A Comprehensive Analysis on Refined BiLSTM-RNN Models for Accurate Forecasting of Bottom Hole Pressure in Managed Pressure Drilling Operations

Managed Pressure Drilling (MPD) is a crucial technique in the oil and gas industry, ensuring optimal pressure conditions to reduce risks like bore instability and blowouts. Accurate Bottom Hole Pressure (BHP) forecasting is essential for real-time decision-making, but traditional methods often fail to capture the complex dynamics of drilling environments. This paper introduces a Bidirectional Long Short-Term Memory Recurrent Neural Network (BiLSTM-RNN) model for BHP forecasting in MPD. The process includes data exploration, cleaning, pattern identification, feature engineering, and dimensionality reduction to enhance computational efficiency. The BiLSTM-RNN model addresses the vanishing gradient problem and passes in time-series analysis. Simulations in Python achieved reliable error metrics, including MSE of 31942.15, MAE of 136.71, MAPE of 13.02, RMSE of 178.72, and an R2-score of 0.6, demonstrating its effectiveness in improving drilling performance and safety.