[2508.05210] Advanced Hybrid Transformer LSTM Technique with Attention and TS Mixer for Drilling Rate of Penetration Prediction

PDF view of the paper entitled advanced LSTM technology with attention and TS mixer to start penetration, written by Saddam Hussein Khan (artificial intelligence laboratory and 4 other authors

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a summary:The accurate prediction of the hacking rate (ROP) is a pivotal to improve the excavation, however it still represents a continuous challenge due to the non -linear, dynamic and incense nature of drilling data. This study provides a new, hybrid educational structure in which the input data is first processed through a long -term long -term memory network (LSTM) to capture multiple -range time dependencies that are in line with drilling operating courses, and the resulting features are improved after that by improved transplantation with positions with positivist and real time weapons. At the same time, the same inputs are directed to the TS-Mixer Mixer mass that allows the effective modeling of the intersecting features of fixed and factional features such as biology indicators and clay properties. Outputs are sequenced by the improved transformer and TS-Mixer, after which adaptive attention is selectively emphasizing the representatives of the most beneficial features of ROP. The proposed framework merges serial memory, fixed features, global learning of the context, and the weighting of dynamic features, providing a comprehensive solution to the heterogeneous nature that depends on events for drilling dynamics. The evaluation on the real world’s excavation data set shows a pioneering performance, as Rsqaure 0.9988 and MAPE achieved 1.447 %, greatly outperformed and independent cruel series. The interpretation of models is achieved through shapes and lemon, and comparisons between actual and expected curves, as well as examination of prejudice, confirming the accuracy and integrity of the model through different scenarios. This advanced hybrid approach allows reliable real ROP, which supports the development of smart and cost -effective drilling improvement systems with significant operational benefits.