Ambyint Insights

Webinar Recap: Field Automation for Data Collection and Control in O&G Beyond SCADA

Posted by Ambyint on Jul 31, 2018 5:01:19 PM

Ryan Benoit, Ambyint CTO, has spent over 15 years in the technology industry. The way he sees it, SCADA has reached its natural scalable limit when faced with the unprecedented opportunities that artificial intelligence and machine learning are now presenting to the oil and gas industry.

Currently, there are a three key pain points inhibiting our ability to deliver solutions in our industry:

  1. Modern wells are numerous and more complex than ever; the advent of horizontal wells have rendered legacy downhole calculations less than sufficient for well optimization.
  2. Dynamic flow conditions make wells more difficult to optimize than was previously thought.
  3. The optimization technology stack has remained relatively stagnant and ineffective over the last decade or so.

Due to these pain points, operations staff today are being asked to do more work with equal or less manpower in the field to manage these wells of varying types and conditions. Operators are widely experiencing an increase in failure rates, a reduction in downtime, and an overall increase in Lease Operating Expenses (LOE). Over time, this has a significant impact on the economic life of the hardware, along with the profits a company can expect to achieve in the long term.


Artificial Lift Technology: 1950's - Today

The history of Artificial Lift technology was marked by rapid progress from the 1950's through the early 2000's with the advent of the Variable Frequency Drive (VFD), but has since stagnated somewhat.

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Meanwhile, there have been significant advancements throughout many other industries, particularly in the consumer marketplace. Artificial Intelligence (AI) and Machine Learning (ML) have increased safety and quality while increasing revenue and reducing costs in numerous consumer applications. The Internet of Things (IoT) has also been advancing from simple edge data collection to intelligent automation, with distributed automation across numerous segments. E&Ps could benefit enormously from these technologies, if they should be willing to push the needle forward.

AI companies logos


Where SCADA Falls Short

So then, why has the upstream oil and gas industry been so disinclined to continue to innovate? Arguably, one significant reason is the usage of SCADA. This system's 50+ year-old architecture prevents implementation of modern data science and analytics in numerous ways:

SCADA problems

Cybersecurity in particular is a concern for SCADA-users. PLC's and RTU's, for example, are typically equipped with various unsecured local ports, leaving them susceptible to denial-of-service attacks (DDoS). Furthermore, these systems do not have the capabilities to leverage some of the advanced encryption technologies that have been developed over the last few decades. That means when the system is at rest, its data is readily available, unencrypted, to anyone who can get server access.

Many companies are attempting to remediate the problem with an 'air gap,' an area where the SCADA system can no longer touch any other areas of the organization. However, this fix in turn causes its own set of problems, as the locked off areas are no longer able to take part or see the improvements that are being made in various aspects of the larger organization.

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*HDD = Hard Disk Drives

But now we live in the world of IoT. These devices are custom designed for a specific purpose with security and advanced communications systems already built in. There is no longer a need for local port zoning systems; encryption can be sourced from the latest IP security advancements and the system can effectively become immune to denial-of-service attacks. Moreover, use of the Cloud makes scaling, replication, and encryption much more accessible. These features and more make IoT the clear choice when laying the foundation for AI and ML within any business organization.

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Five Key Criteria for Quality Data Science

  1. High-resolution data
  2. Domain expertise to inform feature engineering
  3. Data lake (no more silos)
  4. Contextual data
  5. Continuous feed of new data to continually validate ML models

Not all data is created equal. Historian Data Architecture is frequently used on SCADA systems, and is particularly efficient at storage. Historians are primarily built to leverage time series information, pulling and storing pre-specified data at fixed intervals without any deeper context or insight into what is happening at the source.

By contrast, Events Data Architecture continuously collects and stores everything it sees in a relatively unstructured way. This underlies the idea that while the data may not seem useful or valuable now, there could come an unforeseen point in time where it can be leveraged. This architecture has opened the door up to Big Data and all of its subsequent applications.

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The problem with SCADA is that the system is often relatively far away from the actual industrial process that it is making decisions about. This makes the regular interval polling technique the most appropriate solution. But, as you can see from the diagrams below, the details can easily get lost in the fold:


By working with the Edge, users can begin to better understand when are the most important times to send data, and additionally utilize compression, pattern recognition, etc., to better leverage their data. The Edge allows for the generation of data closer to the source. Integrated machine learning algorithms then process that data and return clearer, more succinct insights to the user.

That being said, not all edge devices are created equal, either. Few are specifically designed for adaptable, real-time control.

spectrum edge devices


Ambyint Utilizes the Edge to Gain More Impactful Insights

Below, you can see the data visualization from an Ambyint edge device. The device processes information from a torque signal, accurately determining and aligning information across the strokes so that, even without the use of a position sensor, the end user can easily make a clear-cut, 'apples-to-apples' comparison of the data set.

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Using a combination of IoT, Edge, AI and ML, Ambyint has developed a solution that enables a machine to classify the operation state of wells and then augment and affect the set points in the corresponding control system. Over time, this allows the system to perform pattern recognition and use deep learning capabilities to detect when wells are under- or over-pumping and take the appropriate action. This could mean increasing the speed range to allow more fluid production on an over-pumping well, or decreasing the speed range on an under-pumping well, etc.

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SCADA has been instrumental in bringing revolutionary technological capabilities and visibility to the oil patch in decades past, but that does not mean the industry should stop innovating there. IoT, deep data, and the Edge are the future of business enablement. SCADA is simply too complex and immersed in legacy technology to keep up with the current pace of change. For these reasons, producers across the sector need to start considering where they want to place their long term investments.

Topics: Production Operations, SCADA, Data Science, Artificial Intelligence, Machine Learning, Optimization, Horizontal Wells, Artificial Lift, Technology Adoption, Pump by Exception, Deep Learning, Edge Computing