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Oilman Magazine / September-October 2018 / OilmanMagazine.com
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OILMAN COLUMN
and as internal roughness increases over time, the
estimates are further away from reality. Because of
these considerations the pipeline parameters are
best identied by the operational characteristics. By
incorporating AI and machine learning, the advantages
are that you can actually learn from the system and
resolve parameter evaluations internally over time.
Because operating procedures are complex, our
systems are constantly monitoring changes and once
an anomaly is identied, the system automatically
drills down in the anomaly where you can decide if
the anomaly is operational, if there is a leak, or if
anything is abnormal due to sensor detection. If it is
found to be an unusual operational procedure result,
it can be picked up through the system and learned
to be acceptable. Naturally this is useful for future
operations of the pipeline. Of course, with any new
pipeline network detection system, operators still
have to provide information so that any anomaly
occurrence is properly characterized to prevent a false
alarm. You can drill down and read the pipeline better.
TH: With Sensoleak’s move to Texas, how do
you think the move will affect the company and,
furthermore, the oil and gas industry?
RH: The center of the oil and gas industry is Texas
and with our move, there is more access to many
engineers in Houston to help support the implemen-
tations. Most of the international companies are in
Houston. I believe that being so available to the oper-
ating companies as well as the reneries and pipeline
operators, we will be able to catch on very quickly as a
preferred provider. We already have things to do here
and the storm (surge in clientele) is coming. I think it
is an ideal location and we will be very successful here.
We will be able to expand our role in the oil and gas
industry through one successful implementation after
another.
SK: I second that. I used to y so much to the U.S.
and would realize that if you don’t have a local pres-
ence, it is a difcult way to promote the business. I
learned from Americans that unless they are looking in
the “white of your eye,” nothing will be found. I also
learned that if I am present and working in their time
zone, the demand is higher and we can accomplish
way more. Just seeing the team working in one space
makes me happy. When I just started, I had a couple
of developers in Israel, but because there isn’t really an
industry there, I had difculty recruiting local O&G
engineers so I hired remote ones from Canada and
the U.S. Now we can collaborate better and everyone
can brainstorm and write ideas on the board which is
more convenient. Houston is an international hub and
you can feel and see a lot of languages and cultures
which is way more interesting. The timing is right with
our move to Texas, as O&G majors seem to be ready
to adopt new tech. I feel welcomed now in Houston,
but whenever there’s a need to explain my personal
style I say that I’m not impolite and I’m not aggressive,
I’m just Israeli – hands on approach, hard work and
a very fast pace. This is how the culture is there. As
ex-military I am used to trying to take things forward
fast, keep the best interest of my company and team
in mind while presenting the best solutions to existing
problems. As for the machine learning experimenta-
tion Richard mentioned, we want to see this company
become successful and I can see it is beginning to hap-
pen now. We are about to establish our presence in the
industry pretty soon. We are meeting with companies
big and small and discussing how we are able to assist
them in ways most benecial to them. If our move
here helps to improve the industry and has a favorable
impact, then this would be our real success – seeing
this technology beneting the industry.
TH: Can you describe the articial intelligence (AI)
technology you have in mind to replace the current
pipeline infrastructure?
RH: I really like it. The AI, methodology, and tech
applications we’re using are state-of-the-art and very
cutting edge. The analytics and learning methods
are based on engineering logic of the pipeline
network system. The overall AI system is automatic,
can drill down into circumstances, and identify if
a circumstance is an operational event. The system
can learn the data as it comes in and predict analyti-
cal benchmarks and nd anomalies. Anything that
deviates from the norm is called an anomaly. They are
discovered and rationalized to determine whether or
not individual sensors are being affected. The system
is a lot smarter and faster than an individual. It gives
individuals overseeing the pipeline networks the op-
portunity to work smarter and you don’t get the ‘crying
wolf scenario’ of false alarms. The system gives you
honest alerts and includes the engineering knowledge
automatically in the intelligence software. Regarding its
teaching and learning times, the algorithms are being
taught and are developing norms. The algorithms are
automatically updated as norms change. The system
can readily adapt and learn with current happenings
which the pipeline systems today could really use.
Once you have the correct information it is also really
clear which components require maintenance. This is
an added ‘plus’ of the system implementation.
SK: The innovation lies in the correlations and shows
how variables affect each other. The beauty in machine
learning is that it can learn over time and teach itself
and adapt to changes. In terms of developing technol-
ogy, we want to get to the point where machines can
learn specic alerts, be able to prevent failures, and
identify what is happening in the pipeline network and
what should be done. Right now, the algorithm helps
decision makers make decisions regarding mainte-
nance, operations, safety and identies the network
segment which encountered a problem. We plan a
more robust system in the future that can output de-
scriptions of the recommended maintenance schedule
items and procedures in a timely manner for more
efciency.
TH: What effect do you expect the new technology/
AI to have on the pipeline infrastructure and on the oil
industry as a whole?
SK: The aging infrastructure needs attention although
the industry is very diligent. It is important to protect
nature and water bodies that we have and our system
which allows network segmentation, can allow the
algorithm to ‘tighten up’ evaluations in different
segments per client wishes. As an example, segments
of pipelines that go near residential areas, schools,
churches, wetlands, lakes or rivers, can be automati-
cally identied to allow the algorithm response to be
more sensitive for anomaly detection. We optimized
a process of data analysis and we are putting data in
order and making it more accurate than in the past.
Although, our system can be applicable to monitor
anything that can be equipped with sensors, pipeline
networks are our current focus. With our system, we
can help preserve the environment, improve safety,
save litigation, and create a friendlier public environ-
ment for operators. If we take for example one of the
ofine projects we ran with one of the three largest
O&G companies in the U.S., we were provided with
several months’ data (pressure, ow, temperature and
density) and managed to identify all the defects, all the
failures and all the false alarms while our competition
identied all the false alarms as leaks. Major problem
that costs the industry tens if not hundreds of millions
of dollars annually – where the operators respond to
these (false) alarms by shutting down the pipelines, dig
along the line, just to realize there’s no leak. Sensoleak
is trying to minimize false alarm rate to zero and
provide the operators only with real alerts that require
their attention.
TH: Besides the inclusion of articial intelligence and
other innovations, what other changes do you think
need to happen to improve the current state of the oil
and gas industry?
RH: I think the operators of oil and gas are very
conscientious and I believe that they are striving for
the best. It’s difcult to change an old industry and it
has been on a slow though steady improvement for
the last 50 or 60 years. I have to say that the oil and gas
companies are staffed with very responsible people. I
have many colleagues in the industry and they never
take safety for granted and are environmentally-con-
scious. The companies work diligently and have tough
standards for the employees and if employees do not
follow through with policies, they will be terminated.
I love the industry and have been in it for a long time.
I think the industry is also quite responsive to imple-
menting available improvements that improve system
integrity. Company executives that are operating pipe-
line networks have to answer to government agencies,
shareholders and peers. I think they have been very
responsive in moving system integrity improvements
forward. This AI system may be the next selection for
many of them.
TH: What changes or innovations would you like to
see happen in the oil industry or specically in Sen-
soleak going forward?
RH: I’d like to see our system installed in all of the
pipeline control rooms, but if not in the primary
systems, at least in the sub-systems. Everyone has a
system which is an excellent approach. Having a pri-
mary system in place with another unobtrusive parallel
system running essentially off-line is something I think
to be a great benet.
TH: Are there any other types of articial intelligence
and technology you are working on for other issues in
the oil industry?
SK: Applications are innite. When I researched new
industries while working on aircraft, I considered
both the oil and medical industry. Back in the 60s,
our vascular system was compared in similarity to oil
applications. A question that came to my mind was,
“What if we used systems for the oil industry to detect
problems with the heart or other organs in the body
which can save a life?” If I can save one person’s life I
would know that I left behind something meaningful
with my work. Since the medical industry is rather
difcult to penetrate, I felt the oil industry was ready
for us and there were many issues ready to be solved.
In the future, I am hoping to develop applications that
can prevent anything that can affect our well-being.
We can use data to improve current systems whether
it be for oil and gas, wind energy, rotating equipment,
aircrafts, or health.
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