Well logging curve reconstruction based on kernel ridge regression

Fan, Pengpeng; Deng, Rui; Qiu, Jinquan; Zhao, Zhongliang; Wu, Shengli

doi:10.1007/s12517-021-07792-y

Cited by 46 publications

(12 citation statements)

References 13 publications

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“…The commodity market and financial market are the key channels through which the risks of the crude oil market affect the smooth operation of the economy (Fan et al, 2021;Xiao et al, 2021). Commodity market risks are directly related to macroeconomic operation such as domestic output and price level (Shi and Sun, 2017).…”

Section: Hypothesesmentioning

confidence: 99%

Identifying the Asymmetric Channel of Crude Oil Risk Pass-Through to Macro Economy: Based on Crude Oil Attributes

Jia

Dong²,

Wang

2022

Front. Energy Res.

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The impact channel of crude oil market risk on the macroeconomy is highly related to oil attributes. This paper uses a stepwise test method with dummy variables to identify the channel effect of commodity market risk as well as financial market risk and explore the characteristics of the channel effect in different periods dominated by different oil attributes. Furthermore, this paper investigates the asymmetric characteristics of the channel effect under the condition of crude oil returns heterogeneity. The empirical results show that: First, commodity market risk, as well as financial market risk plays a channel role in the impact of crude oil market risk on the macroeconomic operation. Second, there is a significant difference in the ability of the commodity market and financial market to cope with shocks of crude oil market risk in periods dominated by different attributes. During the period dominated by the commodity attribute of oil, both commodity market and financial market play the role of “risk buffer”; during the period dominated by dual attributes of oil, the commodity market risk plays the role of “risk buffer”, while the financial market risk plays the role of “magnifier” of the crude oil market risk. Third, the channel effect pattern and degree of commodity market risk and financial market risk are significantly asymmetric.

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Section: Hypothesesmentioning

confidence: 99%

Identifying the Asymmetric Channel of Crude Oil Risk Pass-Through to Macro Economy: Based on Crude Oil Attributes

Jia

Dong²,

Wang

2022

Front. Energy Res.

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“…Some new lithology identi cation technologies have emerged. e comprehensive lithology recognition method is based on image and element logging data [4], deep learning rock image for intelligent lithology recognition [5,6], and using WorldView-3's VNIR data to analyze pixel and object-based images to perform lithology identi cation [7]. But for some old wells lacking these new types of data, these methods are also not applicable [8,9].…”

Section: Introductionmentioning

confidence: 99%

Lithology Logging Recognition Technology Based on GWO-SVM Algorithm

Luo

et al. 2022

Mathematical Problems in Engineering

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Accurate identification of lithology is the basis and key process of fine logging interpretation and evaluation. However, reservoirs formed by different sedimentary environments and tectonic movements generally have the characteristics of complex and diverse lithology and strong heterogeneity, which brings great difficulty to the identification of reservoir lithology. This paper proposes an automatic identification technology for lithology logging based on the GWO-SVM algorithm model. The technology is actually applied, and the results are compared with the results of the support vector machine cross-validation optimization model, PNN (probabilistic neural network) model, and ELM (extreme learning machine) model. The results show that the GWO-SVM lithology logging recognition model can efficiently solve the lithology recognition and classification problems in complex reservoir analysis and has strong adaptability and higher recognition accuracy.

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“…Ridge Regression decreases the standard errors when a degree of bias is added to the Regression Estimates. Several authors have addressed Ridge Regression as in [1][2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Ridge Regression Method and Bayesian Estimators under Composite LINEX Loss Function to Estimate the Shape Parameter in Lomax Distribution

Yassen

Al-Duais

Almazah

2022

Computational Intelligence and Neuroscience

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In this paper, the Ridge Regression method is employed to estimate the shape parameter of the Lomax distribution (LD). In addition to that, the approaches of both classical and Bayesian are considered with several loss functions as a squared error (SELF), Linear Exponential (LLF), and Composite Linear Exponential (CLLF). As far as Bayesian estimators are concerned, informative and noninformative priors are used to estimate the shape parameter. To examine the performance of the Ridge Regression method, we compared it with classical estimators which included Maximum Likelihood, Ordinary Least Squares, Uniformly Minimum Variance Unbiased Estimator, and Median Method as well as Bayesian estimators. Monte Carlo simulation compares these estimators with respect to the Mean Square Error criteria (MSE’s). The result of the simulation mentioned that the Ridge Regression method is promising and can be used in a real environment. where it revealed better performance the than Ordinary Least Squares method for estimating shape parameter.

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Well logging curve reconstruction based on kernel ridge regression

Cited by 46 publications

References 13 publications

Identifying the Asymmetric Channel of Crude Oil Risk Pass-Through to Macro Economy: Based on Crude Oil Attributes

Identifying the Asymmetric Channel of Crude Oil Risk Pass-Through to Macro Economy: Based on Crude Oil Attributes

Lithology Logging Recognition Technology Based on GWO-SVM Algorithm

Ridge Regression Method and Bayesian Estimators under Composite LINEX Loss Function to Estimate the Shape Parameter in Lomax Distribution

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