Production Capacity Prediction for Tight Gas Reservoirs Based on ADASVRLGBM

Production Capacity Prediction for Tight Gas Reservoirs Based on ADASVRLGBM

Accurate production capacity prediction plays a crucial role in formulating efficient development plans for tight gas reservoirs. Due to the complexity of tight gas reservoir characteristics and significant reservoir heterogeneity, traditional prediction methods often fail to meet the accuracy and s...

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Bibliographic Details
Main Authors: MENG Sihai, ZHANG Zhansong, GUO Jianhong, HAN Zihao, ZENG Weijie, LYU Hengyang
Format: Article
Language:zho
Published: Editorial Office of Well Logging Technology 2025-04-01
Series:Cejing jishu
Subjects:
tight gas reservoir
production capacity prediction
ensemble algorithm
genetic algorithm
machine learning
Online Access:https://www.cnpcwlt.com/en/#/digest?ArticleID=5727
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Summary:Accurate production capacity prediction plays a crucial role in formulating efficient development plans for tight gas reservoirs. Due to the complexity of tight gas reservoir characteristics and significant reservoir heterogeneity, traditional prediction methods often fail to meet the accuracy and stability requirements in practical applications. This paper proposes an innovative production capacity prediction model, ADASVRLGBM, which integrates AdaBoost (Adaptive Boosting), SVR (Support Vector Regression), and LGBM (Light Gradient Boosting Machine) algorithms. The model utilizes GridSearchCV (Grid Search Cross-Validation) to fine-tune the hyperparameters of each algorithm and applies a genetic algorithm to optimize the weight combinations of the sub-models. The integrated model systematically analyzes the correlation of factors influencing tight gas production, extracts key feature parameters, and builds a predictive model with gas well production capacity as the output label. The study demonstrates that the integrated model significantly outperforms single algorithms in terms of prediction accuracy, achieving an average agreement rate of 93.33% after training. Furthermore, the paper provides an in-depth discussion of the contributions of different sub-models to overall prediction performance and highlights their advantages in handling complex data. The findings offer theoretical and practical support for the efficient development of tight gas reservoirs and valuable insights for extending the application of similar models.
ISSN:1004-1338

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