Cutting through the hype, how is data analytics delivering value to the business? The SPE-GCS Data Analytics Study Group brings a webinar event where two technical case studies will be presented, highlighting novel and practical applications of data analytics/machine learning to solve relevant business problems and industry challenges. The talks will be followed by a virtual networking opportunity.
Abstract Case Study #1:
Discussion of ‘analytics’ in Oil and Gas is often too obscure and high level leading to a lot of confusion in the market. The confusion is mainly caused by most firms approaching analytics differently and keeping their thought processes proprietary. Lessons learned and best practices are rarely documented or published. Therefore, it is extremely hard to decipher what techniques work and are actually being used. Technical staff looking to get into analytics then have no real understanding of what problems have been solved, what problems still need to be solved, and what skillsets to build.
This presentation seeks to lay out a reservoir engineering perspective on defining baseline ‘analytics’. Methodologies and solutions tilted toward unconventional exploration and development will be discussed. In addition, some tips for climbing the learning curve of analytics and machine learning will be discussed. Finally, the presentation seeks to discuss how analytics can drive business decisions and help improve the industry.
Abstract Case Study #2:
Now more than ever, the industry is utilizing remote operations, with a corresponding increase in automation, in order to perform well construction in a safe and reliable manner. Beyond recent advances in automated trajectory drilling, real-time hydraulics management, and the use of digital twins for complex phenomena, artificial intelligence and machine learning are also applied to identify drilling hazards and predict drilling upset events. To improve the likelihood of success when planning a new well, it is common practice to carry out offset analysis to learn from past mistakes and build on success factors. A set of analytics has been developed to build on these data sets and automate the selection of relevant drilling intervals based on similar formation, equipment, and/or trajectory attributes. A case study will be shown where we utilize the output of these analytics to predict bit wear.
In addition to Well Construction applications, Remote Operations is applied through the life of the well. Analytics engines tied to remote monitoring are integrated with Smart Alarms and pump selection algorithms to optimize the cash utilization and reduce inventory carrying costs and production deferrals. A case study will be shown to utilize analytics to determine events that may impact a pump running life and ultimately predict the remaining life of a pump.