By Yogi Schultz
Many oil and natural gas producers are hesitant to explore the possibilities of artificial intelligence (AI) and implement AI applications. In this article, we challenge these obstacles in successful AI use cases.
I'm too busy exploring AI innovation
Most producers have made many successful technical and operational choices. Looking at other ideas, including AI, I believe it will distract you from what works well.
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Nonetheless, oil and gas AI offers fast acquisition, exploration and formation assessments. It offers accelerated approval, more predictable operation, and visible cost control. These major advantages are not distractions from existing oil and gas businesses.
Can oil companies really be too busy to increase their oil and gas production? The risk of AI is the biggest for slow adopters who are left behind by relying solely on old technology.
For example, AI can accelerate the process of geoscience modeling by consolidating multiple data sources and incorporating what is likely to be on a competitor's map. Such AI technologies are catapults for exploration and production.
What is the payment from AI?
The media has repeatedly argued that some of its producer competitors are dedicated to exploring AI. However, these peers are vague about payments. No returns are seen.
It's a short-sighted view. AI applications can demonstrate payments. For example, one producer has booked a large reserve increase using AI to win land sales bids.
Many producers are skeptical that the AI gee-whiz factor can be added to revenue and top line. How does AI add production? How will operational costs be reduced? How do you cut the capital you need?
To address these questions, a variety of oil and gas AI applications are accelerating exploration, enhancing production, reducing costs and improving cash flow. Many AI applications can quantify payments.
We don't want to use AI first
Some producers only look at the costs and risks of AI's first movers. They believe that being a quick follower still achieves most profits and reduces costs significantly.
It is a misconception that only a few companies in the oil and natural gas industry use AI. For example, many oil and gas companies have quietly embraced multiple AI applications.
Why do operators need to be at the edge of this technology's bleeding? We've seen cutting-edge peers spend a lot of money and invest a significant amount of time in some oil and gas projects, with little return. Being a fast follower just behind the cutting edge has paid off us with other oil and gas ventures. Why isn't this new technology?
If you start now, you won't be the first. Some oil and gas operators routinely use AI to monitor edge oil and gas production operations such as natural gas huffs and puffs, SAGDs and microbial floods.
AI risk appears to be higher than return
Some producers believe there is a serious risk of losing their own information to AI services and the risk of inaccurate AI recommendations.
The risks of these AIs are relatively small compared to the familiar risks associated with drilling and fracking of new wells. For most multi-flacked wells, financial profits are modest due to enormous capital expenditures. Nothing about AI, remotely, does not compare to the new risk risks that producers accept on a daily basis.
You don't want to abandon your competitive advantage by providing questions and prompts to AI services. If you train someone to improve their AI software using our data, will AI owners also sell information to their competitors?
Producers are suitable when using AI services from vendors with commercial models that specifically exclude training of input data.
AI hallucinations degrade value
Some producers are concerned that AI can produce inaccurate, biased or meaningless output that looks authentic. The frequency of these hallucinations is unknown. They can be difficult to find. AI concludes that it is not ready for prime time.
For example, if we ask experts ChatGpt or Gemini for technical questions, the answer can be wrong, misleading or superficial. What's even more dangerous is usually written in authoritative sounds.
To address this concern, some AI software developed for the oil and gas industry recognizes this trust issue. Some AI vendors use only industry peer-reviewed data. This response makes the AI output reliable.
We aim to utilize AI applications developed in the oil and gas industry that require industry-specific technical depth. We want to ensure reliable results. How can I achieve this goal?
For example, some AI applications use only information generated by the oil and gas industry and arbitrated by the government. That's significantly different from how AI services such as Gemini and ChatGpt are trained. They are trained on vast, contradictory, misleading, confusing data on the World Wide Web.
Fast AI development is overwhelming
Producers are making exciting announcements every day by AI application vendors, large and small. This suggests that the technology is fluid and not ready for routine and productive use. I'll wait clearly.
This clarity is currently available. Some AI applications implement strict quality control of AI output, allowing oil and gas operators to rely on the accuracy of the results.
Trustworthy AI should be built on trusted, industry-specific data. Trustworthy AI applications need to train multiple oil and gas authorities on data vetted to ensure accuracy and completeness.
How can you develop and test many AI software very quickly? Are there any quality controls? If so, is it useless or even dangerous?
Yes, new software has many risks. Based on the solid foundation of many technical end-user proposals that have contributed over the years, it is best to use AI applications.
Yogi Schulz has over 40 years of experience in information technology in a variety of industries. He has written for Engineering.com, Energynow.ca, energynow.com and other trade publications. Yogi works extensively in the oil industry, selecting and implementing finance, production revenue accounting, land and contracts, and terrestrial systems. He manages changing business requirements, the need to harness technology opportunities, and projects arising from mergers. His specialities include IT strategy, web strategy, and system project management.
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