81st EAGE Conference and Exhibition 2019最新文献_第3页

Implementing New Technology to Revitalize Central North Sea Seismic Via Evolutionary Processing 通过演化处理实现北海中部地震活化新技术

81st EAGE Conference and Exhibition 2019 Pub Date : 2019-06-03 DOI: 10.3997/2214-4609.201901181

P. Hayes, M. Townsend

引用次数: 0

Pre-Processing a Land Walkaway VSP Dataset for Elastic FWI: Effects of Deconvolution Operations 弹性FWI的陆地步行VSP数据预处理:反卷积操作的影响

81st EAGE Conference and Exhibition 2019 Pub Date : 2019-06-03 DOI: 10.3997/2214-4609.201900991

R. Cova, K. Innanen, M. Rauch-Davies

引用次数: 0

Quantification of Uncertainties of Fracture Permeability Via Mud Loss Information and Inverse Stochastic Modeling 利用失泥信息和逆随机模型量化裂缝渗透率的不确定性

81st EAGE Conference and Exhibition 2019 Pub Date : 2019-06-03 DOI: 10.3997/2214-4609.201901624

A. Guadagnini, A. Russian, M. Riva, E. Russo, M. Chiaramonte

{"title":"Quantification of Uncertainties of Fracture Permeability Via Mud Loss Information and Inverse Stochastic Modeling","authors":"A. Guadagnini, A. Russian, M. Riva, E. Russo, M. Chiaramonte","doi":"10.3997/2214-4609.201901624","DOIUrl":"https://doi.org/10.3997/2214-4609.201901624","url":null,"abstract":"Summary This study provides rigorous quantification of uncertainties associated with fracture permeability estimation obtained through stochastic inverse modeling of mud losses recorded while drilling. Fracture characterization is performed in terms of fracture width estimation and is grounded on a stochastic inverse modeling technique. Implementation of the latter rests on a well-defined set of parameters, including drilling fluid, rheological properties, flow rates, pore and dynamic drilling fluid pressure, wellbore geometry. These quantities are generally affected by diverse sources of uncertainty. Drilling mud is modeled as a Herschel–Bulkley fluid. Open fractures are treated as horizontal planes intersecting the wellbore and a simple analytical solution is employed to express mud flow advancement in the fracture as a function of drilling fluid properties and operational conditions. A modern global sensitivity analysis approach is employed to quantify the way uncertain model parameters affect fracture aperture (hence permeability) and extent. Uncertainty propagation from input parameters to model outputs is investigated and quantified through a workflow implemented within a Monte Carlo framework. It is then employed in the context of stochastic inverse modeling of field cases to evaluate posterior probability densities of fracture aperture and to simulate drilling fluid invasion in fractures in quasi-real time during drilling.","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79769652","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

Clair Ridge: Learnings From Processing the Densest OBN Survey in the UKCS 克莱尔·里奇:从处理英国最密集的OBN调查中学习

81st EAGE Conference and Exhibition 2019 Pub Date : 2019-06-03 DOI: 10.3997/2214-4609.201901182

P. Tillotson, D. Davies, M. Ball, L. Smith

引用次数: 2

Application of Geostatistical Seismic AVA Inversion for Shale Reservoir Characterization and Brittleness Prediction with Machine Learning 地统计地震AVA反演在页岩储层表征及机器学习脆性预测中的应用

81st EAGE Conference and Exhibition 2019 Pub Date : 2019-06-03 DOI: 10.3997/2214-4609.201900691

M. Cyz, L. Azevedo, M. Malinowski

{"title":"Application of Geostatistical Seismic AVA Inversion for Shale Reservoir Characterization and Brittleness Prediction with Machine Learning","authors":"M. Cyz, L. Azevedo, M. Malinowski","doi":"10.3997/2214-4609.201900691","DOIUrl":"https://doi.org/10.3997/2214-4609.201900691","url":null,"abstract":"Summary In this study we present an application of geostatistical AVA seismic inversion method for characterization of a unconventional Lower Paleozoic shale reservoir in Northern Poland. The target formations are of a small thickness (up tp 25 meters) and deeply buried (ca. 3 km) what makes their delineation and characterization especially difficult. An application of the iterative geostatistical AVA inversion method allowed for obtaining the high-resolution density, P-wave and S-wave velocity models together with the assessment of the uncertainty on the predictions. The obtained elastic property models were compared with the results of the deterministic simultaneous Amplitude-versus-Offset inversion proving that the application of a such sophisticated (geostatistical) inversion technique is a must while dealing with the thin and highly variable layers. The inverted elastic models where further used to improve the prediction of a spatial distribution of the brittleness index with a machine learning (PSVM) algorithm by integrating well-log data and seismic rock property volumes.","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81733321","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: 2

Experimental Research on the Gas Hydrate Based on T2 and T1–T2 Low Field NMR Technique 基于T2和T1-T2低场核磁共振技术的天然气水合物实验研究

81st EAGE Conference and Exhibition 2019 Pub Date : 2019-06-03 DOI: 10.3997/2214-4609.201901641

J. Liu, Y. Fan, X. Ge, D. Xing, T. Qiu

引用次数: 0

Desert Seismic Noise Attenuation Based on Bayesian Mathematical Morphology Filtering 基于贝叶斯数学形态学滤波的沙漠地震噪声衰减

81st EAGE Conference and Exhibition 2019 Pub Date : 2019-06-03 DOI: 10.3997/2214-4609.201901353

S. Wang, Y. Li, H. Lin

引用次数: 1

Offset-Dependent Overburden Time-Shifts from Ultrasonic Data 基于超声数据的偏移相关覆盖层时移

81st EAGE Conference and Exhibition 2019 Pub Date : 2019-06-03 DOI: 10.3997/2214-4609.201900804

A. Bakk, R. Holt, A. Bauer, B. Dupuy, M. Romdhane

{"title":"Offset-Dependent Overburden Time-Shifts from Ultrasonic Data","authors":"A. Bakk, R. Holt, A. Bauer, B. Dupuy, M. Romdhane","doi":"10.3997/2214-4609.201900804","DOIUrl":"https://doi.org/10.3997/2214-4609.201900804","url":null,"abstract":"Depletion or injection into a reservoir implies stress and strain changes in the reservoir and its surroundings. This may lead to measurable time-shifts for seismic waves propagating in the subsurface. We have measured multi-directional ultrasonic P-wave velocity changes for three different field shale cores, each probed with four different stress paths (i.e. different ratios between the horizontal and the vertical stress change), to systematically quantify the time-shifts for overburden shales with respect to ray angle (offset). The laboratory data show that for a given offset, the time-shifts are stress path dependent, where the isotropic stress path is associated with larger time-shifts as compared to the constant mean stress path or the triaxial stress path. Generally, the time-shifts are largest for zero offset (propagation normal to the bedding) and are decreasing for increasing offsets. The constant mean stress path has the most significant decrease of time-shifts with offset. By utilizing pre-stack seismic offset data, such controlled laboratory experiments can be used to constrain the inversion of 4D seismic data to quantify the stress and strain changes due to production. This may have important implications for improved recovery and safety, particularly in mature fields.","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84895015","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: 2

Survey Design Towards Optimum Reflectivity and Velocity Estimates Directly from Blended and Irregularly-Sampled Data 直接从混合和不规则采样数据中获得最佳反射率和速度估计的调查设计

81st EAGE Conference and Exhibition 2019 Pub Date : 2019-06-03 DOI: 10.3997/2214-4609.201901052

S. Nakayama, G. Blacquière, T. Ishiyama

{"title":"Survey Design Towards Optimum Reflectivity and Velocity Estimates Directly from Blended and Irregularly-Sampled Data","authors":"S. Nakayama, G. Blacquière, T. Ishiyama","doi":"10.3997/2214-4609.201901052","DOIUrl":"https://doi.org/10.3997/2214-4609.201901052","url":null,"abstract":"The application of blended acquisition along with irregular acquisition geometries contributes to the economic perspective of a seismic survey. The joint migration inversion scheme is capable of directly processing the data acquired in this way, i.e., without deblending or data reconstruction, and of subsequently estimating both reflectively and velocity models. The workflow proposed in this study aims to design the source blending operator as well as detector and source sampling operators. The approach iteratively computes these parameters in such a way that the quality of reflectivity and velocity models, which are directly estimated from blended and irregularly-sampled data, is adequate. The workflow integrates a genetic algorithm and a convolutional neural network to derive optimum parameters. Bio-inspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of a joint migration inversion, we utilize a convolutional neural network. The applied network architecture discards suboptimal solutions among newly generated ones. Conversely, it passes optimal ones to the subsequent step, which successfully enhances the efficiency of the proposed approach. The resultant acquisition scenario yields a notable enhancement in both reflectivity and velocity estimates attributed solely to the choice of survey parameters.","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84935451","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

Automatic Classification of Microseismic Signals Related to Mining Activities by Supervised Learning 基于监督学习的采矿微震信号自动分类

81st EAGE Conference and Exhibition 2019 Pub Date : 2019-06-03 DOI: 10.3997/2214-4609.201900757

W. Choi, C. Kim, D. Cheon, S. Pyun

引用次数: 1