Enhancing water saturation predictions from conventional well logs in a carbonate gas reservoir with a hybrid CNN-LSTM model
Abstract Predicting water saturation in the South Pars gas field’s carbonate reservoirs has been challenging due to limitations in conventional methods like the Archie equation. This leads to unproduced gas volume and water coning phenomena, leaving a gap for an intelligent model to determine the wa...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-04-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s13202-025-01995-9 |
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| Summary: | Abstract Predicting water saturation in the South Pars gas field’s carbonate reservoirs has been challenging due to limitations in conventional methods like the Archie equation. This leads to unproduced gas volume and water coning phenomena, leaving a gap for an intelligent model to determine the water saturation profile more accurately. Four machine learning algorithms: XGBoost, long short-term memory (LSTM), 1-dimensional convolutional neural network (1D-CNN), and a hybrid CNN-LSTM were developed to predict experimental water saturation values from conventional well logs. A total of 10,674 data points were collected from four wells in the South Pars gas field, where well logging evaluations and core measurements were available. The methodology includes data pre-processing to de-noise and transform data into a proper format, feature engineering followed by feature selection based on domain knowledge, models’ training, testing, and optimizing. XGBoost hyperparameters were tuned using grid search, and each neural network model was optimized using a genetic algorithm. The network architectures were designed with specific layers and activation functions to enhance predictive capabilities. A robust validation process was conducted using a blind well dataset. Archie equation was also applied for better comparative analysis. Results showed that the CNN-LSTM model was the most effective, with an R-squared accuracy score of 0.859, while the Archie equation had the least R-squared accuracy of 0.322 in the validation dataset. Furthermore, utilizing an inclusive dataset, a new CNN-LSTM model was constructed with a 10-fold framework, achieving an average R-squared of 0.968. This model was utilized to develop an application that displays the reservoir’s water saturation profile and other features as a well log report within a dashboard. The findings support two points: first, 1D-CNN and LSTM work better together in a hybrid structure, and second, the Archie equation is ineffective in carbonate reservoirs of the South Pars gas field. The study intends to enrich the literature by employing a hybrid machine learning algorithm compared to earlier studies and surpassing traditional protocols by comparing algorithms in a blind well to offer a reliable validation mechanism. Graphical abstract |
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| ISSN: | 2190-0558 2190-0566 |