用量子增强网络预测油田渗透率，精度比传统方法提升20%。
Quantum-enhanced long short-term memory with attention for spatial permeability prediction in oilfield reservoirs
- 将量子电路嵌入LSTM注意力模型，利用量子叠加与纠缠特性建模复杂地质数据。
- 8量子比特独立门结构模型使平均绝对误差降低19%，均方根误差降低20%。
- 适合石油工程与地学领域研究者，为量子神经网络落地提供可行框架。
油藏空间参数（尤其是渗透率）的预测对油气勘探开发至关重要。然而，渗透率范围广、变化大，现有方法难以提供可靠预测。本研究首次在地下空间预测中提出量子增强长短期记忆注意力模型（QLSTMA），将变分量子电路（VQCs）融入循环单元。利用量子叠加与纠缠原理，显著提升对复杂地质参数如渗透率的预测能力。设计了两种量化结构：共享门（QLSTMA-SG）与独立门（QLSTMA-IG），以评估量子结构配置及量子比特数量对性能的影响。实验表明，8量子比特的QLSTMA-IG模型显著优于传统LSTMA，平均绝对误差（MAE）降低19%，均方根误差（RMSE）降低20%，尤其在复杂测井数据区域表现突出。结果验证了量子-经典混合神经网络在油藏预测中的潜力，表明增加量子比特数可进一步提升精度，即使依赖经典模拟。本研究建立了未来在真实量子硬件部署的初步框架，并可拓展至石油工程与地球科学更广泛应用。
原文摘要 · Abstract (English)
Spatial prediction of reservoir parameters, especially permeability, is crucial for oil and gas exploration and development. However, the wide range and high variability of permeability prevent existing methods from providing reliable predictions. For the first time in subsurface spatial prediction, this study presents a quantum-enhanced long short-term memory with attention (QLSTMA) model that incorporates variational quantum circuits (VQCs) into the recurrent cell. Using quantum entanglement and superposition principles, the QLSTMA significantly improves the ability to predict complex geological parameters such as permeability. Two quantization structures, QLSTMA with Shared Gates (QLSTMA-SG) and with Independent Gates (QLSTMA-IG), are designed to investigate and evaluate the effects of quantum structure configurations and the number of qubits on model performance. Experimental results demonstrate that the 8-qubit QLSTMA-IG model significantly outperforms the traditional long short-term memory with attention (LSTMA), reducing Mean Absolute Error (MAE) by 19% and Root Mean Squared Error (RMSE) by 20%, with particularly strong performance in regions featuring complex well-logging data. These findings validate the potential of quantum-classical hybrid neural networks for reservoir prediction, indicating that increasing the number of qubits yields further accuracy gains despite the reliance on classical simulations. This study establishes a foundational framework for the eventual deployment of such models on real quantum hardware and their extension to broader applications in petroleum engineering and geoscience.
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