Prediction of Downhole Pressure while Tripping

Abstract

During drilling operations for oil and gas, swab and surge pressure occur while tripping in and out of a wellbore. High tripping speed can lead to fracturing the well's formation, whereas low tripping speed can increase non-productive time and cost. Hence, there is a need to predict surge/swab pressure accurately. Several analytical and machine learning models have already been developed to predict surge/swab pressure. However, these existing models use numerical calculations to generate the data. This paper explored four supervised machine learning models, i.e., Linear Regression, XGBoost, Feedforward Neural Network (FFNN), and Long-Short-Term Memory (LSTM). In this study, actual field data from the Norwegian Continental Shelf provided by an Exploration & Production company is utilized to develop the four machine learning models. The results indicated that XGBoost was the best-performing model with an R2-score of 0.99073. Therefore, this trained model can be applied during a tripping operation to regulate tripping speed where repetitive surge/swab pressure calculation is needed.