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.

Logistic Regression Analysis of Catheter Fixation Defects and Their Influencing Factors

[Objectives] To analyze the influencing factors of fixed defects in patients with catheter fixation in clinical nursing work, in order to provide the best catheter fixation nursing plan for patients.[Methods] 176 inpatients with indwelling catheter from surgical system of Taihe Hospital in Shiyan City from August 2022 to March 2023 were selected. Using a retrospective analysis method, the influencing factors of catheter fixation defects in the study subjects were divided into two categories based on objective characteristics: type I non modifiable influencing factors and type II modifiable influencing factors. Using the standard for catheter fixation defects, whether the patient had catheter fixation defects was determined. After classified and statistically analyzed item by item, binary Logistic multiple regression analysis was used to identify the influencing factors.[Results] The occurrence of catheter fixation defects in patients with catheter fixation was related to factors such as whether the patient was evaluated before fixation, whether the fixation method was standardized and systematic, whether there was sufficient communication between nurses and patients, and the patient s knowledge of catheter fixation. It was also influenced by factors such as the patient s age, catheterization site, catheterization number, catheterization duration, where there was a consciousness disorder, educational level, and external environmental temperature.[Conclusions] Early attention to the key factors affecting patients with catheter fixation defects can effectively prevent adverse factors and provide patients with the best catheter fixation nursing plan to improve nursing quality.

Enterprise Value Valuation-BYD as an Example Based on SWOT Model and Multiple Regression Model

BYD is one of the largest new energy vehicle companies in China.Analyzing its scenario and the factors that affect its value helps to understand and identify development opportunities and potential problems.On one hand,this paper makes a qualitative analysis of BYD,using SWOT model to study the internal capability and external environment of BYD.On the other hand,the multiple regression model is used for quantitative analysis of BYD’s enterprise value,and the model is established based on three factors:enterprise fundamentals,investor behavior and psychology,and macroeconomic policy uncertainty,and the stepwise regression is carried out.The results show that the increase of institutional investors’shareholding ratio,the increase of investor sentiment index,and the increase of M2 growth rate will increase the overall enterprise value,while the increase of economic policy uncertainty will decrease the enterprise value.

Regression Relationship between WI and FMC at Different Growth Periods of Sawtooth Oaks Leaf

[Objective] The aim was to study the regression relationship between water index （WI） and fuel moisture content （FMC） of different growth periods of sawtooth oaks leaf.[Method] Taking sawtooth oaks in Huaguo Mountain,Lianyungang City as research object,the sensitivity of WI to leaf FMC was studied at leaf level,and statistical characteristics were analyzed.[Result] The WI of sawtooth oaks leaves was sensitive to the changes of FMC,and the line regression level between them was significant.A fitting curve between leaf FMC and WI was obtained.[Conclusion] The research provides reference for acquisition methods of vegetation water remote sensing within the range of study area.

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.

Travel Behaviours of Sharing Bicycles in the Central Urban Area Based on Geographically Weighted Regression: The Case of Guangzhou, China

Mobile information and communication technologies(MICTs) have fully penetrated everyday life in smart societies;this has greatly compressed time, space, and distance, and consequently, reshaped residents’ travel behaviour patterns. As a new mode of shared mobility, the sharing bicycle offers a variety of options for the daily travel of urban residents. Extant studies have mainly examined the travel characteristics and influencing factors of public bicycles with piles, while the travel patterns for sharing bicycles and their driving mechanisms have been largely ignored. Using one week’s travel data for Mobike, this study investigated the spatial and temporal distribution patterns of sharing bicycle travel behaviours in the central urban area of Guangzhou, China;furthermore, it identified the influences of built environment density factors on sharing bicycle travel behaviours based on the geographically weighted regression method. Obvious morning and evening peaks were observed in the sharing bicycle travel patterns for both weekdays and weekends. The old urban area, which had a high degree of mixed function, dense road networks, and cycling-friendly built environments, was the main travel area that attracted sharing bicycles on both weekdays and weekends. Furthermore, factors including the point of interest(POI) for the density of public transport stations, the functional mixing degree, and the density of residential POIs significantly affected residents’ travel behaviours. These findings could enrich discourse regarding shared mobility with a Chinese case characterised by rapidly developing MICTs and also provide references to local authorities for improving slow traffic environments.

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.

Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

Use of Support Vector Regression Based on Mean Impact Value Model to Identify Active Compounds in a Combination of Curcuma longa L.and Glycyrrhiza extracts

A support vector regression based on the mean impact value (MIV) model was constructed to identify the bioactive compounds inhibiting proliferation of HeLa cells in a combination of turmeric (Curcuma longa L.) and liquorice (Glycyrrhiza) extracts. The quantitative chemical fingerprint from 50 batches of turmeric and liquorice extracts was established using high performance liquid chromatography hyphenated to an ultraviolet visible detector. Qualitative results were obtained using ultra performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry. A total of 46 peaks (peaks 1–15 from turmeric and 16–46 from liquorice) were selected as “common peaks” for analysis. The inhibitory effect of the combined extracts on HeLa cells was measured by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. It was found that 15 compounds (peaks: 8, 12, 30, 24, 46, 11, 14, 9, 3, 1, 44, 18, 7, 45 and 43) possessing high absolute MIV exhibited a significant correlation with the cytotoxicity against HeLa cells; most of these have already been confirmed with potential cytotoxicity in previous research. The important potential application of the present model can be extended to help discover active compounds from complex herbal medicine prior to traditional bioassay-guided separation. It is considered that this could be a useful tool for re-developing herbal medicine based on the use of these active compounds. © 2017, Tianjin University and Springer-Verlag Berlin Heidelberg.

Suggestion of advanced regression model on friction angle of fault gouge in South Korea

Although friction characteristics of fault gouge are important to understand reactivation of fault,behavior of earthquake,and mechanism of slope failure,analysis results of fault gouge have low accuracy mostly than those of soils or rocks due to its high heterogeneity and low strength.Therefore,to improve the accuracy of analysis results,we conducted simple regression analysis and structural equation model analysis and selected major influential factors of friction characteristics among many factors,and then we deduced advanced regression model with the highest coefficient of determination(R^(2)) via multiple regression analysis.Whereas most coefficients of determination in simple regression analysis are below0.3-0.4,coefficient of determination in multiple regression analysis is remarkably large as 0.657.

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).

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.

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.

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.

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.

Analyzing Some Elements of Technological Singularity Using Regression Methods

Technological advancement has contributed immensely to human life and society.Technologies like industrial robots,artificial intelligence,and machine learning are advancing at a rapid pace.While the evolution of Artificial Intelligence has contributed significantly to the development of personal assistants,automated drones,smart home devices,etc.,it has also raised questions about the much-anticipated point in the future where machines may develop intelligence that may be equal to or greater than humans,a term that is popularly known as Technological Singularity.Although technological singularity promises great benefits,past research works on Artificial Intelligence(AI)systems going rogue highlight the downside of Technological Singularity and assert that it may lead to catastrophic effects.Thus,there is a need to identify factors that contribute to technological advancement and may ultimately lead to Technological Singularity in the future.In this paper,we identify factors such as Number of scientific publications in Artificial Intelligence,Number of scientific publications in Machine Learning,Dynamic RAM(Random Access Memory)Price,Number of Transistors,and Speed of Computers’Processors,and analyze their effects on Technological Singularity using Regression methods(Multiple Linear Regression and Simple Linear Regression).The predictive ability of the models has been validated using PRESS and k-fold cross-validation.Our study shows that academic advancement in AI and ML and Dynamic RAM prices contribute significantly to Technological Singularity.Investigating the factors would help researchers and industry experts comprehend what leads to Technological Singularity and,if needed,how to prevent undesirable outcomes.

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.