NTformer: Near Time Transformer for multi-step prediction of wellhead pressure in fracturing operations

Fracturing process technology is a major factor in the effectiveness of fracturing for increasing production. One of the most critical issues during the construction of fracturing is the control of pressure variation, as excessive pressure can lead to serious accidents such as fracturing fluid leakage and well-bore control loss. To prevent accidents and improve operational efficiency, it is necessary to predict the future multi-step fracturing pressure in advance. Since fracturing operations are a process based on continuous changes in time dimensions, and their feature information has a high correlation in time series, this article optimizes a Transformer network with a serialization structure for wellhead pressure prediction, called Near Time Transformer (NTformer), which can effectively forecast pressure values across multiple time steps without relying on intricate feature engineering or domain-specific knowledge. Firstly, data cleaning and temporal encoding are performed on the original data to reduce the complexity of features at different stages and solve the problem of data temporal misalignment. To better learn from features, a feature conversion mechanism is proposed that the data features after embedding are divided into domain feature and wave feature. Further, utilizing the attention concept to spatially correlate feature information, and using the Transformer structure to transform features to achieve forecasting of future multi-step pressure. Finally, the idea of regularization is used to limit the parameters of the model to avoid over-fitting. Experimental results show that the method has a high prediction accuracy and can satisfy the accuracy requirements of the field application, offering a new ancillary method for improving the efficiency of oil and gas well fracturing construction and preventing safety accidents.