Rate of penetration estimation downhole with machine learning for drilling position control

Abstract

The development of fully autonomous downhole directional drilling systems has been hindered by the lack of rate of penetration (ROP) information downhole. This information is critical for determining bit position and steering dynamics, which currently can only be determined at the rig. Consequently, drilling position controllers must be placed on the surface, which leads to significant performance limitations due to communication delays with the downhole drilling tool. This study investigates a new approach that enables downhole implementation of position control: real-time estimation of ROP downhole. Motivated by results from the field of drilling optimization, estimation is performed using machine learning. In contrast to prior work, we limit model features to only measurements acquired downhole, and we focus on minimizing model size given the constrained computing environment downhole. We use a continuous learning framework with incremental training data availability and derive relationships that reveal how prediction error can impact position measurement and controller dynamics. Additionally, we develop novel performance metrics and introduce a modified form of cross-validation for model evaluation. Using data acquired from three directional wells, experiments are conducted to compare the performance of several machine learning models and to assess the effect of varying retraining frequency and training data quantity. The results indicate that the proposed method achieves comparable accuracy to models implemented on the surface despite being constrained in size and model inputs. Additionally, the results show that increasing the retraining frequency almost always reduces test error, and that the training data can be downsampled substantially without a significant drop in accuracy.