Each blow costs the Oil and Gas industry a Billion dollars.
Can we avoid it? Can we see it coming and take action?
We also know that each operating rig consists of thousands of sensors. This sensor data is used to analyse and reduce
HSE (Health, Safety and Environment) risk
considerably and dollars.
To show in detail how this is done, Flutura will explore
breakthroughs achieved with upstream oil and gas data pools and Big Data
analytics. This would transform the way HSE (Health, Safety and Environment) risk
is detected by unlocking patterns previously not seen. Let’s begin, by asking
ourselves some key leading questions that may help us diagnose the problem
better and help transform the way we have been dealing with this problem so
far:
1.
How can disruptive innovations in big data
processing help mitigate blow out risks in the Oil and Gas
industry to save lives and secure our environment?
2.
What are the key data blind spots in
data upstream in the rig?
3.
What are some of the powerful unanswered questions
from MWD (Measurement while drilling) / LWD (Logging while drilling) data?
4.
How can we triangulate across data streams to
see key early warning signs upfront?
5.
What if we can wear new
lenses to see risk patterns previously undetected?
The Oil n Gas industry is ripe for innovative solutions as two
factors are converging to create the perfect storm.
·
Disruption enabler-1 : An audacious problem related to Health, Safety and Environment ( HSE)
issues
·
Disruption enabler-2 : Large headroom to juice untapped data assets in upstream part of
Oil n Gas value chain
Flutura calls this next generation approach - Oil
n Gas 'Last Mile' Safety Risk Analytics 2.0. This approach hits at the
intersection of Sensor data + Big Data Platform Engineering + Deep Machine
Learning. This is a definite turning point from the traditional ways of looking
at data using Historian and/or traditional database systems for assessing risk.
Most risks which happen in the Oil n Gas industry are
emanating from events which happen in the last mile. If one is able to
triangulate on sensor and human generated event streams to detect early warning
signs which have not been seen, then one can detect signals early on. Flutura
believes that the best way to mitigate risk is by reacting strongly to weak signals in
the last mile and this involves continuous triangulation of
streaming MWD/LWD and SCADA data streams over a long period of time
using a combination of platforms + people
So what does the last mile in the Oil industry look like?
The last mile in the oil industry is a sensor jungle which consists of a
variety of MWD (Measurement while drilling) and LWD (Logging while drilling)
sensors and SCADA devices. Thousands of these sensors are measuring
multiple things from drill rpm, mud flow rates, CO2 gas emissions, valve
positions, pump states and in the process emit billions of events. By using
innovations in Big data like Map reduce and horizontal scaling,
the Oil and Gas industry has enormous
potential for unlocking previously
unseen patterns buried in the data which help reduce risk in the last
mile.
1. Is
there a co-relation between the out of bound of drill rpm rhythms from MWD Sensor data and operators experience?
2. What
are the frequent sequence of events (mud flow, Drill pressure,
temperature, CO2) exhibited in rhythm disturbances prior to a near miss event
which can trigger shutdown actions before an adverse event occurs?
3. Which
parameters where out of preset bounds (2 sigma events) before an incident
happened - pressure / temperature or rpm so that the checklists on
recalibration can be fine tuned?
4. Are
there keyword frequencies in maintenance inspection notes like
"vibration", "leakage" which can be mined from text
mining process which gives early warning signals to an impending
incident?
5. Where
their signals in identity management logs which provide a clue to forensically
investigate safety incidents?
6.
What is the NBI- "Next Best Intervention"
for a Drill, valve, pump etc based on its historical behaviour?
The data
to answer the above questions is available in the last mile sensor data
captured as illustrated below:
Throwing data-blind-spots into the spotlight
There are devices like pumps, valves and sensors attached to a drill which emit either change of value events or alarm events. These events can be stored in a central event repository instantiated on a Hadoop cluster with massively parallel processing capabilities which can scale in an economic manner. Storm can be used for event stream processing, PIG to do computations in data pipeline and Hadoop /HIVE to do batch based computation
3 key best practices to mitigate risk in Oil n Gas (HSE) using big data
So what are additional best practices which can be leveraged
to mitigate HSE risks?
1.
TRIANGULATE :
Look for patterns which reinforce each
other across SCADA/MWD/LWD/Surveillance video event streams - structured,
unstructured, internal and external.
- What are the top 3 cross event stream
triangulations which are important signals to watch out for?
2.
REACT STRONG TO WEAK SIGNALS :
Consider having organisational processes
which can pick the weakest link and amplification processes for field
intervention. An alert prioritisation framework which rank orders signals would
help risk specialists respond in a surgical fashion
- How can a weak signal be picked up and
transmitted in real time to a point of action?
3.
LAST MILE DATA BLIND SPOTS :
Be conscious of last mile data blind spots
be it contractor data, incident notes or device logs.
- How can we build a last mile integrated 360
degree integrated data picture?
We at Flutura believe that answering powerful
unanswered questions and executing the 3 key best practices would dramatically heighten
situational
awareness and mitigate operational risks.
New machine learning and big data innovations in processing
billions of events and a variety of messy data sources using Map reduce
paradigms has helped a lot of human generated companies to disrupt their
markets. The question is how can Oil and Gas companies use the same data
innovations levers on machine data to disrupt the way of managing last minute
risks?
As Marcel Proust said “The real voyage of discovery consists not in
seeking new landscapes, but in having new eyes”, we at Flutura too believe that discovering HSE (Health, Safety and Environment) risk
consists not in looking for more systems but in seeing with ‘new analytical eyes’
to look at previously collected data.
Srikanth Muralidhara
Co-Founder
Flutura Decision Sciences & Analytics
