A Data Driven Approach of ROP Prediction and Drilling Performance Estimation

Abstract

Given the expense of drilling in resource development, explore the optimal drilling operations, especially improve the rate of penetration (ROP), has become increasingly important. The prediction of ROP is challenging due to the complex bit and rock interactions. Several experience based models have attempted to predict ROP, but few of them can have excellent estimations. In this paper, a new ROP prediction methodology is developed using machine learning techniques. The booming computation capacity and large volume of data provide a new way to solve the problems. In this paper, wells from southern China have been used to demonstrate our approach. Well logging, mud logging data, geological information, daily well reports, and many other data have been included for the model development. Parameters which have been previously considered irrelative to ROP calculation show their impacts in the model. The machine learning approaches bring new insights into the ROP prediction. Comparing with the conventional approach, our method consider drilling as a continuous procession. ROP at current time relates to the drilling rate in previous time step. In our case, the model improves ROP estimation accuracy. In addition, engineers can use this tool to arrange better drilling plans during operation. Dive deep into the data, the acceleration and deceleration of ROP showed close relationship to WOB and mud properties, improve the mud rheology and operation strategies can keep the ROP in a optimal range which may enhance the drilling efficiency. With the assistance of machine learning techniques, a new method of ROP prediction has been developed. Comparing with the conventional approaches, this method considers more parameters and improves accuracy. Factors that may enhance the drilling bit performances were also explored. This new data driven method can be beneficial for drilling efficiency.