Frequency Domain Feature Matching and Adaptive Modeling for Rate of Penetration Prediction Based on Real-Time Data Streams

To address the challenges of non-stationary data streams and working condition drift in Rate of Penetration (ROP) prediction for deep complex formations, a dynamic modeling method based on frequency domain perception and adaptive feedback is proposed. The method utilizes sliding windows and Fast Fourier Transform (FFT) to extract frequency domain features from real-time data streams, quantifying the matching degree of working conditions between the main well and neighboring wells via cosine similarity. An innovative dynamic threshold mechanism based on $ {R}^{2} $ feedback is constructed to adjust discrimination standards in real time: historical models are reused when conditions are similar, while the Random Forest model is retrained using hybrid data (main well accumulation plus neighbor well depth matching) during abrupt changes. Validation with real drilling data from 10 wells demonstrates that the method achieves an average $ {R}^{2} $ of 0.96 across the entire well section, with a Mean Squared Error (MSE) of 0.001 5. Compared to the fixed threshold strategy (with a reuse rate of 0%), the dynamic mechanism significantly increases the model reuse rate to 18%.Furthermore, the generalization capability of the proposed method was validated through blind tests on three previously unseen wells, achieving an average R² of 0.91. This study effectively resolves the contradiction between the difficulty of quantifying time-domain signals and poor model adaptability. It significantly reduces computational overhead while ensuring high accuracy.The proposed method provides a new technical framework for real-time rate of penetration prediction and intelligent drilling decision support under complex non-stationary drilling conditions.