As oil and gas (O&G) companies continue towards investing in
digital technologies to drive business value, it’s clear that data
science will play a big part in helping drive that success. In a
recent Gartner 2021 CIO Survey, about 50% of oil and gas companies
say they plan to increase their investments in analytics,
Artificial Intelligence (AI), automation, Internet of Things and
cloud technology. This is not surprising, as the tool becomes more
intuitive and computing power rises.
The O&G industry has long been a pioneer in computing use.
From building some of the world’s first supercomputers to
processing terabytes of seismic data to complex reservoir
simulation, engineers and geo-professionals have often been on the
leading edge of using data and models to unlock the earth’s secrets
and improve operational efficiency.
However, todays’ journey to AI is still a challenge for most
O&G companies. It’s well known that adopting this technology in
the industry is slow – but it doesn’t have to be. An area in
Upstream E&P that can reap the benefits of this technology is
petroleum engineering as they are continually finding new ways to
optimize production and costs. Today, they can combine their deep
domain expertise with data-driven analytics to enhance and develop
better asset strategies. Within 5-10 years, IHS Markit believes
many AI/Machine Learning (ML) applications will move from being the
purview of a specialized team of dedicated data scientists to
become a new set of tools used regularly by a wide range of
engineers across the company.
Upstream E&P: What Engineers Can Expect from an AI
Solution
Under current O&G market conditions, engineers face many
challenges that require them to find more efficient ways to solve
complex subsurface problems. Part of that responsibility involves
analyzing data from disparate streams and building models that feed
into their drilling, completions and production strategies. So, the
importance of model accuracy is vital to ensure confidence in
results and in decision-making.
Modern advanced data analytics solutions built for Upstream
E&P provide the advantage of AI without requiring specialized
data scientists, data managers and domain experts writing complex
code. The best solutions analyze vast amounts of data rapidly and
provide interpretability to help users better understand the model
results. Visual displays allow for efficient interrogation and
analyses. Even more sophisticated solutions provide workflow
templates that incorporate ML algorithms for addressing known
E&P challenges.
Built with existing well and production basin data, these
analytics platforms are flexible enough that users such as
petroleum engineers can incorporate proprietary datasets, validate
results and rerun their predictive models as more data and
knowledge accumulates.
Analytics Explorer dashboard analyses of multiple data sources
to be used in a predictive model. Results show the order of
importance of variables and which ones should be used to create the
predictive model.
Unboxing the Black Box
AI predictive models have been called Black Boxes: data is put
into the model; a user hits Run and out spits a number. It’s easy
to understand why engineers are often skeptical of this approach.
They learned long ago that models can be made to output nonsensical
results. The IHS Markit models of today are designed to be very
different. Users can – and should leverage their expertise to
provide reality checks, verify the data correlations and compare
outputs to ground truth numbers. Detailed error analysis is
available for each model, allowing users to review the error
associated with the inputs and final results. Users can identify
which inputs are causing high error, address them, and rerun the
model.
Importantly, the IHS Markit predictive tools do more than
predict future wells. They also quantify how much different well
attributes-for example, well path tortuosity, location or proppant
volume-are contributing to the final model results. Users can
choose which data goes into the model, evaluate the importance of
each attribute, and remove the data that does not make a meaningful
contribution. Removing noise and unimportant data strengthens the
model and provides better results. Even more importantly, the users
can test hypotheses to determine whether potentially important
factors are impactful or not. They can be open to surprises and
serendipity.
Accuracy and Interpretability
Perhaps most importantly, the results of IHS Markit’s modern
predictive models provide that elusive combination of results that
are both accurate and easy to interpret, even for non-data
scientists. For example, starting with a crossplot of actual
12-month production compared to the results predicted from the
model, users can select any data point and see quantitative
information about the impact on production of each and every
attribute in the model. The crossplot also shows the strength of
the correlation, so users can measure the model’s deviation from
the truth data.
A crossplot of actual 12-month production data (x-axis) and
predicted machine learning model (y-axis) results. The bar chart
shows the contribution of the input variables to production for a
selected well.
Reach Better Outcomes with Analytics
Explorer
IHS Markit is making advanced data analytics technology easier
to adopt and deploy, giving engineers the ability to utilize data
science methodologies along with their domain expertise to solve
complex subsurface problems.
Analytics Explorer is an advanced data analytics solution from
IHS Markit that makes data science accessible to everyone.
Developed for Upstream E&P, Analytics Explorer incorporates
advanced data science methodologies in guided and automated
workflows that incorporate interpretability methods to help
engineers better understand their models with confidence.
With customizable workflows like predictive modeling, Analytics
Explorer can be used for a broad range of applications,
including:
- Predicting the performance of wells before drilling them
- Quantifying the impact of specific parameters on a well’s
performance - Understanding optimum well design
- Understanding the impact of location and completion
quality - Identifying re-frac candidates
- Benchmarking performance among operators
