Leveraging Cloud-Based Analytics to Enhance Near-Real Time Stage Management

Abstract

Abstract Normally, the optimization of hydraulic fracturing performance is limited to pre-job modeling and analytics. A design is determined for a particular well or project and applied without significant change during the course of the stimulation. Performance results are collected during the job and then analyzed after the fact, with the primary purpose of designing for the next project. Significant design improvements can be made by evaluating stage performance in real-time as the well is being stimulated. Unfortunately, real-time analytics are difficult because the immense of volume, variety, and velocity of the available data. The typical frac fleet captures metered data from as many as one hundred measurement points simultaneously on a second-by-second basis. This means that for a single stage, the comma-separated values (CSV) files containing the recorded channels often include over one million discrete data points. Utilizing these large files (approximately 5 MB) with typical off-the-shelf software can be time-consuming. The manual process of file acquisition by analytical staff alone can often exceed the time available between stages. While these files are an invaluable resource, they are often left untouched until long after a job is completed, if they are ever used at all. Cloud-based analytics greatly shorten the acquisition and utilization timeline, making near real-time analysis possible. While the challenges involved in utilizing "big data"; for actionable analytics are frequently discussed, the technology and approaches described in this paper are relatively new to the field of real-time stage management. This paper introduces a novel and highly effective approach in the field of hydraulic fracturing optimization. The history of CSV analysis is presented along with examples of specific types of beneficial stage analytics.