;Sanjog Chhetri Sapkota ,&nbsp;Prasenjit Debnath ,&nbsp;Prasenjit Saha ,&nbsp;Arunava Ray ,&nbsp;Manoj Khandelwal","doi":"10.1016/j.jappgeo.2024.105557","DOIUrl":"10.1016/j.jappgeo.2024.105557","url":null,"abstract":"<div><div>Electrical Resistivity (ER) is one of the best geophysical methods for subsurface investigation, especially for geotechnical and geo-environmental studies. Being non-invasive, economical and rapid, this method is highly preferable to geotechnical engineers for continuous evaluation of soil properties along the resistivity profile. Numerous studies have been conducted to correlate the subsurface properties with the ER. However, most of the studies consider a single input variable, which is correlated with the resistivity values using some conventional regression analyses. Very few studies have been conducted to obtain the resistivity value with multiple input parameters, like unit weight, temperature, porosity, moisture content, etc. Since, the soil parameters have a combined effect on resistivity, hence, correlations between the resistivity and the multiple input parameters are urgently required for a better and more reliable result. Moreover, the non-linear properties of soil make the task more complicated. To fill up this research gap, in the present study, 2772 ER tests were conducted using seven different types of soil with different combinations of temperature, density, and water content. Using this database, a Support Vector Regression (SVR), Artificial Neural Network (ANN) model and Extreme Gradient Boosting (XGB) were developed for prediction of ER. It has been understood that all the models are acknowledged as trustworthy data modelling tools. However, the XGB model performs better with an <em>R</em>^<em>2</em> of 0.99 during the training and testing phase. Further, a parametric study was also done to determine, how each input parameter affects the ER. An error analysis was also performed to see the consistent discrepancy between the experimental and projected values of ER. The outcomes validate the robustness of the XGB model, indicating that it can serve as a substitute method for ER prediction.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105557"},"PeriodicalIF":2.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Seal and reservoir risk evaluation using hierarchical clustering analysis with seismic attributes in Northwestern Australia","authors":"Alexandro Vera-Arroyo,&nbsp;Heather Bedle","doi":"10.1016/j.jappgeo.2024.105556","DOIUrl":"10.1016/j.jappgeo.2024.105556","url":null,"abstract":"<div><div>Assessing the presence and quality of reservoir rocks and their sealing capacity is crucial for various applications, including hydrocarbon, geothermal, and CO₂ sequestration projects. Typically, exploration geoscientists rely on seismic attributes and borehole logs into interpretation to integrate diverse data for estimating reservoirs and seals. However, for all seismic interpreters, the process is time-consuming.</div><div>In this study, we explore the application of Hierarchical Clustering Analysis (HCA), an unsupervised machine learning technique, to streamline the integration of multidisciplinary information. While HCA and similar techniques may occasionally misclassify critical data, we demonstrate how to enhance their accuracy by carefully selecting the number of clusters and their calibration with borehole data.</div><div>The novelty of our work is the innovative transformation of HCA clusters into a 3D lithology model, which can significantly facilitate the estimation of reservoir rock and seal-rock juxtaposition risk. Using the HCA clustering hierarchy, five clusters effectively discern the presence and quality of seal and reservoir rock in two different datasets. The classification, in combination with the fault probability, addresses the seal risk offshore the Northern Carnarvon Basin.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"232 ","pages":"Article 105556"},"PeriodicalIF":2.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705897","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":"Recognition and classification of microseismic event waveforms based on histogram of oriented gradients and shallow machine learning approach","authors":"Hongmei Shu ,&nbsp;Ahmad Yahya Dawod ,&nbsp;Longjun Dong","doi":"10.1016/j.jappgeo.2024.105551","DOIUrl":"10.1016/j.jappgeo.2024.105551","url":null,"abstract":"<div><div>Accurate identification of microseismic events is vital for understanding underground rock deformation, rupture behavior, and mechanical properties. This study proposes a method that combines the Histogram of Orientation Gradient (HOG) and shallow machine learning techniques for microseismic waveform recognition. HOG features are extracted from event waveform images, and five classifiers including Linear classifier (LC), Fisher Discriminant (FD), Decision Tree (DT), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) are compared. Experimental results show good accuracy and efficiency, with the SVM classifier and FD classifier achieving the best performance at 97.1 % and 96.9 % accuracy, respectively. Compared to previous studies, this method offers simplicity, ease of use, and low computational resource requirements, making it valuable for real-time monitoring and disaster prediction applications. It provides a foundation for evaluating mine geological structure stability.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105551"},"PeriodicalIF":2.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554617","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":"Prospection of faults on the Southern Erftscholle (Germany) with individually and jointly inverted refraction seismics and electrical resistivity tomography","authors":"Nino Menzel ,&nbsp;Norbert Klitzsch ,&nbsp;Michael Altenbockum ,&nbsp;Lisa Müller ,&nbsp;Florian M. Wagner","doi":"10.1016/j.jappgeo.2024.105549","DOIUrl":"10.1016/j.jappgeo.2024.105549","url":null,"abstract":"<div><div>As part of the Lower Rhein Embayment (LRE), the Southern Erft block is characterized by a complex tectonic setting that influences hydrological and geological conditions on a local as well as regional level. The study area is located in the south of North Rhine-Westphalia and traversed by several NW-SE-oriented fault structures. Since the tectonic structures were located by past studies based on a sparse foundation of geological data, the positions include considerable uncertainties. Therefore, it was decided to re-evaluate and refine the assumed fault locations by conducting geophysical measurements. Seismic Refraction Tomography (SRT) as well as Electrical Resistivity Tomography (ERT) was performed along seven measurement profiles with a length of up to 1.1 km. In addition to compiling individual resistivity and velocity models for all deduced measurements, ERT and SRT datasets were cooperatively inverted using the Structurally Coupled Cooperative Inversion (SCCI). This algorithm strengthens structural similarities between velocity and resistivity by adapting the individual regularizations after each model iteration. Previously assumed locations of the tectonic structures diverge from the new evidence based on ERT and SRT surveys. Especially in the western and eastern parts of the research area, differences between the survey results and formerly assumed locations are in the order of 100 m. Seismic and geoelectric measurements further indicate a fault structure in the southern part of the area, which remained undetected by past studies. The cooperative inversions do not improve the geophysical models qualitatively, since the individually inverted datasets already provide results of good quality and resolution.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105549"},"PeriodicalIF":2.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Salt dome identification using incremental semi-supervised learning and unsupervised learning-based label generation","authors":"Kui Wu ,&nbsp;Wei Hu ,&nbsp;Yu Qi ,&nbsp;Yixin Yu ,&nbsp;Sanyi Yuan","doi":"10.1016/j.jappgeo.2024.105552","DOIUrl":"10.1016/j.jappgeo.2024.105552","url":null,"abstract":"<div><div>Salt domes represent distinctive geological anomalies in seismic data, crucial for pinpointing hydrocarbon reservoirs and strategizing drilling paths. Conventional seismic attributes or computer vision methods usually fail to capture the intricate details of salt domes, resulting in interpretation results marred by noise. While deep learning presents a promising approach for intelligent 3D salt dome interpretation, its effectiveness is heavily dependent on the availability of labeled samples. To facilitate accurate interpretation, we propose an innovative workflow that integrates an unsupervised label generation component with an incremental semi-supervised learning framework utilizing the U-Net architecture. To generate salt dome labels, we prioritize both the root mean square (RMS) amplitude attribute and variance attribute (VA) as foundational data. Utilizing convolutional autoencoders (CAE), we establish a relationship between the input RMS attribute and the output reconstructed attribute. The intermediate features extracted by CAE are transformed into the salt boundary feature via principal component analysis and K-Means clustering. Concurrently, we employ K-Means clustering on VA to ascertain the salt internal feature. We further propose a feature aggregation method to consolidate the salt boundary feature and the salt internal feature for label generation of the salt dome. For 3D salt dome interpretation, we begin by predicting adjacent test datasets using labels generated by the unsupervised salt dome label generation module. The prediction results of these test datasets are then integrated into the training datasets to enhance the interpretation performance of U-Net, steering it towards an incremental semi-supervised learning method for salt dome interpretation. Additionally, we extend this research by applying transfer learning techniques for identifying mound-shoals using the same semi-supervised model parameters initially developed for interpreting salt domes. This method is validated using datasets from the Netherlands F3 block for salt domes and the North China block for mound-shoals. The results demonstrate that this innovative process only requires a minimal number of labels from unsupervised methods to precisely interpret salt domes across 3D seismic data. Furthermore, the low-level features of salt domes learned from neural network can be seamlessly transferred to mound-shoal identification. This automated approach significantly streamlines the interpretation process, reducing the time and resources traditionally necessary for reservoir analysis.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105552"},"PeriodicalIF":2.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579052","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}