scholarly journals Reconstruction of critical coalbed methane logs with principal component regression model: A case study

2020 ◽  
Vol 38 (4) ◽  
pp. 1178-1193
Author(s):  
Wan Li ◽  
Tongjun Chen ◽  
Xiong Song ◽  
Tianqi Gong ◽  
Mengyue Liu
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Analysis ◽  
Regression Model ◽  
Coalbed Methane ◽  
Multivariate Regression ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Methane Content ◽  
Sonic Logs

Wireline logging plays a critical role in coalbed methane exploration. However, the lack of crucial log data, such as neutron and sonic logs, makes coalbed methane exploration difficult. In this paper, we propose a principal component regression model incorporating a multiscale wavelet analysis, a histogram calibration, a principal component analysis, and a multivariate regression to reconstruct essential neutron and sonic logs from conventional logs (i.e., density, resistivity, gamma ray, spontaneous potential, and caliper logs). Our proposed model does not need core-related correlation, and there is no local optimization. We have applied the model to evaluate coalbed methane content in a real case. Firstly, we use the multiscale wavelet analysis and histogram calibration to improve logs’ reliability and lateral comparability. Then, we apply principal component analysis to transform the well-correlated wireline logs into linearly independent components and regress reconstruction functions for neutron and sonic logs with multivariate regression. The reconstructed logs are like the measured logs in trend, mean, and scale. Finally, we apply the reconstructed neutron logs to predict the coalbed methane-content distribution. The predicted distribution is not only following the regional distribution characteristics of coalbed methane enrichment zones but also validated by the coalbed methane production data. In summary, the successful applications of wireline-log reconstruction and regional coalbed methane-content prediction have demonstrated the reliability of the proposed principal component regression model.

scholarly journals Quantify Coal Macrolithotypes of a Whole Coal Seam: A Method Combing Multiple Geophysical Logging and Principal Component Analysis

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 213
Author(s):  
Chao Cui ◽  
Suoliang Chang ◽  
Yanbin Yao ◽  
Lutong Cao
Keyword(s):  
Principal Component Analysis ◽  
Coal Seam ◽  
Coalbed Methane ◽  
Principal Component ◽  
Component Analysis ◽  
Gas Production ◽  
Index Model ◽  
Geophysical Logging ◽  
The Relationship ◽  
Observation Results

Coal macrolithotypes control the reservoir heterogeneity, which plays a significant role in the exploration and development of coalbed methane. Traditional methods for coal macrolithotype evaluation often rely on core observation, but these techniques are non-economical and insufficient. The geophysical logging data are easily available for coalbed methane exploration; thus, it is necessary to find a relationship between core observation results and wireline logging data, and then to provide a new method to quantify coal macrolithotypes of a whole coal seam. In this study, we propose a L-Index model by combing the multiple geophysical logging data with principal component analysis, and we use the L-Index model to quantitatively evaluate the vertical and regional distributions of the macrolithotypes of No. 3 coal seam in Zhengzhuang field, southern Qinshui basin. Moreover, we also proposed a S-Index model to quantitatively evaluate the general brightness of a whole coal seam: the increase of the S-Index from 1 to 3.7, indicates decreasing brightness, i.e., from bright coal to dull coal. Finally, we discussed the relationship between S-Index and the hydro-fracturing effect. It was found that the coal seam with low S-Index values can easily form long extending fractures during hydraulic fracturing. Therefore, the lower S-Index values indicate much more favorable gas production potential in the Zhengzhuang field. This study provides a new methodology to evaluate coal macrolithotypes by using geophysical logging data.

scholarly journals Principal component analysis optimization of a PM2.5 land use regression model with small monitoring network

2012 ◽  
Vol 425 ◽  
pp. 27-34 ◽  
Author(s):  
Hector A. Olvera ◽  
Mario Garcia ◽  
Wen-Whai Li ◽  
Hongling Yang ◽  
Maria A. Amaya ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Land Use ◽  
Regression Model ◽  
Monitoring Network ◽  
Principal Component ◽  
Component Analysis ◽  
Land Use Regression

Use of Chemometrics: Principal Component Analysis (PCA) and Principal Component Regression (PCR) for the Authentication of Orange Juice

1999 ◽  
Vol 32 (15) ◽  
pp. 3131-3141 ◽  
Author(s):  
Stella Vaira ◽  
Víctor. E. Mantovani ◽  
Juan C. Robles ◽  
Juan C. Sanchis ◽  
Héctor C. Goicoechea
Keyword(s):  
Principal Component Analysis ◽  
Orange Juice ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis

scholarly journals METODE STATISTIK PRINCIPAL COMPONENT REGRESSION DALAM ANALISIS HUBUNGAN KUANTITATIF STRUKTUR DAN AKTIVITAS PENGHAMBAT LIPOKSIGENASE SENYAWA TURUNAN KURKUMIN

2018 ◽  
Vol 1 (1) ◽  
pp. 60
Author(s):  
Didi Nurhadi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis

ABSTRAK Hubungan Kuantitatif Struktur dan Aktivitas (HKSA) pada suatu seri senyawa turunan kurkumin telah dikaji dengan menggunakan data muatan bersih atom hasil perhitungan semi empirik AM1 dengan pendekatan Principal Component Regression PCR. Pengkajian dilakukan terhadap data aktivitas antiinflamasi yang menghambat lipoksigenase (log (1/IC50)) sebagai fungsi linear dan variable laten (Tx) hasil transformasi data muatan bersih atom menggunakan Principal Component Analysis (PCA). Persamaan HKSA ditentukan berdasar kontribusi komponen yang terpilih dan selanjutnya dianalisis dengan pendekatan Model persamaan HKSA yang diperoleh adalah: log (1/IC50) = -0,669-1,816.T1+1,697.T2 –3,643.T3 Persamaan tersebut mempunyai tingkat kepercayaan 95 % dengan parameter statistik n =9,  r2 = 0.700,  SE = 0,355, Fhitung/Ftabel=1,19 dan PRESS = 0,082.  Kata kunci : HKSA, kurkumin, lipoksigenase, PCA, muatan bersih atom

scholarly journals FUZZY PRINCIPAL COMPONENT REGRESSION (FPCR) FOR FUZZY INPUT AND OUTPUT DATA

2006 ◽  
Vol 14 (01) ◽  
pp. 87-100 ◽  
Author(s):  
JIH-JENG HUANG ◽  
GWO-HSHIUNG TZENG ◽  
CHORNG-SHYONG ONG
Keyword(s):  
Principal Component Analysis ◽  
Regression Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Fuzzy Regression ◽  
Output Data ◽  
Input And Output ◽  
Fuzzy Input ◽  
Fuzzy Regression Model ◽  
Component Scores

Although fuzzy regression is widely employed to solve many problems in practice, what seems to be lacking is the problem of multicollinearity. In this paper, the fuzzy centers principal component analysis is proposed to first derive the fuzzy principal component scores. Then the fuzzy principal component regression (FPCR) is formed to overcome the problem of multicollinearity in the fuzzy regression model. In addition, a numerical example is used to demonstrate the proposed method and compare with other methods. On the basis of the results, we can conclude that the proposed method can provide a correct fuzzy regression model and avoid the problem of multicollinearity.

Sign in / Sign up

Export Citation Format

Share Document