Enhanced Oil Production Forecasting in CCUS-EOR Systems via KAN-LSTM Neural Network
The accurate forecasting of crude oil production in CCUS-EOR (carbon capture, utilization, and storage–enhanced oil recovery) operations is essential for the economic evaluation and production optimization of oilfield blocks. Although numerous deep learning models have been widely applied for this p...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/11/2795 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849722554541932544 |
|---|---|
| author | Wei Xia Qiu Li Quan Shi Rui Xu Jiangtao Wu Song Deng |
| author_facet | Wei Xia Qiu Li Quan Shi Rui Xu Jiangtao Wu Song Deng |
| author_sort | Wei Xia |
| collection | DOAJ |
| description | The accurate forecasting of crude oil production in CCUS-EOR (carbon capture, utilization, and storage–enhanced oil recovery) operations is essential for the economic evaluation and production optimization of oilfield blocks. Although numerous deep learning models have been widely applied for this purpose, existing methods still face challenges in extracting complex features from multidimensional time series datasets, limiting the accuracy of oil production forecasts. In this study, we propose a novel KAN-LSTM model that integrates a KAN (knowledge-aware network) layer with a long short-term memory (LSTM) neural network to enhance the accuracy of oil production forecasting in CCUS-EOR applications. The KAN layer effectively extracts relevant features from multivariate data, while the LSTM layer models temporal information based on the extracted features to generate accurate predictions. To evaluate the performance of the proposed model, we conducted two case studies using both mechanistic model data and real project production data. The prediction performance of our method was compared with that of typical deep learning approaches. Experimental results demonstrate that the KAN-LSTM model outperforms other forecasting methods. By providing reliable estimates of future oil production, the KAN-LSTM model enables engineers to make informed decisions in reservoir development planning. |
| format | Article |
| id | doaj-art-c3a09ae83446444a81ec9cfbaa096e46 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-c3a09ae83446444a81ec9cfbaa096e462025-08-20T03:11:18ZengMDPI AGEnergies1996-10732025-05-011811279510.3390/en18112795Enhanced Oil Production Forecasting in CCUS-EOR Systems via KAN-LSTM Neural NetworkWei Xia0Qiu Li1Quan Shi2Rui Xu3Jiangtao Wu4Song Deng5Key Laboratory of Thermo-Fluid Science and Engineering, Ministry of Education, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou 213164, ChinaSchool of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou 213164, ChinaZhejiang Oilfield Company, PetroChina Company Limited, Hangzhou 310000, ChinaKey Laboratory of Thermo-Fluid Science and Engineering, Ministry of Education, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou 213164, ChinaThe accurate forecasting of crude oil production in CCUS-EOR (carbon capture, utilization, and storage–enhanced oil recovery) operations is essential for the economic evaluation and production optimization of oilfield blocks. Although numerous deep learning models have been widely applied for this purpose, existing methods still face challenges in extracting complex features from multidimensional time series datasets, limiting the accuracy of oil production forecasts. In this study, we propose a novel KAN-LSTM model that integrates a KAN (knowledge-aware network) layer with a long short-term memory (LSTM) neural network to enhance the accuracy of oil production forecasting in CCUS-EOR applications. The KAN layer effectively extracts relevant features from multivariate data, while the LSTM layer models temporal information based on the extracted features to generate accurate predictions. To evaluate the performance of the proposed model, we conducted two case studies using both mechanistic model data and real project production data. The prediction performance of our method was compared with that of typical deep learning approaches. Experimental results demonstrate that the KAN-LSTM model outperforms other forecasting methods. By providing reliable estimates of future oil production, the KAN-LSTM model enables engineers to make informed decisions in reservoir development planning.https://www.mdpi.com/1996-1073/18/11/2795CCUS-EORKAN-LSTMdeep learning |
| spellingShingle | Wei Xia Qiu Li Quan Shi Rui Xu Jiangtao Wu Song Deng Enhanced Oil Production Forecasting in CCUS-EOR Systems via KAN-LSTM Neural Network Energies CCUS-EOR KAN-LSTM deep learning |
| title | Enhanced Oil Production Forecasting in CCUS-EOR Systems via KAN-LSTM Neural Network |
| title_full | Enhanced Oil Production Forecasting in CCUS-EOR Systems via KAN-LSTM Neural Network |
| title_fullStr | Enhanced Oil Production Forecasting in CCUS-EOR Systems via KAN-LSTM Neural Network |
| title_full_unstemmed | Enhanced Oil Production Forecasting in CCUS-EOR Systems via KAN-LSTM Neural Network |
| title_short | Enhanced Oil Production Forecasting in CCUS-EOR Systems via KAN-LSTM Neural Network |
| title_sort | enhanced oil production forecasting in ccus eor systems via kan lstm neural network |
| topic | CCUS-EOR KAN-LSTM deep learning |
| url | https://www.mdpi.com/1996-1073/18/11/2795 |
| work_keys_str_mv | AT weixia enhancedoilproductionforecastinginccuseorsystemsviakanlstmneuralnetwork AT qiuli enhancedoilproductionforecastinginccuseorsystemsviakanlstmneuralnetwork AT quanshi enhancedoilproductionforecastinginccuseorsystemsviakanlstmneuralnetwork AT ruixu enhancedoilproductionforecastinginccuseorsystemsviakanlstmneuralnetwork AT jiangtaowu enhancedoilproductionforecastinginccuseorsystemsviakanlstmneuralnetwork AT songdeng enhancedoilproductionforecastinginccuseorsystemsviakanlstmneuralnetwork |