Regression analysis and its application to oil and gas exploration:A case study of hydrocarbon loss recovery and porosity prediction,China

In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery.

Multiple regression analysis of risk factors related to radiation pneumonitis

BACKGROUND Radiation pneumonitis(RP)is a severe complication of thoracic radiotherapy that may lead to dyspnea and lung fibrosis,and negatively affects patients’quality of life.AIM To carry out multiple regression analysis on the influencing factors of radiation pneumonitis.METHODS Records of 234 patients receiving chest radiotherapy in Huzhou Central Hospital(Huzhou,Zhejiang Province,China)from January 2018 to February 2021,and the patients were divided into either a study group or a control group based on the presence of radiation pneumonitis or not.Among them,93 patients with radiation pneumonitis were included in the study group and 141 without radiation pneumonitis were included in the control group.General characteristics,and radiation and imaging examination data of the two groups were collected and compared.Due to the statistical significance observed,multiple regression analysis was performed on age,tumor type,chemotherapy history,forced vital capacity(FVC),forced expiratory volume in the first second(FEV1),carbon monoxide diffusion volume(DLCO),FEV1/FVC ratio,planned target area(PTV),mean lung dose(MLD),total number of radiation fields,percentage of lung tissue in total lung volume(vdose),probability of normal tissue complications(NTCP),and other factors.RESULTS The proportions of patients aged≥60 years and those with the diagnosis of lung cancer and a history of chemotherapy in the study group were higher than those in the control group(P<0.05);FEV1,DLCO,and FEV1/FVC ratio in the study group were lower than those in the control group(P<0.05),while PTV,MLD,total field number,vdose,and NTCP were higher than in the control group(P<0.05).Logistic regression analysis showed that age,lung cancer diagnosis,chemotherapy history,FEV1,FEV1/FVC ratio,PTV,MLD,total number of radiation fields,vdose,and NTCP were risk factors for radiation pneumonitis.CONCLUSION We have identified patient age,type of lung cancer,history of chemotherapy,lung function,and radiotherapy parameters as risk factors for radiation pneumonitis.Comprehensive evaluation and examination should be carried out before radiotherapy to effectively prevent radiation pneumonitis.

Population Quantity Variations of Oriental Fruit Fly (Bactrocera dorsalis Hendel) on the Basis of Stepwise Regression Analysis

[Objective] The research aimed to study the significant influence factors of the population variations of oriental fruit fly. [Method] Using stepwise regression analysis, the population variations law of oriental fruit fly in Jianshui County of Yunnan province and the meteorological factors that caused its occurrence were analyzed. And the regression model was built. Finally, the regression model was tested on the basis of the data in Jianshui County of Yunnan Province during 2004-2006.[Result] The main meteorological factors that influenced the occurrence of oriental fruit fly were relative humidity, the lowest monthly temperature and rainfall. [Conclusion] This study will provide certain reference for the prediction researches on the time, quantity and occurrence peak of oriental fruit fly.

DDM regression analysis of the in-situ stress field in a non-linear fault zone

A multivariable regression analysis of the in-situ stress field, which considers the non-linear deformation behavior of faults in practical projects, is presented based on a newly developed three-dimensional displacement discontinuity method （DDM） program. The Bar- ton-Bandis model and the Kulhaway model are adopted as the normal and the tangential deformation model of faults, respectively, where the Mohr-Coulomb failure criterion is satisfied. In practical projects, the values of the mechanical parameters of rock and faults are restricted in a bounded range for in-situ test, and the optimal mechanical parameters are obtained from this range by a loop. Comparing with the traditional finite element method （FEM）, the DDM regression results are more accurate.

Stability of mine ventilation system based on multiple regression analysis

In order to overcome the disadvantages of diagonal connection structures that are complex and for which it is difficult to derive the discriminant of the airflow directions of airways, we have applied a multiple regression method to analyze the effect, of changing the rules of mine airflows, on the stability of a mine ventilation system. The amount of air （ Qj ） is determined for the major airway and an optimum regression equation was derived for Qi as a function of the independent variable （ Ri ）, i.e., the venti- lation resistance between different airways. Therefore, corresponding countermeasures are proposed according to the changes in airflows. The calculated results agree very well with our practical situation, indicating that multiple regression analysis is simple, quick and practical and is therefore an effective method to analyze the stability of mine ventilation systems.

Regression analysis of major parameters affecting the intensity of coal and gas outbursts in laboratory

Estimating the intensity of outbursts of coal and gas is important as the intensity and frequency of outbursts of coal and gas tend to increase in deep mining. Fully understanding the major factors contributing to coal and gas outbursts is significant in the evaluation of the intensity of the outburst. In this paper, we discuss the correlation between these major factors and the intensity of the outburst using Analysis of Variance(ANOVA) and Contingency Table Analysis(CTA). Regression analysis is used to evaluate the impact of these major factors on the intensity of outbursts based on physical experiments. Based on the evaluation, two simple models in terms of multiple linear and nonlinear regression were constructed for the prediction of the intensity of the outburst. The results show that the gas pressure and initial moisture in the coal mass could be the most significant factors compared to the weakest factor-porosity. The P values from Fisher's exact test in CTA are: moisture(0.019), geostress(0.290), porosity(0.650), and gas pressure(0.031). P values from ANOVA are moisture(0.094), geostress(0.077), porosity(0.420), and gas pressure(0.051). Furthermore, the multiple nonlinear regression model(RMSE: 3.870) is more accurate than the linear regression model(RMSE: 4.091).

Prediction of cavity growth rate during underground coal gasification using multiple regression analysis

During underground coal gasification （UCG）, whereby coal is converted to syngas in situ, a cavity is formed in the coal seam. The cavity growth rate （CGR） or the moving rate of the gasification face is affected by controllable （operation pressure, gasification time, geometry of UCG panel） and uncontrollable （coal seam properties） factors. The CGR is usually predicted by mathematical models and laboratory experiments, which are time consuming, cumbersome and expensive. In this paper, a new simple model for CGR is developed using non-linear regression analysis, based on data from 1 l UCG field trials. The empirical model compares satisfactorily with Perkins model and can reliably predict CGR.

Biological stability in drinking water: a regression analysis of influencing factors

Some parameters, such as assimilable organic carbon(AOC), chloramine residual, water temperature, and water residence time, were measured in drinking water from distribution systems in a northern city of China. The measurement results illustrate that when chloramine residual is more than 0.3 mg/L or AOC content is below 50 μg/L, the biological stability of drinking water can be controlled. Both chloramine residual and AOC have a good relationship with Heterotrophic Plate Counts(HPC)(log value), the correlation coefficient was -0.64 and 0.33, respectively. By regression analysis of the survey data, a statistical equation is presented and it is concluded that disinfectant residual exerts the strongest influence on bacterial growth and AOC is a suitable index to assess the biological stability in the drinking water.

New empirical model to evaluate groundwater flow into circular tunnel using multiple regression analysis

There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow to tunnels due to their simplicity and practical base theory. Investigations show that the real amount of water infiltrating into jointed tunnels is much less than calculated amount using analytical methods and obtained results are very dependent on tunnel＇s geometry and environmental situations. In this study, using multiple regression analysis, a new empirical model for estimation of groundwater seepage into circular tunnels was introduced. Our data was acquired from field surveys and laboratory analysis of core samples. New regression variables were defined after perusing single and two variables relationship between groundwater seepage and other variables. Finally, an appropriate model for estima- tion of leakage was obtained using the stepwise algorithm. Statistics like R, R2, R2e and the histogram of residual values in the model represent a good reputation and fitness for this model to estimate the groundwater seepage into tunnels. The new experimental model was used for the test data and results were satisfactory. Therefore, multiple regression analysis is an effective and efficient way to estimate the groundwater seeoage into tunnels.

Multiple Nonlinear Regression Analysis for the Stability of Non-overtopping Perforated Quarter Circle Breakwater

Breakwaters have been built throughout the centuries for the coastal protection and the port development,but changes occurred in their layout and criteria used for the design.Quarter circle breakwater(QBW)is a new type evolved having advantages of both caisson type and perforated type breakwaters.The present study extracts the effect of change in the percentage of perforations on the stable conditions of seaside perforated QBW by using various physical models.The results were graphically analyzed using dimensionless parameters and it was concluded that there is a reduction in dimensionless stability parameter with an increase in steepness of the wave and change in water depth to the height of breakwater structure.Multiple non–linear regression analysis was done and the equation for the best fit curve with a higher regression coefficient was obtained by using Excel statistical software—XLSTAT.

Regression Analysis of the Number of Association Rules

The typical model, which involves the measures： support, confidence, and interest, is often adapted to mining association rules. In the model, the related parameters are usually chosen by experience; consequently, the number of useful rules is hard to estimate. If the number is too large, we cannot effectively extract the meaningful rules. This paper analyzes the meanings of the parameters and designs a variety of equations between the number of rules and the parameters by using regression method. Finally, we experimentally obtain a preferable regression equation. This paper uses multiple correlation coeficients to test the fitting efiects of the equations and uses significance test to verify whether the coeficients of parameters are significantly zero or not. The regression equation that has a larger multiple correlation coeficient will be chosen as the optimally fitted equation. With the selected optimal equation, we can predict the number of rules under the given parameters and further optimize the choice of the three parameters and determine their ranges of values.

Logistics Regression Analysis between TCM Constitution Types of Patients with Hypertension,Insomnia and ABPM,PSQI

[Objectives]The research aimed to explore the distribution characteristics of TCM constitution types of patients with hypertension and insomnia,and study the clinical characteristics of patients with different constitutions,in order to provide new ideas for the treatment of patients with hypertension and insomnia.[Methods]Cross sectional observation method was used,and 420 patients with hypertension and insomnia were selected.Required information was collected,and the constitution type of traditional Chinese medicine was determined,and relevant data were recorded.SPSS and Logistic regression analysis method were used to explore the correlation between the distribution of TCM constitution types and gender,age,24 h-SBP,24 h-DBP,24 h-BPV,PSQI score,etc.[Results]Among 420 patients,the proportion of gentleness constitution was the most,and others in turn were Qi deficiency constitution>Yang deficiency constitution>phlegm dampness constitution>Qi stagnation constitution>Yin deficiency constitution>blood stasis constitution>damp heat constitution>special constitution.Among male patients,the proportion of gentleness constitution was the most.Among female patients,the proportion of Qi deficiency constitution was the most.In each constitution,the proportion of men and women was different,and the difference in gentleness constitution,Qi deficiency constitution and Yin deficiency constitution had statistical significance(P<0.05).The proportion of gentleness constitution for young and middle-aged patients was the most,while elderly patients with Qi deficiency constitution was the most.There was difference in the distribution of TCM constitution in different age groups,and the difference had statistical significance(P<0.05).Compared with the patients with gentleness constitution,the patients with Qi deficiency constitution,Yang deficiency constitution,Yin deficiency constitution,damp heat constitution,blood stasis constitution and Qi stagnation constitution had different differences in terms of age,24 h-SBP,24 h-DBP,24 h-BPV and PSQI score,and there was statistical significance(P<0.05).Except damp heat constitution,blood stasis constitution and special constitution,other constitutions had certain correlation with age,24 h-SBP,24 h-DBP,24 h-BPV and PSQI score.[Conclusions]TCM constitution types of patients with hypertension and insomnia were dominant by gentleness constitution,Qi deficiency constitution and Yang deficiency constitution.The distribution of TCM constitution in different gender and age groups was different,and different TCM constitution was related to ABPM and PSQI.

Statistical Evaluation and Regression Analysis of Vehicle Sound Quality

A statistical approach to evaluate the subjective perception of the annoyance caused by the vehicle noise was presented in this paper. After recording the noises of Sanfeng, Huali and Xiali at speeds of 30, 40, 50, 60, 70 and 80 km/h respectively, the annoyance of the vehicle noises was evaluated in the testing room using paired comparison method, and the sound quality metrics and subjective annoyance were then distilled. Loudness, sharpness, roughness, periodicity and impulsiveness were selected for each of the vehicle noises. By correlation analysis method, it can be found that loudness has a higher correlation （0.91） with annoyance than other parameters. Meanwhile, sharpness, periodicity, roughness and impulsiveness have correlation with subjective perception with correlation coefficients being 0.84, -0.82, 0.62 and 0.87, respectively. The result of multiple regression analysis shows that calculated annoyance obtained by the regression equation can explain the perceptual annoyance and the regressed evaluation model is feasible to evaluate the sound quality of vehicle.

Regression Analysis of Nutrient Elements between Soil and Tobacco Leaves and Their Influences on Aroma Quality of Flue-cured Tobacco

This study aimed to analyze the relationship between nutrient elements K, Ca, Mg, Cu, Mn, Zn and Fe in tobacco-planting soils and tobacco leaves from six main tobacco-producing areas, and to investigate the influences of these elements on chemical composition and aroma components in tobacco leaves. Results showed that there were certain relationship between contents of nutrient elements in tobacco-planting soils and contents of corresponding elements in tobacco leaves; various elements exhibited different influences on the aroma quality of flue-cured tobacco. Based on the actual situation of nutrient contents in soils from different tobaccoproducing areas, contents of various elements in tobacco leaves should be regulated by soil fertilization and foliar spraying, thereby improving the aroma quality of flue-cured tobacco.

Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana

To transition from conventional to intelligent real estate, the real estate industry must enhance its embrace of disruptive technology. Even though the real estate auction market has grown in importance in the financial, economic, and investment sectors, few artificial intelligence-based research has tried to predict the auction values of real estate in the past. According to the objectives of this research, artificial intelligence and statistical methods will be used to create forecasting models for real estate auction prices. A multiple regression model and an artificial neural network are used in conjunction with one another to build the forecasting models. For the empirical study, the study utilizes data from Ghana apartment auctions from 2016 to 2020 to anticipate auction prices and evaluate the forecasting accuracy of the various models available at the time. Compared to the conventional Multiple Regression Analysis, using artificial intelligence systems for real estate appraisal is becoming a more viable option (MRA). The Artificial Neural network model exhibits the most outstanding performance, and efficient zonal segmentation based on the auction evaluation price enhances the model’s prediction accuracy even more. There is a statistically significant difference between the two models when it comes to forecasting the values of real estate auctions.

Using Linear Regression Analysis and Defense in Depth to Protect Networks during the Global Corona Pandemic

The purpose of this research was to determine whether the Linear Regression Analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during the Global Corona Virus Pandemic. The way this was determined or methods used in this study consisted of scanning 20 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals for a list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The methods further involved using the Likert Scale Model to create an ordinal ranking of the measures and threats. The defense in depth tools and procedures were then compared to see whether the Likert scale and Linear Regression Analysis could be effectively applied to prioritize and combine the measures to reduce pandemic related cyber threats. The results of this research reject the H0 null hypothesis that Linear Regression Analysis does not affect the relationship between the prioritization and combining of defense in depth tools and procedures (independent variables) and pandemic related cyber threats (dependent variables).

COX MULTIVARIATE REGRESSION ANALYSIS OF RECURRENCE FACTORS FOR COLONIC CARCINOMA

Objective: To determine the independent prognostic factors in the recurrence of colonic carcinoma after curative resection. Methods: Two hundred and one patients undergoing curative resections for colonic carcinoma were investigated by univariate and Cox multivariate regression analyses. Ten factors contributed to the rate were analyzed. Results: Dukes stages, obstruction, postoperative chemotherapy as well as the growth manner of the tumor were significantly associated with the recurrence rate of colonic carcinoma (P<0.05) by univariate analysis, while Dukes stages, obstruction, and postoperative chemotherapy were significant factors by the multivariate analysis. Conclusion: Dukes stages, obstruction, and postoperative chemotherapy are independent prognostic factors in the recurrence of colonic carcinoma.

Statistical Regression Analysis of Response of Northern Mid and Upper Tropospheric Circulation to Winter Eurasian Snow Cover Effects

Response for anomalous circulation in relation to snow coverage is derived by use of regression coefficients in dealing with the Eurasian snow cover time series and northern mid and upper tropospheric height data. Results show that not only does the regression response pattern represent the correlation between snow coverage and circulation change but reflects the amplitude strength in correlation cores as well, with a greater amplitude of the circulation response in the mid troposphere and remarkable equivalent barotropy in the mid to upper levels, and that the response of winter-summer circulations to winter snow cover displays noticeable stationary planetary-scale wavetrain, leading to NEUP and NPNA patterns in winter, slightly changed forms in spring months and LEU and EANA in summer time. Also, the study demonstrates that the rasponse-produced wavetrain is marked by branch and propagates energy in a wave-front manner with the energy trapped at subtropical latitudes.

Bootstrapping estimates of regression analysis for a non-random sample and its application in the research on anti-proliferation effects of triptolide

Objective: To solve the problem of parameter estimate in the regression analysis of non-random sample. Methods: Calculating residuals according to the regression function based on original data. Modifying residuals and correcting them with mean. Adding mean-corrected residuals on original response and bootstrapping them to get 1000 samples. Fitting regression functions of 1000 resampling samples and calculating the 2.5th percentile and 97.5th percentile of corresponding coefficient. Results: The interval estimates deriving from bootstrap method had more statistical significance than that from usual method. Conclusion: Bootstrapping a regression with residuals is a valid method for estimating parameter in regression analysis.

Regression Analysis for Segment Size and Mean Size of Cocoon Filament

In order to extract deterministic component from trend nonstationary time series, regression analysis by Akaike Information Criterion (AIC) for segment size and mean size of cocoon filament is introduced, and deterministic component is extracted from size series of cocoon filament by analysis result. Experiments of simulating deterministic components on 9 cocoon categories are carried out, and experimental result is analyzed. Through analysis and experiment, it is known that selecting the order and coefficients of regression equation by AIC is beneficial to accurately describe the relation between segment value and mean value. This study is also useful for pretreatment of nonstationary time series.