A physics and data dual-driven method for real-time fracturing pressure prediction
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摘要: 井口压力预测存在压力波动剧烈、干扰因素多以及影响机理复杂等问题。现阶段研究中，由于对复杂的地层条件、裂缝特征及流体动力学过程的过度简化，传统物理模型难以捕捉多重非线性变化和突发波动，导致在真实施工环境下的预测精度和实时响应能力受到局限。而人工智能模型尽管具有较强的非线性拟合能力，但往往缺乏对压力波动的物理机理的深入理解，对地层和施工参数的敏感性不足，导致在极端或动态变化的条件下稳定性较差、解释性不足。针对这一难题，提出了一种物理—数据双驱动的压力曲线的预测方法对未来压力趋势进行预测。首先，构建了基于长短期记忆(LSTM)神经网络的智能模型，融合缝内支撑剂床平衡高度计算结果与井场实时泵注数据作为模型输入，预测了未来60 s的压力数据；其次，结合传统井口压力反演方法，使用小波变换分解智能模型与传统模型预测结果，利用LSTM模型整体趋势与压力反演计算方法(IPC)模型中突变点特征，重构了兼顾整体趋势和局部波动的井口压力预测曲线。结果表明，相比LSTM模型，IPC和LSTM的小波融合模型未来60 s井口压力预测的均方根误差(RMSE)和均方绝对误差(MAE)分别下降了37.87%和15.29%，预测结果能够精准捕捉现场施工的压裂压力变化，为现场施工提供更为可靠的指导和决策依据。Abstract: Wellhead pressure prediction is challenging due to problems such as drastic pressure fluctuations, numerous disturbing factors, and complex influencing mechanisms. Current research often adopts traditional physical models which find it difficult to capture multiple nonlinear changes and sudden fluctuations due to the oversimplification of complex formation conditions, fracture characteristics, and fluid dynamics processes, limiting their prediction accuracy and real-time responsiveness in actual operations. Artificial intelligence (AI) models, despite their strong nonlinear fitting capabilities, often lack an in-depth understanding of the physical mechanisms underlying pressure fluctuations and are less sensitive to formation and operational parameters, resulting in poor stability and insufficient interpretability under extreme or dynamically changing conditions. To address these challenges, a physics and data dual-driven prediction method was proposed to predict future pressure trends. An intelligent model based on a long and short-term memory (LSTM) neural network was constructed, integrating the equilibrium height calculations of the proppant bed within the fracture and real-time pumping data at the wellsite as model inputs to predict pressure for the next 60 seconds. Then, combined with traditional wellhead pressure inversion method, wavelet transform was used to decompose predictions from both the intelligent and traditional models. The overall trend of the LSTM model and the characteristics of mutation point in the inverse pressure calculation (IPC) model were utilized to reconstruct the wellhead pressure prediction curves that could balance the overall trend and local fluctuations. Results showed that compared to pure LSTM model, the wavelet fusion model of IPC and LSTM reduced the root mean square error (RMSE) and mean absolute error (MAE) by 37.87% and 15.29%, respectively, in wellhead pressure prediction for the next 60 seconds. The fusion model can accurately capture fracturing pressure changes during field operations, providing more reliable guidance and decision support for field operations.
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Key words:
- fracturing pressure prediction /
- physics and data dual driven /
- LSTM /
- IPC /
- wavelet transform /
- fusion model
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图 1 物理—数据双驱动压力预测模型计算框架
Figure 1. Computational framework for physics and data dual-driven pressure prediction model
图 3 物理—数据双驱动模型预测压力(60 s片段)的5层分解
Figure 3. Five decomposition layers of pressure prediction by physics and data dual-driven model (60 s segments)
图 4 小波重构的有效信息融合效果和“漂移值”问题
Figure 4. Fusion effect of effective information in wavelet reconstruction and "drift value" problem
图 5 LSTM模型压力数据的灰色关联分析
Figure 5. Gray correlation analysis of pressure data in LSTM model
图 7 LSTM模型加入DEH前后压力预测结果
Figure 7. Pressure prediction results before and after adding DEH to LSTM model
图 8 LSTM模型训练过程中的损失变化曲线和学习率变化
Figure 8. Loss curves and learning rate variations during training of LSTM model
图 11 原始压力和IPC模型预测压力的高低频分解
Figure 11. High and low-frequency decomposition of original and IPC model-predicted pressure
图 13 LSTM模型与融合模型局部压力预测结果
Figure 13. Local pressure prediction results of LSTM and fusion models
表 1 LSTM模型加入DEH前后预测评价指标对比
Table 1. Comparison of predictive evaluation indicators before and after adding DEH to LSTM model
Params RMSE MSE MAE MAPE 仅压力排量砂浓度 0.787407 0.589 978 0.838 024 加入DEH 0.577 155 0.333 108 0.384 967 0.543 502 
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表 2 各种压力预测模型预测评价指标对比
Table 2. Comparison of prediction evaluation indicators for various pressure prediction models
模型 RMSE MSE MAE MAPE IPC >10 >10 LSTM 0.583 829 0.340 857 0.412 863 0.557 477 LSTM+IPC+WAVEREC 0.460 177 0.211 763 0.349 780 0.472 104
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