AI solutions for the upstream industry combine the latest AI, 5G, and Edge
technologies for rapid visual data curation, proactive remote monitoring,
continuous tracking, and automated alerting for solutions scaled across E&P
sites and facilities.
ELIZABETH SPEARS, Plainsight
With Large Language Models (LLMs)
and Generative AI flooding news headlines, the massive advancements in AI
have become topics of everyday
conversation. For the upstream sector, the time to implement practical
applications of these exciting technological gains to drive digital
transformation is here and now.
At the forefront of these
transformative technologies, vision AI and 5G-accelerated Edge deployments are
delivering real-time video analytics, enabling powerful solutions that give oil
and gas operators a complete understanding of needs and risks across site and
facility infrastructure, equipment, and workforces.
Positioned at the center of Gartner’s latest Emerging Tech and Trends Radar, Edge computer vision is enabling more proactive maintenance and
monitoring with transformative solutions.
By integrating Plainsight’s
“always-on” computer vision engine into their operations, E&P companies are
equipping organizational decision-makers with a standardized, centralized
source of real-time and predictive insights across the value chain. This
streamlines operational processes and automates analyses for reporting that can
scale across high-value use cases such as:
From Leak Detection and Repair
(LDAR) programs, to proper PPE usage and process compliance, to remote tank
fill-level measurement, and detection of piping corrosion and predictive signs
of equipment failures, technological advancements with cameras, 5G, Edge
computing, and computer vision can reduce downtime and losses while mitigating
environmental and health impacts.
The need for innovation has never been greater, and operators can now be
empowered with solutions that transform their
risk-producing traditional, manual processes and technologies and obtain the
full value of their visual data in plain sight.
Below is a detailed look at computer vision’s capabilities for
holistically transforming legacy processes across the upstream value chain,
with an emphasis on remote monitoring to streamline operational management of
VOC leak detection and tank fill-level monitoring.
INTO THIN AIR: VOC GAS LEAK DETECTION & QUANTIFICATION
The UN’s Global Methane Assessment identifies emission reduction—particularly during extraction,
processing and distribution of fuel—as the
crucial objective. Oil and gas operators are uniquely positioned to deliver
on it and promote sustainability. LDAR is not merely the strategy with the
greatest potential. It’s also the most affordable, with advancements in vision
AI and Edge computing continually making it even more attainable and effective.
Reports on fugitive emissions
identify a number of potential sources, underlining the necessity of a holistic
approach to vision AI-powered monitoring: valves (60%), flanges and pumps
(15%), relief valves (15%), and tanks (10%). Leak location is not the only
variable presenting obstacles to timely remediation and accurate reporting.
Grading the severity of leaks and prompting an appropriate response is another
previously unattainable capability enabled by evolving Edge computer vision
technology and connectivity advancements.
DATA CHALLENGE: SEEING THE INVISIBLE
Though operators are committed to
locating and addressing excess emissions, traditional protocols have obvious
drawbacks. Historically, locating the source of leaks and quantifying their
severity has been time-consuming, manual work with limited scope. In addition
to leaving open the possibility of human errors, traditional methods also
potentially expose employees to unnecessary risks.
Toxic vapor analyzers (TVAs),
colloquially known as sniffers, probe areas for detectable concentrations of
VOCs. While capable of quickly determining leak
concentrations, TVAs demand boots on the ground and are only useful for detecting
gasses directly in their path. The Climate and Clean Air Coalition notes that,
while relatively inexpensive in comparison to competing options, TVA tools need
frequent recalibration. Even regulators may be lagging behind the latest
developments. EPA Method 21, one of the organization’s recommended approaches
to controlling fugitive emissions, suggests the use of handheld TVCs. Optical
Gas Imaging (OGI) technology-equipped cameras can allow for broader monitoring
at a safer distance.
The EPA’s New Source Performance
Standards for Oil and Natural Gas (NSPS) include requirements that operators
make use of the best available control technology (BACT) to track and report on
their emissions. As requirements increase in parallel with sustainability and
ESG goals, LDAR strategies will need accurate, reliable data input for
compliance and reporting.
Even when organizations have the
best technology at their disposal, they often deploy it via testing methods
that still rely on manual, pre-scheduled inspections and reviews. While
advancements may have reduced the total workload, they have not sufficiently
reduced errors or time commitments. When relying on a still image, for example,
imperfections in a photo can easily be misinterpreted as leaks. Legacy technology
is also subject to the elements, such as extreme temperature and weather.
Without vision AI models trained to ignore irrelevant distractions, the
visual data that operators receive may not be useful. For an organization with
a reactive approach to addressing such issues, the cost of false positives can
add up fast in unnecessary truck rolls out to remote sites.
Existing leak detection methods
that rely on human inspection are not just costly, risky, and unavoidably
error-prone, but difficult to scale as well. To make the best-available
technology as effective as possible, organizations need highly scalable
solutions capable of conducting proactive monitoring, detecting and quantifying
leaks, automating alerts, and enabling predictive maintenance across the full
upstream value chain.
VISION AI SOLUTION
Plainsight’s end-to-end platform
centralizes and standardizes visual data management and model-building to make
LDAR processes part of robust downtime- and loss-prevention programs. They are
the key to catalyzing digital transformation across the industry.
VOC gas leak detection & quantification. Plainsight’s
automated VOC leak detection system (Fig. 1) for extraction pad
deployment delivers detection and location of leaks (both on a map and in terms
of camera and pad coordinates), grades leak severity (low, medium, high, or
very high), and automatically sends an email or text alerts to designated
individuals and systems within seconds.
Constructing this solution
involved augmenting the organization’s existing camera infrastructure (REO Link
Optical cameras and FLIR OGI cameras) with high-accuracy computer vision
models. Data was collected at the site in the form of 10-frames-per-second
video from cameras. After a fully managed labeling and training process, the
model produced dynamic polygonal detections of leaks on each relevant frame, as
well as coordinates for locating leaks as outputs.
Arriving at these outputs and
achieving the desired results was a five-part process:
tank fill-level monitoring. Historical fill-level monitoring processes
for gas and water tanks are characterized by many of the same shortcomings as
processes for leak detection. When traditional measurement instruments like
depth sensors and waveguides malfunction or report inaccurate results (not an
infrequent occurrence), workers have no choice but to climb on top of tanks,
open the thief hatches, or even venture inside to rectify issues. These costly,
risky legacy processes are others that computer vision-enabled monitoring can
Deploying a vision AI solution for
non-invasive tank fill-level monitoring (Fig. 2) may not even require an
investment in new hardware. Plainsight has implemented such a solution with
updates to an organization’s existing network of security cameras. Even a
single thermal camera equipped with a vision AI solution can monitor a pad’s
worth of tanks, reducing fugitive emissions without putting employees at risk.
Without the need to open thief hatches, organizations reduce their overall
carbon footprint as well.
Capturing visual data at regular
intervals, the solution then feeds video data to a proprietary regression model
that effectively provides for x-ray vision, reporting on fluid levels, and
delivering reports with location (pad and tank number) and fill-level detail.
Data logging was automated via
integrations with existing SCADA systems. While these specific solutions
streamed and stored data to the cloud, the addition of an on-site server could
make the same technology applicable at the Edge, now possible with 5G to open
the innovation floodgates.
MAKING THE BEST AVAILABLE TECHNOLOGY BETTER: COMPUTER VISION AT THE EDGE
As Gartner’s analysis attests,
computer vision has broad near-term potential for businesses across sectors. So
far, enterprises that have seen the greatest results from deploying computer
vision to the cloud and Edge are characterized by both a reliance on legacy
processes and an appetite for innovation. McKinsey’s report, Curbing methane emissions: How five
industries can counter a major climate threat, suggests that oil & gas
is one such industry, poised to deliver on abatement goals with the help of
emerging solutions. The report reads, “the oil and gas industry is probably
best-positioned to implement abatement measures, reflecting its relatively
consolidated structure and deep resources.”
Historically, roadblocks to
effective, real-time monitoring across extraction and production facilities
have included both the sheer quantity of visual data collected each day
(upstream producers generate an estimated 1.5 TB a day) as well as the latency
and bandwidth concerns inherent to the industry’s remote locations. With the
latest in camera developments, such as high-resolution thermal, infrared, and
EPA OOOOA-certified cameras and thanks to Edge computing, the massive amounts
of data collected at each point in the upstream supply chain can be analyzed in
real time for safer, quicker, and more accurate insight generation to address
and record incidents and other causes of unplanned downtime, for greater
accuracy and easier reporting.
REDUCING DOWNTIME WITH EDGE VISION AI SOLUTIONS
Upstream companies experience 27 days of unplanned downtime annually. Altogether, these stoppages lead to nearly $40 million
in costs. Vision AI solutions can both optimize the use of planned downtime and
minimize equipment failures, injuries, and other causes of unscheduled
stoppages, Fig. 3.
Advancements in computer vision are
enabling full scalability across sites, facilities and organizations to
encompass the full scope of potential solutions, such as:
As addressing climate change grows
increasingly urgent, and organizations devote more resources to refining their
LDAR protocols, computer vision’s potential for supporting transformative-yet-practical
change will grow as well. Combining the best of quantifiable OGI solutions and
expertly honed ML models, solutions for leak detection, tank fill-level
monitoring, and more offer the speed, scalability and central source of
insights necessary to predictively address issues that are not merely industry-critical
but globally relevant.
The following best practices can
help guide enterprises as they explore the nascent computer vision ecosystem:
Designed holistically, deployed
responsibly, and managed effectively, computer vision makes the technologies
that have become just possible an everyday reality, helping enterprises quickly
and sustainably solve new and long-standing challenges. For the industry, the
future is now in plain sight with vision AI solutions for realizing
game-changing efficiencies with digital transformations. WO
ELIZABETH SPEARS is the Co-Founder and Chief
Product Officer for Plainsight, a leading provider of enterprise vision AI
solutions. Ms. Spears has led productization of a series of multi-layer,
compute-intensive software service platforms, usually pioneering the product
management function in her companies. She began her career while still in
college, as an engineer and product leader at Alelo, which developed a social
simulation, rich media learning platform for culture and language training. Her
product helped U.S. troops perform more effectively when deployed to
Afghanistan and Iraq. Ms. Spears similarly built and led the product
function at Bottlenose, where she simultaneously transformed the usability,
scalability and data diversity of the Nerve Center real-time streaming data
ingestion, comprehension and analytics platform for enterprise. As a product
consultant, she led collaborative innovation teams at Google, Adidas and
others, and later joined Distillery Tech, a design and development agency, to
launch their start-up & enterprise products development division, leading
the product, UI and UX teams. At Plainsight, Ms. Spears has productized nascent
technologies to a very high functional and usability standard, creating the
products that put her companies into revenue and sustainable funding.