Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique
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) ”...
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| Format: | Article |
| Language: | English |
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Sakarya University
2025-06-01
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| Series: | Sakarya University Journal of Computer and Information Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/4340969 |
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| author | Azhar Alyahya Gülüzar Çit |
| author_facet | Azhar Alyahya Gülüzar Çit |
| author_sort | Azhar Alyahya |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-475620fbcd5d4233a36666b64ac43d3d |
| institution | Kabale University |
| issn | 2636-8129 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya University Journal of Computer and Information Sciences |
| spelling | doaj-art-475620fbcd5d4233a36666b64ac43d3d2025-08-20T03:50:06ZengSakarya UniversitySakarya University Journal of Computer and Information Sciences2636-81292025-06-018221222210.35377/saucis...157959928Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning TechniqueAzhar Alyahyahttps://orcid.org/0009-0002-6214-5179Gülüzar Çit0https://orcid.org/0000-0002-1220-0558SAKARYA ÜNİVERSİTESİPlanning 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.https://dergipark.org.tr/en/download/article-file/4340969machine learning modelsrandom forest regressorextremely randomized trees regressork- nearest neighborsgradient boosting regressorstacking model |
| spellingShingle | Azhar Alyahya Gülüzar Çit Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique Sakarya University Journal of Computer and Information Sciences machine learning models random forest regressor extremely randomized trees regressor k- nearest neighbors gradient boosting regressor stacking model |
| title | Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique |
| title_full | Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique |
| title_fullStr | Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique |
| title_full_unstemmed | Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique |
| title_short | Enhanced Oil and Gas Production Forecasting Through Stacked generalization Ensemble Learning Technique |
| title_sort | enhanced oil and gas production forecasting through stacked generalization ensemble learning technique |
| topic | machine learning models random forest regressor extremely randomized trees regressor k- nearest neighbors gradient boosting regressor stacking model |
| url | https://dergipark.org.tr/en/download/article-file/4340969 |
| work_keys_str_mv | AT azharalyahya enhancedoilandgasproductionforecastingthroughstackedgeneralizationensemblelearningtechnique AT guluzarcit enhancedoilandgasproductionforecastingthroughstackedgeneralizationensemblelearningtechnique |