Tristan Arbus graduated from Johns Hopkins University in 2010 with a BS in Physics and a BS in Mechanical Engineering. He has over 8 years of experience in oil and gas as a company man, drilling engineer, and data scientist. He has been with Devon Energy since 2014 and is currently focused on solving complex oil and gas problems through the use of technology and artificial intelligence.
June 25, 2020 05:00 PM PT
Oil and gas companies have thousands of wells in production. These wells frequently require action or maintenance. However, it is impossible for engineers to monitor them all. Thus, a model to determine whether or not a well is online based off real-time data streams would be of significant value in order to keep wells running and producing. Creation and deployment of such a model presents challenges with both people and technology. To be of value, it must be utilized, and ergo must both be accurate enough to provide value and be understandable and trusted by its end users. These necessities require careful balancing of complexity and readability. Additionally, in a world where opening just one additional application, window, or screen can be a roadblock for adoption, integrating with currently used technology is a must. Thus, a method of model creation that gives end users insight and can be deployed directly in existing systems would be ideal.
To accomplish this, Databricks is used to pull well data directly from an OSIsoft PI database. Then, statistical analysis is performed and python models are created utilizing various techniques including outlier removal, SMOTE (oversampling), hyperparameter tuning, and gradient boosting. Then, utilizing a custom python function, created models are combed for features and output as code which can be placed directly in PI as an AF Analysis, thereby allowing these models to live in users' existing systems. This deployment method offers pros and cons but maximizes usability and potential for providing immediate business value. This talk will also include discussion centered around the need to sometimes temper the data science mindset of academic perfection in the pursuit of usability and adoption.