Titlebook: Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization; Robust Optimization Reza Yousefzadeh,Ali

小客車

書目名稱Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization影響因子(影響力)




書目名稱Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization影響因子(影響力)學(xué)科排名




書目名稱Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization網(wǎng)絡(luò)公開度




書目名稱Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization被引頻次




書目名稱Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization被引頻次學(xué)科排名




書目名稱Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization年度引用




書目名稱Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization年度引用學(xué)科排名




書目名稱Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization讀者反饋




書目名稱Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization讀者反饋學(xué)科排名

強(qiáng)制性

后退

使人入神

偏見

Reducing the Geological Uncertainty by History Matching,ave better and more reliable estimations of the true model. This chapter provides the details of history matching, different data types and their scale that are used in history matching, use of seismic, static, and production data in history matching, challenges encountered during history matching,

In-Situ

Dimensionality Reduction Methods Used in History Matching,dual deformation, principal component analysis, and higher-order singular value decomposition, and deep learning methods, including the autoencoders, variational autoencoders, and convolutional variational autoencoders. Also, a brief introduction to machine learning?and deep learning is provided.

Gourmet

Field Development Optimization Under Geological Uncertainty,presentative geological realizations to be?used in robust optimization, decreasing the computational cost of the?optimization under geological uncertainty by constrained optimization, and challenges related to these activities are described in this chapter.

得罪

History Matching and Robust Optimization Using Proxies,history matching and robust field development optimization. Different kinds of proxy models; such as physics-based, non-physics-based, and hybrid proxies; pros and cons of using proxy models; and some example applications of proxy models are introduced.

災(zāi)禍

. Result shows the optimal setting of machining variables in laser microgrooving of K-60 alumina ceramic at pulse frequency of 4.4?kHz, diode current of 17 amp, scan speed of 35?mm/s, number of passes as 12, with estimated groove upper width deviation of 0.02669?μm. The methodology described here is

樹木中