An innovative lost circulation forecasting framework utilizing multivariate feature trend analysis

The prompt and precise prediction of lost circulation is essential for safeguarding the security of drilling operations in the field. This study introduces a lost circulation prediction model convolutional neural networks-long short-term memory-feature-time graph attention network-transformer (CL-FTGTR) that combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) data decomposition and trend reconstruction. A notable feature of this model is the utilization of an innovative logging data analysis technique for processing drilling fluid and engineering parameters, and the synthesis of two consecutive encoding modules: Feature-GAN-transformer (FGTR) and time-GAN-transformer (TGTR). Experimental results confirm the following: ① The ICEEMDAN algorithm can effectively filter out noise in logging data and extract trend components, minimizing the impact of noise on prediction outcomes. ② Convolutional neural networks-long short-term memory (CLSTM) position encoding module, substituting traditional sin-cos encoding, significantly improves the model's ability to encapsulate global information within the input data. ③ The FGTR and TGTR modules are capable of efficiently handling feature and time dimension information in logging data, leading to a significant enhancement in the performance of the lost circulation prediction model. The CL-FTGTR model was experimentally tested across four wells in the same block, with the essentiality of its modules confirmed by five metrics. The CL-FTGTR model attained peak precision, recall, F1PA%K, and area under the curve values of 0.908, 0.948, 0.967, and 0.927, respectively. The findings demonstrate that the CL-FTGTR model for predicting lost circulation boasts high precision and dependability.