Drilling Optimization Using Artificial Neural Networks and Empirical Models

A key role of drilling optimization is reducing the cost and non-productive time (NPT) for drilling operations. The rate of penetration (ROP) directly affects the overall cost and cost per foot of drilling operations and could lead to significant cost savings or expenses. Traditionally, empirical ROP modeling is used to predict bit response or estimate ROP using nearby offset data. Due to the complexity and nonlinearity of ROP, data-driven modeling, such as machine learning (ML), became more attractive. The objective of this paper is to develop an ROP data-driven artificial neural network (ANN) model using drilling and formation data collected from three nearby wells. Additionally, drilling optimization was conducted and compared with traditional empirical ROP models. The advantages and disadvantages of both methods are discussed, and the direction of future data-driven modeling is highlighted. The data-driven ANN model demonstrated strong performance when compared to the field data. The ANN model showed an RMSE and R2 of 3.89 m/h and 0.93 for the training data and an RMSE and R2 of 4.16 m/h and 0.92 for the testing dataset. The sensitivity analysis showed that the ANN model predicted higher ROP than the empirical models in the selected interval. Due to the limited bit wear data compared to the operational parameters, coupled simultaneous data-driven and empirical modeling is believed to be the future direction for data-driven drilling optimization.