Enhanced Oil Production Forecasting in CCUS-EOR Systems via KAN-LSTM Neural Network

Wei Xia, Qiu Li (), Quan Shi, Rui Xu, Jiangtao Wu and Song Deng
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Wei Xia: Key Laboratory of Thermo-Fluid Science and Engineering, Ministry of Education, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Qiu Li: School of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou 213164, China
Quan Shi: School of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou 213164, China
Rui Xu: Zhejiang Oilfield Company, PetroChina Company Limited, Hangzhou 310000, China
Jiangtao Wu: Key Laboratory of Thermo-Fluid Science and Engineering, Ministry of Education, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Song Deng: School of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou 213164, China

Energies, 2025, vol. 18, issue 11, 1-16

Abstract: 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.

Keywords: CCUS-EOR; KAN-LSTM; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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