Extracting the lifeblood of AI at ExxonMobil

By | 4 minute read | September 29, 2019

“Data is the new oil.” The quote goes back to 2006, credited to mathematician Clive Humby. Gartner’s Peter Sondergaard took it a step further, calling analytics data’s combustion engine.

While Artificial Intelligence (AI) still has that “new car smell,” it needs clean data in its fuel lines. The utility of data as a driver of digital transformation and, ultimately, as a cleaner-burning fuel for AI projects, comes only when it’s analyzed or extracted into meaningful narratives about the past, present and future. Otherwise, it can sit around the business in silos—uncollected, unorganized like unrefined crude, clogging up multiple cloud storehouses and repositories.

A decade ago, with a Ph.D. in Physics from MIT, Xiaojun Huang was working as a seismic interpreter for ExxonMobil in the Gulf of Mexico, figuring out what less-explored areas might hold promise for oil discovery. Reflecting on that mentally-tedious process, she did a calculation and concluded that AI could have easily turned a grueling, year-long process of churning through 2D seismic maps, tectonic and historical data into a six-month play that would detail the potential payoff of new hydrocarbon fields.

While ExxonMobil’s AI aspirations have been high, like most large enterprise companies, they were facing obstacles along their journey.

Data is siloed in hundreds and even sometimes thousands of applications, making collecting and organizing data complex and time-consuming. Then there’s the skill set. While most specialists go deep on their subject matter, they can lack the toolset of data scientist practitioners. Finally, just getting started with a whole new system can slow things down. AI begs for experimentation and somewhat visionary thinking. But it does require a process: involvement from a diverse set of stakeholders, and an agile approach that can take a small bite into one slice of the problem pie before attacking the entire meal.

Xiaojun, who had worked her way up to the position of senior advisor for the company’s Upstream Digital Transformation unit, could fully appreciate the challenge, and was in the perfect spot to help ExxonMobil succeed.  With the company’s multi-billion dollar investment in Guyana – a new offshore oil discovery – all eyes were on building a modern data platform that would enable AI and workflows that in turn could speed project development and more quickly achieve a return on the massive investment.

As the company had faced some challenges on how to apply AI to seismic interpretation, it turned to another company for help: IBM.

Xiaojun jumped at the opportunity and a chance meeting with one of the top data scientists at IBM, Vishnu Alavur Kannan, at the company’s Houston office.

What followed was a 12-month collaboration between seismic experts and the IBM Data Science and AI Elite team to essentially modernize all ExxonMobil’s data estates into one easy to access repository. Based on open source technologies, experts can access the data from its multicloud environment, helping make decisions on a much faster time scale. In other words, any team member can collect data from any application from any source and make it available seamlessly through APIs.

Describing the first day of the engagement, Xiaojun describes what happened when the geologist, the reservoir engineer, the drilling engineer, the seismic interpreter, site investigator and operational geologist and a formation evaluator all landed in one room.

“We did not talk about technology or the data silos. We pretty much asked them about their pain points and their whole workflow.” Quickly, the group arrived at a common goal: to collaborate seamlessly with a lot more efficiency on a project driven by the business needs. It was a tight approach she’d never really seen before in her large organization.

A team comprising of up to 20 different roles continued working side by side with the IBM data scientists to bring together all the data into a series of well-formed workflows initially for a small drill well planning exercise. This was no small feat – as data types span the realm from geology to geophysics to rock properties to economic analysis to well log data.

Now, for the first time, critical data for the Guyana project is available all in one place, accessible anywhere through various devices.  The data foundation was ready to prove itself in the field.

Benefits include an initially shortened planning cycle for the drilling design for new wells  – from nine to seven months. In an industry where players are racing against time to move first oil as early as possible, any efficiencies on capital investments are critical.  Another benefit is the time the team saved on data preparation—an estimated 40 percent, due to the agile processes developed along with IBM.

The data foundation passed its initial test and will now expand to handle both subsurface data and surface data in Guyana’s commercial projects. “As you can imagine, once we connect all that data together, we can start to ask very intelligent questions, and get the answers very quickly,” said Xiaojun.

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