Petroleum Geostatistics 2019最新文献_第4页

Seismic Waveform Inversion of Elastic Properties Using an Iterative Ensemble Kalman Smoother 基于迭代集合卡尔曼平滑的地震波形弹性特性反演

Petroleum Geostatistics 2019 Pub Date : 2019-09-02 DOI: 10.3997/2214-4609.201902267

M. Gineste, J. Eidsvik

引用次数: 0

Integrated Geo-modelling and Ensemble History Matching of Highly Faulted Turbiditic Reservoir Model 高断陷浊积岩储层模型综合地质建模与集合历史拟合

Petroleum Geostatistics 2019 Pub Date : 2019-09-02 DOI: 10.3997/2214-4609.201902200

V. Zaccardi, A. Abadpour, N. Haller, P. Berthet, D. Rappin, J. Grange-Praderas

引用次数: 1

Random Walk for Simulation of Geobodies: A New Process-like Methodology for Reservoir Modelling 模拟地质体的随机游走:一种新的类过程油藏建模方法

Petroleum Geostatistics 2019 Pub Date : 2019-09-02 DOI: 10.3997/2214-4609.201902243

G. Massonnat

引用次数: 1

Raising the Bar: Electrofacies as a Framework for Improving the Practice of Geomodeling 提高标准:电相作为改进地质建模实践的框架

Petroleum Geostatistics 2019 Pub Date : 2019-09-02 DOI: 10.3997/2214-4609.201902214

D. Garner

引用次数: 0

Well Log Data Standardization, Imputation and Anomaly Detection Using Hidden Markov Models 基于隐马尔可夫模型的测井数据标准化、归算与异常检测

Petroleum Geostatistics 2019 Pub Date : 2019-09-02 DOI: 10.3997/2214-4609.201902208

K. Struminskiy, A. Klenitskiy, A. A. Reshytko, D. Egorov, A. Shchepetnov, A. Sabirov, D. Vetrov, A. Semenikhin, O. Osmonalieva, B. Belozerov

引用次数: 2

Correlation Analysis of Fracture Intensity Descriptors with Different Dimensionality in a Geomechanics-constrained 3D Fracture Network 地质力学约束下三维裂缝网络中不同维数裂缝强度描述符的相关性分析

Petroleum Geostatistics 2019 Pub Date : 2019-09-02 DOI: 10.3997/2214-4609.201902235

W. Zhu, B. Yalcin, S. Khirevich, T. Patzek

引用次数: 4

Statistical Characterisation of Fluvial Sand Bodies: Implications for Complex Reservoir Models 河流砂体的统计特征:对复杂储层模型的影响

Petroleum Geostatistics 2019 Pub Date : 2019-09-02 DOI: 10.3997/2214-4609.201902215

M. Franzel, S. Jones, Ian H. Jermyn, M. Allen, K. McCaffrey

{"title":"Statistical Characterisation of Fluvial Sand Bodies: Implications for Complex Reservoir Models","authors":"M. Franzel, S. Jones, Ian H. Jermyn, M. Allen, K. McCaffrey","doi":"10.3997/2214-4609.201902215","DOIUrl":"https://doi.org/10.3997/2214-4609.201902215","url":null,"abstract":"Summary The three-dimensional geometry of fluvial channel sand bodies has received considerably less attention than their internal sedimentology, despite the importance of sandstone body geometry for subsurface reservoir modelling. The aspect ratio (width/thickness, W:T) of fluvial channels is widely used to characterize their geometry. However, this does not provide a full characterization of fluvial sand body shape, since one W:T ratio can correspond to many different channel geometries. The resultant over- or underestimation of the cross-sectional area of a sand body can have significant implications for reservoir models and hydrocarbon volume predictions. There is thus a clear need for the generation of versatile, quantitative, and statistically robust models for sand body shape. The main aim of this research is to develop a new statistically-based approach that will provide quantitative data, derived from outcrop analogues, to fully constrain stochastic fluvial reservoir models. Here, we describe the construction of a new shape database and conduct a preliminary qualitative analysis in order to understand measurement and other uncertainties, and to explore the catalogue of shape configurations. A future quantitative analysis will develop a predictive model to enable forecasting of reservoir channel sand body geometries and shapes that can be built into existing reservoir models.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125298222","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: 1

Automatic Scenarios Extraction from Depth Uncertainty Evaluation 基于深度不确定度评估的场景自动提取

Petroleum Geostatistics 2019 Pub Date : 2019-09-02 DOI: 10.3997/2214-4609.201902184

Pedro Correia, J. Chautru, Y. Meric, F. Geffroy, H. Binet, P. Ruffo, L. Bazzana

引用次数: 0

Feedback Between Gravity and Viscous Forces in Two-phase Buckley-Leverett Flow in Randomly Heterogeneous Permeability Fields 随机非均质渗透率场中两相Buckley-Leverett流重力与粘滞力的反馈

Petroleum Geostatistics 2019 Pub Date : 2019-09-02 DOI: 10.3997/2214-4609.201902185

P. Alikhani, A. Guadagnini, F. Inzoli

{"title":"Feedback Between Gravity and Viscous Forces in Two-phase Buckley-Leverett Flow in Randomly Heterogeneous Permeability Fields","authors":"P. Alikhani, A. Guadagnini, F. Inzoli","doi":"10.3997/2214-4609.201902185","DOIUrl":"https://doi.org/10.3997/2214-4609.201902185","url":null,"abstract":"Summary Data on hydrocarbon reservoir attributes (e.g., permeability, porosity) are only available at a set of sparse locations, thus resulting (at best) in an incomplete knowledge of spatial heterogeneity of the system. This lack of information propagates to uncertainty in our evaluations of reservoir performance and of the resulting oil recovery. We consider a two-phase flow setting taking place in a randomly heterogeneous (correlated) permeability field to assess the feedback between viscous and gravity forces in a numerical Monte Carlo context and finally characterize oil recovery estimates under uncertainty for a water flooding scenario. Our work leads to the following major conclusions: Uncertainty in the spatial distribution of permeability propagates to final oil recovery in a way that depends on the feedback between gravity and viscous forces driving the system. Uncertainty of final oil recovered (as rendered in terms of variance) is smallest for vertical flows, consistent with the observation that the gravity effect is largest in such scenarios and is dominant in controlling the flow dynamics. Uncertainty of final oil recovered tends to be higher when there is competition between the effects of gravity and viscous forces, the latter being influenced by the strength of the spatial variability of permeability.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"609 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116176996","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

Machine Learning-based Approach for Automated Identification of Produced Water Types from Conventional and Unconventional Reservoirs 基于机器学习的常规和非常规油藏采出水类型自动识别方法

Petroleum Geostatistics 2019 Pub Date : 2019-09-02 DOI: 10.3997/2214-4609.201902193

P. Birkle, M. Zouch, M. Alzaqebah, M. Alwohaibi

引用次数: 6