Oil's new strategic asset: Leveraging technology, big data, and analytics to create competitive advantage
Judson Jacobs, Research Director Upstream Technology, IHS Markit discusses the development and application of new technology in the energy industry with Jose Silva, Lead Business Strategist Advanced Analytics and Emerging Technologies, Anadarko Petroleum Corporation; Bill Keller, Vice President Geoscience and Chief Geophysicist, EnerVest Ltd.; and Russell Roundtree, Vice President Upstream Data Analytics, IHS Markit.
Many in the industry are acknowledging the use of technology as a critical vehicle to extend or maintain the impressive efficiency and productivity gains that the industry has achieved in recent years across multiple resource types. A lot has been said about Advanced Analytics, Machine Learning (ML), Artificial Intelligence (AI) and Deep Learning. To cut through some of the buzzwords and get a sense of where this technology is in the industry today, IHS Markit gathered a panel of industry leaders who are on the front lines of developing and deploying these capabilities within their organizations and applying them to deliver business value.
At what point did your organizations recognize analytics as a discrete opportunity and something that required a devoted approach, and how are you developing organizational capability around it?
At Anadarko, we started with a bottom up approach back in 2014, with a group focused on figuring out if there was anything to this "trend." The goal was to determine what we could implement to address development optimization problems. Even at a small scale it took us time to figure out the tools, integration and strategies that would add value to the business unit. These learnings led to the formation of our Advanced Analytics and Emerging Technologies team, and we continue to create data science career paths and seek opportunities to scale up the application of the work being done.
Necessity has driven the adoption of analytics within EnerVest. Thinking back to conventional reservoirs, we used to be able to make a reservoir quality map, display production data and make sense of the information. With the advent of horizontal drilling and unconventional reservoirs, that's not possible anymore. Displaying production data on top of a geologic map doesn't reveal the same kind of answers as it once did. Part of our job is to determine why that is. The reality is that we now have a lot of other factors that are affecting production beyond just looking at the geology. Completion, targeting, vertical landing zones, and where underperforming or overperforming wells exist all need to be considered. The primary driver behind our use of analytics is trying to make sense of all those factors.
As we assess new technologies, is any E&P company capable of shifting to these models on their own? What is the role of partners in developing and supporting implementation?
There is a lot of data in the E&P industry that is organized either for easy consumption and delivery, or in a way that is most conducive to solving problems for different operators. It turns out that neither one of those two mechanisms put the data in a format that is easily ingested in today's modern AI and ML algorithms. There is additional transformation that needs to happen to make it valuable and analytics-ready. This is one of the primary areas that IHS Markit is ready to partner with industry. AI is not a new concept, but today we have a much richer algorithm set, computers are exponentially more powerful and have a lot more storage capability, there is a growing willingness in industry to move to the Cloud - these are enabling us to make AI more integrated with our problem-solving toolkits. One of my biggest recommendations to organizations looking to implement AI is to start small. Build your capacity to solve problems. Pick the problems that are impactful but also small enough in scope that you're likely to be successful.
This isn't the first time that the industry has gotten excited about digitalization and analytics. How do you identify the right problems, develop solutions to address them, and make sure those solutions get used?
Teams are incentivized to create products and partner with companies to properly leverage technologies and add value. We also implement processes to ensure that the things that we're trying to solve have short term, midterm, and long-term implications for the organization. A lot of our time continues to be spent on determining how we can grow our knowledge-base and build our skills in this area, while providing education around how technology can enable the organization to meet its goals on time, on budget, and in a safe manner.
In your experience, who within the organization seems to be the most receptive to new technology or technology processes? Is there a specific functional group or a generational difference?
At EnerVest, our analytics efforts have primarily been led by the geophysicists. In my experience I haven't noticed age or generation having much of an impact. As a profession, geoscientists tend to have an interest in integrating multiple types of data into their analysis. I think the real drivers are the people who are creative thinkers and who express curiosity about new technologies and ways to solve problems.
There have been technological disruptions in our jobs over our entire careers. We often address this by creating specialized groups to accelerate how quickly we adopt solutions and implement them to solve problems, but the reality is that big data and advanced analytics are becoming core competencies. It is incumbent on all of us learn the skills and the techniques, and eventually they will be part of business as usual.
How much does necessity come into play for companies evaluating a data-driven approach to field development and operations? Is it required to stay competitive?
We're in the camp that investments made in analytics and data science are value-add, and create a huge differentiator between us and our peers. It has provided capacity for us to communicate results to shareholders in ways we haven't been able to before. We see benefit in being able to benchmark ourselves against other industries. Following the downturn, we all had to ask how we could do more with less while addressing market requirements. A data-driven approach has helped us succeed.
The need for analytics is really being driven by the changes in the way the industry is operating. To be successful in today's environment, you need to integrate data from thousands of wells and different completion types and many other factors that we haven't had to consider in the past. Data science is a necessity in terms of helping us keep pace with the ways the industry is changing.
We're solving very difficult problems in industry today. The physics of unconventional reservoirs are 2-10 times more complicated than the physics of conventional reservoirs. On top of that, we are facing these problems with a smaller workforce and reduced capital. There is absolutely necessity to implement new technologies and techniques, because our industry has fundamentally changed, and will only continue to do so in the future.
To hear the full conversation, watch the on-demand replay.
Posted 8 January 2019
Follow IHS Markit Energy
- PennEast Pipeline to Be Built in Two Phases, Announces Developer
- Rio Grande LNG Rehearing Denial Raises New Issues for FERC
- From start to finish: Stages of life impact on oil and gas greenhouse gas emission intensity
- Continued focus on shareholder returns means there will be no upstream activity recovery in 2020
- Unprecedented challenges in global crude supply impact E&P company futures
- Ten downstream issues to watch in 2020
- Analysts: China's carbon market may aid least efficient coal plants
- Video: Hydrogen as an energy carrier: A win-win situation for mainland China for technology exports, indigenous resource deployment, and deep decarbonization