Abstract: In the process of drilling, owing to problems such as data distortion during the measurement of bottom-hole pressure, absence of return data, and incapacity of bottom-hole pressure calculation models to accurately reflect the measurements, the bottom-hole pressure cannot be accurately monitored, and this results in a substantial safety risk for drilling operations. To provide an effective overall monitoring of the bottom-hole pressure, a K-means clustering optimization method was established to improve the Naive Bayesian model. Combined with the principle of bottom-hole pressure monitoring, a Naive Bayesian model optimized by K-means clustering was designed, which could perform intelligent dynamic analysis of the bottom-hole pressure. This model was adopted to correct the bottom-hole pressure calculated by the traditional hydraulic model, and the results of both these models were compared to minimize the calculation error. Analysis of field data suggested that the calculated bottom-hole pressure corrected using the optimized model demonstrated a smaller error. This error was within the safe range of the pressure monitoring of drilling operation, indicating that the model was able to satisfy the requirements of regular drilling practices.

Key words: bottom hole pressure, K-means clustering, Naive Bayesian, hydraulic model, pressure correction