Friday, June 28th, 2019
Happy Friday and welcome to Energized, your weekly look into the geopolitics, news, and happenings of energy markets.
This issue continues our coverage of the 2019 Upstream Intelligence Data Driven Drilling and Production Conference. DDDP was so chock-full of rich content that we spread our coverage over three issues of Energized. You can find the prior issue here.
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Part 2/3: New Frontiers
Incremental Steps Towards Achieving Widespread Digitalization
June Spotlight Issue: Data-Driven Drilling and Production Conference (DDDP)
June 11th and 12th, 2019
Welcome to part 2/3 of our DDDP Energized newsletter series.
Part 1 provided an introduction to the conference, what it is, why it’s important, and how the current operating model of most companies is causing resistance to digital change.
In part 2, we discuss the widespread implementation of digital tools across many upstream companies. These tools lay the foundation for digitization, acting as a small step for cost savings and efficiency but a giant step for changing the way traditional upstream operations work.
AI-Based Predictive Reliability
First is AI-based predictive reliability, now used for a variety of upstream applications. It is a miniature “system of systems” that, when properly executed, is equipment agnostic, reduces false positives, engages in continual deep learning, and deals with both unknown and known failures.
A great example of effective implementation of this miniature “system of systems” is an offshore rig where data is viewed holistically with, let’s say, 30 main operations control systems each consisting of 15 sub-systems. The next step would be to integrate the same sophistication across all of a company’s offshore operations. For example, Shell has over 14 production operations in the Gulf of Mexico. That would truly bring in the next wave of advancement but it’s also a point of resistance for most companies.
Many companies have AI-based predicative reliability down pat, and are struggling with the next step. I personally believe this is impressive, considering where the industry was just 5-10 years ago, but there’s still room for improvement.
Digital Twins in a System of Systems
Using data by LNS Research, a presentation on “digital twins” provided a clear example of the value that can be derived by cross-functional solutions. The presenter defined digital twins as the following:
“An executable, virtual representation of an asset, process, value chain or human; based on data, data models and knowledge, ideally incorporating physical properties, chemistry, physics, and thermodynamics where applicable; and which uses advanced analytics to create and safely operate assets, support effective decision-making, and optimize business outcomes.”
Simply put: A marriage of data, physics, and applied mathematics
Digital twins replicate the physical asset with sufficient accuracy and robustness which can lead the twin to identify previously undetected or unexplained patterns, meanings, anomalies, and discrepancies. Other capabilities include:
- Performing what-if scenarios
- Acting as a test-bed for potential physical, process, and operating changes
- Capture the full operations history of the asset
- Be a single point of reference for trusted information
- Be adaptable, continuously updating, and scalable
- Facilitate change management
While these features are all brilliant in their own right, their scope of benefit is limited if the twin representation is isolated to its own silo.
But, if the twin is part of a network of twins that make up the broader value chain to explore, develop, drill and complete, produce, transport, and finally sell, then the data gathered by the twin will be much more meaningful.
Imagine having digital twins at the wellsite, utilities twins, reliability and integrity twins, etc, all working across upstream – even to the handoff at midstream or downstream.
Coordination of a company’s entire value chain is challenging if not impossible under the current operational model. A revamp is needed in the organizational structure.
C-suite-level executives such as the CTO or CIO or even the creation of a Chief Data Officer (CDO) would give projects like an interconnected system of digital twins the muscle needed to actually pull of digital transformation at scale.
There Is No Standard IoT Solution That Can Be Implemented at Scale Better Than SCADA
Supervisory Control And Data Acquisition (SCADA) is one of the most widely used systems in the upstream.
SCADA is essentially several industrial applications used together to support process control across most if not all upstream business segments. SCADA is both the medium for process control and the source of collected data from field operations.
An article by Oil and Gas Engineering that quoted Michael Day, oil and gas market development manager at Siemens Industry Inc…. “While new automation technologies can enable quantum gains in upstream productivity, the industry has been conservative in their deployments, preferring what it considers time-tested production methods and technologies.”
What Michael is saying is that the new technologies are available, but risk aversion leads to continual reliance on SCADA.
In The Top 5 Problems with SCADA Systems, which was released in February, Tony Poole states that “SCADA Systems are visualization and data collection (aggregation) tools. Even though most also have moderate storage systems in terms of compression, what they usually do not do well is support hundreds or thousands of simultaneous clients or 3rd party interface connections. As a SCADA System grows in size sometimes the number of clients/interfaces has a significant impact on the core SCADA system performance. “Dragging” huge amounts of data from SCADA storage systems is simply put, bad practice.”
Basically, data in SCADA is limited to SCADA and SCADA has limits in scale partly due to misalignment and a lack of integration with in house or vendor-made IoT tools. This rigidness of SCADA and its foothold across operations makes it difficult to replace.
This is a huge problem for customized IoT solutions, tailor-made for a specific organization.
The entrenchment of SCADA reaffirms our earlier point that upper management absolutely has to buy into digitalization for there to be any chance of widespread change.
Ground –> Up Feedback Approach
Although upper management buy-in is critical for overarching strategy, tactical decisions start from the ground up.
At the conference, companies agreed that input from the ground-up field operations staff was the best way to address the greatest problems that digital solutions can fix. After all, SCADA is just a tool. Start with the “why” on what needs to be done and then build or buy a tool that fits management objectives and has a practical use for the field personnel.
Asking those employees “what would make your job easier” is a great way to increase the likelihood that new technology is adopted. Then have your data scientists spend some serious time with them riding in a truck through their field operations.
The stickiness of these digital tools is often an overlooked criterion. Will people begrudgingly use a new technology only when absolutely necessary — while secretly reverting to old software? The adoption of new technology really starts when it benefits both management and field employees.
Many presenters stressed the importance of saving time for users of the new tools. The last thing you want to do is take someone who so is so busy they are resistant to doing their job differently or make their job harder.
The difficulty lies in answering the following question: Can the organization prove to its employees and investors that the suggested tool or tools save time, improve the job, and make the company more money?
After all, many engineers are well versed in SCADA and their job security is partially tied to the ongoing use of SCADA. It’s important to not alienate the SCADA-related workforce of an organization and instead, have management ask them “what’s wrong with SCADA, how would you improve it, and how can we (the company) make that happen. It’s impossible to change the mindset of someone without new incentives.
As far as new IoT solutions go if they’re just about saving the employee time but there’s no clear improvement to the bottom line, funding will likely peter out. If they improve the bottom line but workers hate using them, then turnover and HR costs could soar.
Thanks for reading!
The next issue of Energized will feature Part 3: “Cutting Edge, Crowing Achievements in Data-Driven Solutions”
Be sure to check out the latest episodes from our digital oilfield podcast series which are listed in the Energized IoT in oil and gas podcast supplement. Here’s the link again so you don’t have to scroll up.
Have a great weekend!
EKT Interactive Contributing Editor
Head Writer | Eau Claire Writing
Eau Claire Writing is a Houston-based freelance writing company that specializes in gas compression, turbomachinery, onshore and offshore drilling, and well service content for the oil and gas industry.
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