ProjectX: Innovation as a Service in Oil & Gas
In the newest installment of ProjectX we’re bringing you a snapshot of our work with many current customers in oil and gas — the secrets behind using data analytics to solve major problems.
It’s all about getting your data to talk back to you, creating a new layer of insight through behavioral information that allows you to search your data and get answers back right away.
Join GrayMatter’s Senior Digital Utility Consultant Alex Gelsick as he takes you into the story of how he helps solve problems in oil & gas by doubling down on data analytics to drive ROI.
“They need to get things right the first time, every time, to provide the market with the products it needs.”
The oil & gas field is divided into three sectors: upstream, midstream and downstream. Each one has a unique purpose. However, they all have similar goals in mind when it comes to business pains.
In a market that has a large impact on everyday life for customers, whether it’s natural gas to heat a house in the winter or supplying gasoline to cities for the morning commute, errors and accidents are not an option.
Well up-time, high efficiency, preventative maintenance and the need for better drilling economics are critical things needed across the sector, accomplished through data analytics.
“They need to get things right the first time, every time, to provide the market with the products it needs,” said Gelsick.
“It’s like Alexa for operational technology.”
The solution lies in GrayMatter’s Innovation as a Service, where data that companies are already collecting is used to get analytics faster.
The first phase in this is taking data from the SCADA systems, manual logs and equipment telemetry of assets, for various companies we’ve collected data from 25 pumps, 50 wells and 5 drilling rigs. The data is then pumped in, and the creation of digital twins begins, allowing manipulation on the platform without scrubbing beforehand.
This has shown a different “aha” moment for every company. In some cases we’re looking at three months worth of data — we can see 100 occurrences of a part being replaced during that time. Sometimes it’s after 100 hours of operation, sometimes it’s a lot faster — so we’re digging into the inconsistency there. In another case we pinpointed the most expensive maintenance item, which allowed us to already see drivers for why it lasts longer and why it doesn’t.
Innovation as a Service is like Alexa for operational technology, allowing companies to get their data to talk back to them.
It allows us to then dig deeper and determine actions or appropriate pressures that may be helping, or hurting, the part to run at its best.
What Does Phase 1 Look Like?
To get started, a typical proof of concept takes about three months. This process involves first identifying assets to monitor, which could be 25 to 50 wells, five or six compressor stations, five drilling rigs, or two to three furnaces for refining.
Then it’s all about working together to understand what data is integral to operations, allowing us to drive insights. The final step is pumping the data in, allowing us to create those digital twins — or virtual representations — of the assets.
This allows us to pull needles from the data haystack, creating an “aha” moment for everyone and allowing you to start creating new revenue models for your business.