Prediction of Oil Production through Linear Regression Model and Big Data Tools

Abstract

Fossil fuels, including oil, are the most important sources of energy. They are commonly used in various forms of commercial and industrial consumption. Producing oil is a complex task that requires special management and planning. This can result in a serious problem if the oil well is not operated properly. Oil engineers must have the necessary knowledge about the well's status to perform their duties properly. This study proposes a linear regression method to predicate the oil production value. It takes into account various independent variables, such as the pressure, downhole temperature, and pressure tubing. The proposed method can accurately reach a very close prediction of the actual production value by achieving very interesting results at the end of this study.

Comparison and Analysis of Several Quantitative Identification Models of Pesticide Residues Based on Quick Detection Paperboard

“…A linear regression model describes the linear connection between independent and dependent variables. The goal of predicting dependent variables through independent factors is achieved by fitting the data of dependent variables and independent variables with a linear regression equation [34][35][36][37]. For model estimates, the modeling tool utilized SPSS 26.0 statistical analysis software.…”

Application of Big Data in Industry and Commerce Based on Data Mining Algorithm

“…The data analysis is a comprehensive evaluation method of industrial and commercial big data [1], and to choice a comprehensive evaluation of market and production data [2], and unreasonable analysis [3]. At present, industrial and commercial big data has the low accuracy [4], long actual deviation time [5].To apply intelligent algorithms in industrial and commercial big data and identify main factors for better decision-making analysis [6].…”