scholarly journals Modified Principal Component Analysis for Identifying Key Environmental Indicators and Application to a Large-Scale Tidal Flat Reclamation

Water ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 69 ◽  
Author(s):  
Kejian Chu ◽  
Wenjuan Liu ◽  
Yuntong She ◽  
Zulin Hua ◽  
Min Tan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Tidal Flat ◽  
Large Scale ◽  
Principal Component ◽  
Component Analysis ◽  
Environmental Indicators

scholarly journals Regionalization of Daily Soil Moisture Dynamics Using Wavelet-Based Multiscale Entropy and Principal Component Analysis

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 548 ◽  
Author(s):  
Yuqing Sun ◽  
Jun Niu
Keyword(s):  
Principal Component Analysis ◽  
Soil Moisture ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Simulated Data ◽  
Grid Cell ◽  
Principal Component ◽  
Component Analysis ◽  
Multiscale Entropy ◽  
Homogeneous Groups

Hydrological regionalization is a useful step in hydrological modeling and prediction. The regionalization is not always straightforward, however, due to the lack of long-term hydrological data and the complex multi-scale variability features embedded in the data. This study examines the multiscale soil moisture variability for the simulated data on a grid cell base obtained from a large-scale hydrological model, and clusters the grid-cell based soil moisture data using wavelet-based multiscale entropy and principal component analysis, over the Xijiang River basin in South China, for the period of 2002–2010. The effective regionalization, for 169 grid cells with the special resolution of 0.5° × 0.5°, produced homogeneous groups based on the pattern of wavelet-based entropy information. Four distinct modes explain 80.14% of the total embedded variability of the transformed wavelet power across different timescales. Moreover, the possible implications of the regionalization results for local hydrological applications, such as parameter estimation for an ungagged catchment and designing a uniform prediction strategy for a sub-area in a large-scale basin, are discussed.

Assisted History Matching of Channelized Models by Use of Pluri-Principal-Component Analysis

SPE Journal ◽  
2016 ◽  
Vol 21 (05) ◽  
pp. 1793-1812 ◽  
Author(s):  
C.. Chen ◽  
G.. Gao ◽  
B. A. Ramirez ◽  
J. C. Vink ◽  
A. M. Girardi
Keyword(s):  
Principal Component Analysis ◽  
History Matching ◽  
Large Scale ◽  
Gaussian Model ◽  
Principal Component ◽  
Component Analysis ◽  
Work Flow ◽  
Real Field ◽  
Assisted History Matching ◽  
Facies Types

Summary Assisted history matching (AHM) of a channelized reservoir is still a very-challenging task because it is very difficult to gradually deform the discrete facies in an automated fashion, while preserving geological realism. In this paper, a pluri-principal-component-analysis (PCA) method, which supports PCA with a pluri-Gaussian model, is proposed to reconstruct geological and reservoir models with multiple facies. PCA extracts the major geological features from a large collection of training channelized models and generates gridblock-based properties and real-valued (i.e., noninteger-valued) facies. The real-valued facies are mapped to discrete facies indicators according to rock-type rules (RTRs) that determine the fraction of each facies and neighboring connections between different facies. Pluri-PCA preserves the main (or principal) features of both geological and geostatistical characteristics of the prior models. A new method is also proposed to automatically build the RTRs with an ensemble of training realizations. An AHM work flow is developed by integrating pluri-PCA with a derivative-free optimization algorithm. This work flow is validated on a synthetic model with four facies types and a real-field channelized model with three facies types, and it is applied to update both the facies model and the reservoir model by conditioning to production data and/or hard data. The models generated by pluri-PCA preserve the major geological/geostatistical descriptions of the original training models. This has great potential for practical applications in large-scale history matching and uncertainty quantification.

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