82nd EAGE Annual Conference & Exhibition最新文献

A New Workflow to Enhance Intercept and Gradient Data Quality 提高截距和梯度数据质量的新工作流程

82nd EAGE Annual Conference & Exhibition Pub Date : 2023-07-01 DOI: 10.3997/2214-4609.202112410

H. P. Huu

引用次数: 0

Seismic Monitoring of the United Downs Deep Geothermal Power Project (UDDGP) Site with Public Seismic Networks 利用公共地震台网对联合唐斯深层地热发电项目(UDDGP)场地进行地震监测

82nd EAGE Annual Conference & Exhibition Pub Date : 2021-10-20 DOI: 10.3997/2214-4609.202113053

G. Rodríguez-Pradilla, J. Verdon

引用次数: 0

A Procedure for Quantifying a UAV-borne Magnetometer Vertical Setback Distance 一种量化无人机载磁强计垂直后退距离的程序

82nd EAGE Annual Conference & Exhibition Pub Date : 2021-10-18 DOI: 10.3997/2214-4609.202113170

C. Walter, A. Braun, G. Fotopoulos

{"title":"A Procedure for Quantifying a UAV-borne Magnetometer Vertical Setback Distance","authors":"C. Walter, A. Braun, G. Fotopoulos","doi":"10.3997/2214-4609.202113170","DOIUrl":"https://doi.org/10.3997/2214-4609.202113170","url":null,"abstract":"Summary Within this study, a vertical buzz test methodology is applied to characterize the distance at which the electromagnetic interference generated by a UAV platform attenuates below the sensitivity threshold of a high-resolution magnetometer in a controlled setting. A DJI Wind 4 heavy-lift, multi-rotor UAV platform was used to characterize the spatial extent of the electromagnetic interference generated inflight. The vertical setback distance of a UAV-borne aeromagnetic system was characterized using a vertical buzz test maneuver in a magnetically quiet area. Through conducting the characterization test, it was determined that the DJI Wind 4 with a 2.2 kg payload required a vertical setback distance of approximately 5 m when surveying with a magnetometer employing a sensitivity of 0.01 nT. Furthermore, it was determined that a magnetometers vertical setback distance is unique for each specific combination of UAV platform and magnetometer employed within a UAV-borne aeromagnetic system. Based on previous tests, using the same magnetometer and methodology, the vertical setback distance was determined to be 3 m, for both a DJI - S900 and M600. Therefore, the assessment shown herein should be conducted to characterize the vertical setback distance for specific UAV magnetometry systems (each platform and magnetometer) prior to conducting surveys.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115453825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning approach for joint inversion of DC Resistivity and MT data 直流电阻率与大地电磁法联合反演的深度学习方法

82nd EAGE Annual Conference & Exhibition Pub Date : 2021-10-18 DOI: 10.3997/2214-4609.202113268

A. Singh, D. Vashisth, S. Srivastava

{"title":"A deep learning approach for joint inversion of DC Resistivity and MT data","authors":"A. Singh, D. Vashisth, S. Srivastava","doi":"10.3997/2214-4609.202113268","DOIUrl":"https://doi.org/10.3997/2214-4609.202113268","url":null,"abstract":"Summary Joint inversion of multiple geophysical datasets has its own set of advantages for interpreting the geology of an area. Using neural networks (NN), we propose the joint inversion of MT and DC apparent resistivity datasets to delineate the subsurface conductivity distribution. The NN model used is inspired by the Siamese networks to provide different prediction channels for the two different datasets before integrating them to get the layered earth parameters. The NN model trained on the specified range of model parameters has predicted each layers’ resistivity and thickness close to the true values for all the four types of resistivity distribution (A, Q, H, and K) for a three-layered earth model and takes advantage of the two different datasets to see through the equivalence problem to detect the thin second layer for an H type curve. The NN model accurately estimated the resistivity distribution even when the true data was corrupted with 10% Gaussian noise. Not only the method proposed provides good results for all the models considered but also saves time over other optimisation techniques where every model requires separate simulation. The method, therefore, proves to be a fast, efficient and reliable way for joint inversion of geophysical datasets.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115598266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new determining porosity method based on four detectors and D-T neutron source 基于四探测器和D-T中子源的孔隙度测定新方法

82nd EAGE Annual Conference & Exhibition Pub Date : 2021-10-18 DOI: 10.3997/2214-4609.202113190

F. Junting, Z. Feng, Z. Hui, F. Jilin

引用次数: 0

Advanced Ultrasonic technology measuring annulus thickness and detecting formation collapse on casing 先进的超声技术测量环空厚度，探测套管地层塌陷

82nd EAGE Annual Conference & Exhibition Pub Date : 2021-10-18 DOI: 10.3997/2214-4609.202112985

O. Alzankawi, M. Fitouri, P. Rebaud

引用次数: 0

A high resolution method of seismic data via joint dictionary learning and sparse representation 基于联合字典学习和稀疏表示的高分辨率地震数据提取方法

82nd EAGE Annual Conference & Exhibition Pub Date : 2021-10-18 DOI: 10.3997/2214-4609.202113239

G. Zhang, Y. Wang, C. Liu, B. She, B. Zou

{"title":"A high resolution method of seismic data via joint dictionary learning and sparse representation","authors":"G. Zhang, Y. Wang, C. Liu, B. She, B. Zou","doi":"10.3997/2214-4609.202113239","DOIUrl":"https://doi.org/10.3997/2214-4609.202113239","url":null,"abstract":"Summary The traditional deconvolution methods have some disadvantages, such as suppressing weak reflection coefficients and are difficult to identify thin interbedding and so on. In order to overcome these shortcomings, this paper presents a new approach to improve the resolution of seismic data, based upon joint dictionary learning and sparse representation (JDLSR). The characteristics of reflection coefficients can be obtained by dictionary learning. In order to explore the correspondence between seismic data and reflection coefficients more efficiently, we introduce the joint dictionary learning. The combined features (DR and DS) of log reflection coefficients and seismic data of well beside can be learned by joint dictionary learning. The known seismic data are sparsely represented under DS to obtain the representation coefficient, which can be combined with DR to reconstruct the unknown reflection coefficients. The effectiveness of the proposed method is verified by the single-channel seismic data and the classical Marmousi model. This method is applied to high-resolution processing of actual seismic data, and it is found that the result is better than sparse-spike deconvolution (SSD).","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124912206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Entropy-driven particle swarm optimization for reservoir modelling under geological uncertainty – application to a fractured reservoir 地质不确定性条件下储层建模的熵驱动粒子群优化——在裂缝性储层中的应用

82nd EAGE Annual Conference & Exhibition Pub Date : 2021-10-18 DOI: 10.3997/2214-4609.202113321

B. Steffens, V. Demyanov, D. Arnold, H. Lewis

{"title":"Entropy-driven particle swarm optimization for reservoir modelling under geological uncertainty – application to a fractured reservoir","authors":"B. Steffens, V. Demyanov, D. Arnold, H. Lewis","doi":"10.3997/2214-4609.202113321","DOIUrl":"https://doi.org/10.3997/2214-4609.202113321","url":null,"abstract":"Summary In this work we introduce a novel reservoir modelling workflow where modelling is assisted by an entropy-driven particle swarm optimizer. Producing a representative range of reservoir models that cover geological uncertainties in an effective way is a challenging task. We therefore make use of entropy to ensure that the ensemble of generated models adequately reflects the available information and provides diversity that reflects the associated variability in fluid flow behavior. The workflow is tested on a synthetic case study of a fractured reservoir. The results indicate that the entropy-driven PSO is able to prevent the diversity of the ensemble of models from collapsing whilst staying within the bounds of a predefined expected dynamic flow response. It is also shown that the entropy-driven PSO outperforms a standard PSO in this task. Secondary outcomes from the workflow, such as a spatial entropy map, provide a great tool for further uncertainty assessment and can be used to identify swept or unswept reservoir regions and the regions where more information is needed to reduce the uncertainty.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126619199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tertiary Technostratigraphic Evolution of the Veslemøy High and Sørvestnaget Basin, Western Barents Sea 西巴伦支海Veslemøy High和Sørvestnaget盆地第三纪地层演化

82nd EAGE Annual Conference & Exhibition Pub Date : 2021-10-18 DOI: 10.3997/2214-4609.202113245

L. Torgersen, D. Marín

{"title":"Tertiary Technostratigraphic Evolution of the Veslemøy High and Sørvestnaget Basin, Western Barents Sea","authors":"L. Torgersen, D. Marín","doi":"10.3997/2214-4609.202113245","DOIUrl":"https://doi.org/10.3997/2214-4609.202113245","url":null,"abstract":"Summary The Tertiary succession is a promising reservoir in the western margin of the Barents Sea. Previous studies have mapped injectites and submarine fans in a single 3D seismic dataset in the Sorvestnaget Basin. Wells have confirmed the reservoir potential, but the lateral variation of these reservoir rocks and the role of active tectonics in their distribution is not yet properly understood. Even though both the tectonics and the sedimentation are studied, they are only studied separately and there are no references displaying the role of tectonics on the sedimentation for the Tertiary succession in this area. The technostratigraphical evolution during Tertiary in the study area can be related to the regional geological mechanism such as, rifting, uplift, subsidence, and glaciation. All of these which have an important factor for controlling the deposition and remobilization of sediments in the subsurface. Salt halokinesis have together with the regional tectonic events also affected the Tertiary succession to a large degree as a major halokinetic event occurred during Eocene, reworking the sediments by truncation, erosion, and the formation of local deep basins.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122689144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Separating and Sparse Imaging of GPR Diffractions by Dictionary Learning and Least-Squares Migration 基于字典学习和最小二乘迁移的探地雷达衍射分离与稀疏成像

82nd EAGE Annual Conference & Exhibition Pub Date : 2021-10-18 DOI: 10.3997/2214-4609.202010793

J. Zhao, S. Peng, X. Cui

引用次数: 0