Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique

Gülüzar Çit; Azhar Alyahya

doi:10.35377/saucis...1579599

EN

Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique

Abstract

Planning a strategy throughout the oil and gas sector depends on production forecasting. Precise projections aid in estimating future output rates, streamlining processes, and effectively allocating resources. Techniques like “ Decline Curve Analysis (DCA) and Numerical Reservoir Simulation (NRS) ” have been used in the past, but they have drawbacks such reliance on static models and time consumption. A stacked generalization ensemble learning method for predicting oil and gas production is presented in this work. Using Python and data from wells in the state of “New York State”, the model contains four machine learning techniques: “ Random Forest Regressor (RFR), Extremely Randomized Trees Regressor (ETR), K-Nearest Neighbors (KNN), and Gradient Boosting Regressor (GBR) ”. The stacked model works better than separate models, according to the results of experiments, via R2 scores of 0.9709 per oil and 0.9998 per gas.

Keywords

Supporting Institution

Sakarya University

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Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Gülüzar Çit
0000-0002-1220-0558
Türkiye

Azhar Alyahya ^*
0009-0002-6214-5179
Türkiye

Early Pub Date

June 13, 2025

Publication Date

June 30, 2025

Submission Date

November 5, 2024

Acceptance Date

April 21, 2025

Published in Issue

Year 2025 Volume: 8 Number: 2

DOI

https://doi.org/10.35377/saucis...1579599

IZ

https://izlik.org/JA29UU35WB

Cite

RIS / Bibtex

APA

Çit, G., & Alyahya, A. (2025). Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique. Sakarya University Journal of Computer and Information Sciences, 8(2), 212-222. https://doi.org/10.35377/saucis...1579599

AMA

1.Çit G, Alyahya A. Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique. SAUCIS. 2025;8(2):212-222. doi:10.35377/saucis.1579599

Chicago

Çit, Gülüzar, and Azhar Alyahya. 2025. “Enhanced Oil and Gas Production Forecasting Through Stacked Generalization Ensemble Learning Technique”. Sakarya University Journal of Computer and Information Sciences 8 (2): 212-22. https://doi.org/10.35377/saucis. 1579599.

EndNote

Çit G, Alyahya A (June 1, 2025) Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique. Sakarya University Journal of Computer and Information Sciences 8 2 212–222.

IEEE

[1]G. Çit and A. Alyahya, “Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique”, SAUCIS, vol. 8, no. 2, pp. 212–222, June 2025, doi: 10.35377/saucis...1579599.

ISNAD

Çit, Gülüzar - Alyahya, Azhar. “Enhanced Oil and Gas Production Forecasting Through Stacked Generalization Ensemble Learning Technique”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 1, 2025): 212-222. https://doi.org/10.35377/saucis. 1579599.

JAMA

1.Çit G, Alyahya A. Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique. SAUCIS. 2025;8:212–222.

MLA

Çit, Gülüzar, and Azhar Alyahya. “Enhanced Oil and Gas Production Forecasting Through Stacked Generalization Ensemble Learning Technique”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 2, June 2025, pp. 212-2, doi:10.35377/saucis. 1579599.

Vancouver

1.Gülüzar Çit, Azhar Alyahya. Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique. SAUCIS. 2025 Jun. 1;8(2):212-2. doi:10.35377/saucis. 1579599

Öz

Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique

Abstract

Keywords

Supporting Institution

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite