Severe bit damage is an issue in West Texas land drilling because of abrasive sand formation and interbedded hard stringers. Operational performance and rig costs often are affected by bits damaged beyond repair (DBR), low rates of penetration (ROPs) with worn bits, and inefficient decision-making regarding tripping. A real-time data-analytics application is developed that aims to provide information to operators to expedite decision-making.

Introduction

As bottomhole-assembly (BHA) design and bit selection have become standardized, a historical data set of surface mechanics data and bit records has been accumulated from 40 bit runs. By combining conventional physical modeling of drilling mechanics and supervised machine learning, a hybrid analysis is conducted to separate bit-failure patterns from normal formation transitions and drilling-parameter adjustments. A metric-based algorithm is constructed for real-time monitoring of bit performance and for predicting bit wear.

A lightweight web-based framework is used for deployment in real time. A shadow mode trial on three wells in the same pad was conducted and generated satisfactory results.