Part 3 – Automation is a Force Multiplier
As land leaders think about the strategies needed to navigate the complexities of today and tomorrow, multiple business priorities are top of mind, including how to maximize asset base, drive data-driven decisions, and contain G&A costs. Technology thought leader and CEO of ThoughtTrace, Nick Vandivere, has teamed up with W Energy Software to provide his valuable perspective on the tough challenges facing the land department and how to future proof energy companies with artificial intelligence, automation, and a single version of the truth, increasing business agility, reducing costs, and improving free cash flow.
By Nick Vandivere, CEO at ThoughtTrace
I’m so proud of where our industry is headed. Only a few years ago G&A was a function of production growth – add more wells, add more people. Unfortunately, the correlation ran in reverse, forcing oil & gas companies to resort to large-scale layoffs during inevitable market busts. But we are at an inflection point where things just don’t have to work that way anymore. Technology and automation have finally matured to create the “low-cost operator,” who can do everything an E&P needs to do, including scaling up production, with a lean team and greater business agility. Let’s talk about how to do that.
If you are just discovering my W Energy Software guest blogs, be sure to read my first post, which provides a rallying point for change and the characteristics that will define energy companies going forward. My last blog outlined three ways to uplift your margins by digitally mining leases for answers, including drilling and royalty obligations as well as acquisitions and divestitures. In my fourth and final post, I’ll complete the list of strategies for modernizing the land department by delving into the energy transition and the abundant opportunities that exist to redeploy skillsets as we head into the future.
Sustainable Cost Containment
Let’s talk about the elephant in the room. General and administrative (G&A) cost is often substituted when we want to talk about the costs associated with our people, i.e., the full-time employee with benefits, bonuses, paid time off, and sick leave. It’s my opinion that we all use G&A because that is more sterile and distances us from the hard reality that balancing the books has historically involved terminating employees with who we have personal connections with. For the land department, these are our close colleagues, including landmen, land managers, and lease analysts. So, when I talk about G&A, I know there are faces behind that word.
I think the best lens through which to view G&A puts the emphasis on achieving more with the resources you already have vs. reducing headcount. Literally speaking, the million-dollar question is:
How can my team add production and revenue without scaling up our workforce?
The answer is about empowerment. You need to increase the productivity of everyone in the land department with technology, leading to more output and enabling the entire organization to grow the top line without adding G&A costs. In that light, technologies that automate workflows, including artificial intelligence (AI), augment land teams with productivity drivers instead of automating them out of a job.
AI: A Pragmatic Approach
AI and machine learning (ML) represent extraordinary modern technologies that are transforming our way of life and ways of doing business. My advice to buyers and users of this cutting-edge tech is to throttle back your expectations of what will be required of your organization. You don’t need to become an expert to benefit from AI and ML, so there is no need to worry about creating algorithms or training models.
Partner with an expert in the field and co-own success with them. As you think about AI, find a quick win. Look for an area in the land department that creates immediate business value (i.e., a boost to your cash flow) at low cost with a small team, delivering measurable results now rather than waiting six months with a large-scale automation project before seeing any value.
Royalties are a good place to start. AI and natural language processing work wonders on lease packets by enabling identification of things like affiliate and cost-free language, for example, to be “understood” compared to merely performing optical character recognition (OCR)). Document understanding technology is transformative, enabling an analyst to accurately capture information at a speed and accuracy that was impossible until very recently, which creates massive value. With each success you encounter, step back and see how more effectively you can integrate AI document understanding and land data extraction into your day-to-day processes going forward. As you will discover, getting the most out of AI across the energy enterprise is a matter of integrating it with your digital ecosystem.
The Power of Integration
Glance around the information landscape of a typical oil & gas company and you’ll find scattered pieces of poorly integrated and disconnected software, including land management, accounting, division order, production, and tax. You’ve got well databases there, GIS here, division of interest over there, land records in multiple places, and maybe an ML or AI product in the mix, but it’s the digital wild west. How can your team fit all of this together and eliminate multiple versions of the truth?
Your team can address information sprawl in two ways: first, you can bring workflows and data from related applications together in one unified system of record, just as W Energy Software has done with their SaaS ERP. Or you can attempt to integrate multiple software applications from different vendors together to achieve a similar end state(beyond the scope of this blog).
But when it comes to integrating the output from AI-powered land data extraction the key is elasticity. An application programming interface (API) is the glue that holds different types of software and scattered sources of data together. And like glue, it often hard wires systems, requiring additional development to reconnect software A with software B. That’s why we completely rebuilt the ThoughtTrace API using an event source model, which enables teams to rapidly configure data pipelines with exactly the view or abstraction needed then connect our AI document understanding output directly to your software of choice. No data left behind – analyze thousands of leases and automatically import into W Energy Software’s land module for faster, better decision making or access ThoughtTrace data from data science and other apps in your digital ecosystem.
I’ll be concluding this series in my next blog where I’ll deviate slightly from hydrocarbon extraction to explore how the land department will always be crucial as our industry takes on new challenges with wind, solar, and geothermal energy production. If you have enjoyed reading my ideas on modernizing land workflows, feel free to reach out – I’d love to hear from you!
|Nick Vandivere serves as CEO of ThoughtTrace. During his tenure, he has led the transformation of ThoughtTrace into a company that is a technology and thought leader for the application of applied artificial intelligence and machine learning. As a software CEO, Mr. Vandivere believes that product innovation, reliability, and an outstanding user experience are the required ingredients for exceptional performance and long term value. Mr. Vandivere joined ThoughtTrace in 2010 as Director of Business Development. Prior to joining the company, he served as an officer in the US Army and later in an advisory role with the US Department of State. Mr. Vandivere holds a Bachelor of Science degree in Economics from Texas A&M University..