@Article{cmes.2022.020498,
AUTHOR = {Hongfei Ma, Wenqi Zhao, Yurong Zhao, Yu He},
TITLE = {A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {134},
YEAR = {2023},
NUMBER = {3},
PAGES = {1773--1790},
URL = {http://www.techscience.com/CMES/v134n3/49751},
ISSN = {1526-1506},
ABSTRACT = {Accurate prediction of monthly oil and gas production is essential for oil enterprises to make reasonable production
plans, avoid blind investment and realize sustainable development. Traditional oil well production trend prediction
methods are based on years of oil field production experience and expertise, and the application conditions
are very demanding. With the rapid development of artificial intelligence technology, big data analysis methods
are gradually applied in various sub-fields of the oil and gas reservoir development. Based on the data-driven
artificial intelligence algorithm Gradient Boosting Decision Tree (GBDT), this paper predicts the initial single-layer
production by considering geological data, fluid PVT data and well data. The results show that the GBDT algorithm
prediction model has great accuracy, significantly improving efficiency and strong universal applicability. The
GBDT method trained in this paper can predict production, which is helpful for well site optimization, perforation
layer optimization and engineering parameter optimization and has guiding significance for oilfield development.},
DOI = {10.32604/cmes.2022.020498}
}