Open Access

ARTICLE

A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression

Hongfei Ma^1,*, Wenqi Zhao², Yurong Zhao¹, Yu He¹

1 School of Energy Resources, China University of Geosciences (Beijing), Beijing, 100083, China
2 Research Institute of Petroleum Exploration and Development, China National Petroleum Corporation, Beijing, 100083, China

* Corresponding Author: Hongfei Ma. Email:

(This article belongs to the Special Issue: Modeling of Fluids Flow in Unconventional Reservoirs)

Computer Modeling in Engineering & Sciences 2023, 134(3), 1773-1790. https://doi.org/10.32604/cmes.2022.020498

Received 27 November 2021; Accepted 19 April 2022; Issue published 20 September 2022

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.

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Keywords

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