Data-Enhanced Variable Start-Up Pressure Gradient Modeling for Production Prediction in Unconventional Reservoirs

Qiannan Yu, Chenglong Li (), Xin Luo, Yu Zhang, Yang Yu (), Zonglun Sha and Xianbao Zheng
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Qiannan Yu: College of Energy and Power Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
Chenglong Li: Exploration and Development Research Institute, Daqing Oilfield Co., Ltd., Daqing 163712, China
Xin Luo: Fourth Oil Production Plant 8th Unit, Daqing Oilfield Co., Ltd., Daqing 163453, China
Yu Zhang: School of Computer Science and Information Technology, Daqing Normal University, Daqing 163712, China
Yang Yu: College of Energy and Power Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
Zonglun Sha: Exploration and Development Research Institute, Daqing Oilfield Co., Ltd., Daqing 163712, China
Xianbao Zheng: Exploration and Development Research Institute, Daqing Oilfield Co., Ltd., Daqing 163712, China

Energies, 2025, vol. 18, issue 21, 1-25

Abstract: Unconventional reservoirs are critical for future energy supply, but present major challenges for predictions of production due to their ultra-low permeability, strong pressure sensitivity, and non-Darcy flow. Mechanistically grounded physics-based models depend on uncertain parameters derived from laboratory tests or empirical correlations, limiting their field reliability. A data-enhanced variable start-up pressure gradient framework is developed herein, integrating flow physics with physics-informed neural networks (PINNs), surrogate models, and Bayesian optimization. The framework adaptively refines key parameters to represent spatial and temporal variability in reservoir behavior. Validation with field production data shows significantly improved accuracy and robustness compared to baseline physics-based and purely data-driven approaches. Sensitivity and uncertainty analyses confirm the physical consistency of the corrected parameters and the model’s stable predictive performance under perturbations. Comparative results demonstrate that the data-enhanced model outperforms conventional models in accuracy, generalization, and interpretability. This study provides a unified and scalable approach that bridges physics and data, offering a reliable tool for prediction, real-time adaptation, and decision support in unconventional reservoir development.

Keywords: unconventional reservoirs; variable start-up pressure gradient; pressure-sensitive effect; data-enhanced model; physics-informed neural networks (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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