Exploration of Training Strategies for a Quantile Regression Deep Neural Network for the Prediction of the Rate of Penetration in a Multi-Lateral Well

In recent years, rate of penetration (ROP) prediction using machine learning has attracted considerable interest. However, few studies have addressed ROP prediction uncertainty and its relation to training data and model inputs. This paper presents the application of a quantile regression deep neural network (QRDNN) for ROP prediction on multi-lateral wells drilled in the Alvheim field of the North Sea. The quantile regression framework allows the characterization of the prediction uncertainty, which can inform the end-user on whether the model predictions are reliable. Three different training strategies for the QRDNN model are investigated. The first strategy uses individual hole sections of the multi-lateral well to train the model, which is then tested on sections of similar hole size. In the second strategy, the models are trained for specific formations encountered in the well, assuming the formation tops are known for both the training and test sections. The third strategy uses training data from offset wells from the same field as the multi-lateral well, exploring different offset–well combinations and input features. The resulting QRDNN models are tested on several complete well sections excluded from the training data, each several kilometers long. The second and third strategies give the lowest mean absolute percentage errors of their median predictions of 27.3% and 28.7% respectively—all without recalibration for the unknown test well sections. Furthermore, the third model based on offset training gives a robust prediction of uncertainty with over 99.6% of actual values within the predicted P10 and P90 percentiles.