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 pro-poses 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.

Rehab Alharbi, Nojood Alageel, Maryam Alsayil, Rahaf Alharbi and A’aeshah Alhakamy, “Prediction of Oil Production through Linear Regression Model and Big Data Tools” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131246

@article{Alharbi2022,
title = {Prediction of Oil Production through Linear Regression Model and Big Data Tools},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131246},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131246},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Rehab Alharbi and Nojood Alageel and Maryam Alsayil and Rahaf Alharbi and A’aeshah Alhakamy}
}