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A paradigm shift is occurring in the energy industry, as the
weakening economy and current market slowdown continues to air
uncertainty. Lower oil prices, challenging investment environments,
cost reductions, and the diversification of energy resources are
current knowns. But in a volatile market, conditions can change
quickly. The ability to make critical decisions, before your
competition does, requires agility. Digitalization and digital
innovation are now fundamental requirements to contend with these
profound changes - using predictive analytics to better anticipate
the future, real-time data insights to drive better decisions, and
the integration of artificial intelligence, machine learning, and
automation to take advantage of every possible increment of cost
and speed.
Energy market participants are becoming increasingly
sophisticated. They are looking for technologies and accelerated
analytics solutions that drive efficiencies, improve outcomes, and
reduce risk. The greatest transformational potential for
digitalization is its ability to break down boundaries between
energy sectors and silos, increasing flexibility and enabling
integration across the entire energy value chain. As Clive Humby
famously stated: "Data is the new oil. It's valuable, but if
unrefined can not really be used. It has to be changed into gas,
plastic, chemicals, etc. to create a valuable entity that drives
profitable activity; so data must be broken down, analyzed for it
to have value."
As digital transformation continues to evolve in energy,
innovative technologies are revolutionizing the ability to leverage
petabytes of data. New rapid computational environments enable the
integration of data science, analytics, and location intelligence
for accelerated, interactive "speed of thought" and decision
support.
Graphics processing units (GPUs) are the main driver in
accelerated analytics (also known as GPU analytics). GPUs harness
the massive parallelism of high-performance computing in order to
accelerate processing-intensive operations in data science. The
gaming industry is powered by GPUs. In the era of growing AI and
machine learning, incorporating the computing power of GPUs is
vital for processing and extracting insights from a continuous flow
of energy datasets, which contain billions of data points, with
lightning speed and accuracy. The amount of data being generated
every year across the energy ecosystem is beyond "big data" - it's
"mega data." The majority of mega data analytics used in the energy
industry today involves some measure of geospatial and
spatiotemporal data (measuring location and time). Interactive
spatiotemporal analytics enable scalable visualizations and high
performance unparalleled by existing business intelligence
tools.
The velocity, volume, and diversity of data brought together for
interactive analytics now can drive additional value from
historical data. It also can enable imbedded models for real-time
data interrogation, yielding previously unseen insights. Merging
telematics data from cell phones and smart cars (using location
intelligence and real time movement), unrelated data streams from
across multiple business verticals, and weather data, companies can
enhance overall business intelligence and critical decision-making
in ways that were not possible even a few years ago. Hurricanes are
a great example. The ability to track weather in real time is
critical when natural disasters threaten downstream infrastructures
and supply chains. Forecasting tools, algorithms, historical data
embedded with geospatial analysis, and 4D geospatial analytics
leverage the dimensions of time and space for more accurate
predictive analytics - because understanding the past enables
better foresight into the future.
Below are two examples of how IHS Markit is leveraging data
science, accelerated analytics, and location intelligence from a
macro- to micro-level, providing detailed insight and new value
from existing data assets.
US directional survey/trajectory dataResults enable analysts to
understand the average weighted break-even point for an entire
state, by play, section, operator, and well. It offers the ability
to run different models and analytics scenarios, and analysts can
leverage embedded algorithms and models to grade acreage by peak
rate, break even, cost, quintiles, spacing, or any other chosen
metric.
Risk analysis with multivariate analytics: Leveraging
almost 400 million data points interactively
Data requirements:
US well and production data
Commodity price
Break-even model, cost model
Detailed completion and frac data, including stagesand
completion design
Decline curve analysis and forecasting model Results allow
analysts to instantly see the effect of completion changes over
time (such as fracturing stage count). They can understand the
relevant commodity price environment (e.g. West Texas Intermediate
crude) and effects on productivity performance. Users can quickly
perform operator benchmarking for asset evaluation with scenario
analysis through time and space.
Beyond enabling an unprecedented scale and predictability of
outcomes, accelerated analytics can also help organizations and
stakeholders develop a greater understanding of data and its true
intrinsic value. Many data efforts are descriptive and diagnostic
in nature, focusing primarily on what happened and why things
happened the way they did. These are important points, but they
only tell part of the story. Predictive analytics broaden the
approach by answering why it happened and "what if," so outcomes
can be weighted against probable certainty.
Every day, every hour, and every minute, energy participants
make decisions with wide-ranging implications that require the
ability to see in real time the interconnected factors that impact
their organizations. When mega data assets are leveraged together,
companies require a holistic approach to understand the "connected
enterprise" and reap exponential benefits from the convergence of
automation, communications, and information technology across
digital oilfields, pipelines, and refineries. Accessing and
monitoring assets from upstream, midstream, and downstream
operations - and merging disparate oilfield data into actionable
information - is necessary for companies to remain competitive. As
the energy landscape fluctuates and transitions, energy companies
require new intelligence with a wider picture and a deeper
focus.
Digital transformation initiatives currently present both
opportunities and challenges for the oil and gas industry where a
low-price environment makes managing costs, timelines and
operational efficiency crucial to success. IHS Markit provides
strategies to meet digital transformation needs across asset teams
and resource lifecycles. Find out more:
Digital Transformation in Upstream Energy
Posted 16 December 2020 by Ali Sangster, Executive Director, Upstream Data Strategy and Analytics IHS Markit and
Leanne Todd, Senior Vice President, North America Upstream Energy, IHS Markit
