Real-Time Data Analytics Allows for Bit-Wear Monitoring and Prediction
Authors: Wilson Adam 
Published: December 2018

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 189602, “Real-Time Bit-Wear Monitoring and Prediction Using Surface Mechanics Data Analytics: A Step Toward Digitization Through Agile Development,” by Yu Liu, Justin Kibbey, Yanbin Bai, and Xianping Wu, SPE, Shell, prepared for the 2018 IADC/SPE Drilling Technology Conference and Exhibition, Fort Worth, Texas, USA, 6–8 March. The paper has not been peer reviewed.
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.
Agile-Development Framework
The agile-development framework is an integrated platform for fast technology prototyping, which consists of the following components:
The Amazon cloud server is the platform for the application repository, computing-engine execution, and data stream and storage. Most real-time data-analytics applications are delivered as software-as-service, where the algorithm-processed data is stored on the cloud server and pertinent results are delivered to end-users over the web. User interaction with the web application is limited to default viewing options, and other data exchanges such as value inputs are kept to a minimum to reduce complexity.
MATLAB and Python are the engines for algorithm development, prototyping, and early-stage deployment. MATLAB is a powerful engineering computing/coding program with strong capability in data management, visualization, and debugging. It provides various toolboxes for signal processing, machine learning, and statistical analysis. Python is an alternative to MATLAB with similar functionality and the advantage of being open source. Codes of data preprocessing, core algorithms, and data stream input/output
(I/O) are realized in the MATLAB environment running on the Amazon cloud server.
Wellsite Information Transfer Standard Markup Language (WITSML) I/O is the industrial standard for drilling-data stream and management. In this application, surface mechanics drilling data are streamed into MATLAB using a WITSML wrapper.
Plotly is a solution for visualization on the web and user interface. Computation results from MATLAB are delivered to a web page by calling Plotly functions within the MATLAB environment.