Overcoming AI Impediments for AI Success in Oil and Gas – Yogi Schulz

By Yogi Schulz

Many oil and natural gas producers hesitate to explore the potential of artificial intelligence (AI) and implement AI applications. This article challenges these impediments with examples of successful AI use cases.

Too busy to explore AI innovation

Most producers have made many successful technical and operational choices. Some believe that looking around at other ideas, including AI, distracts them from what’s working well.

Nevertheless, oil and gas AI offers high-speed acquisition, exploration, and formation evaluation. It provides accelerated approvals, more predictable operations, and visible cost control. Those enormous benefits are not a distraction from the existing oil and gas business.

Are oil companies really too busy to increase oil and gas production? The AI risk is biggest for slow adopters who are being left behind by relying solely on older technology.

For example, AI can accelerate the process of geoscience modelling by integrating multiple data sources and incorporating what is likely to be on a competitor’s maps. Such AI tech is a catapult for exploration and production.

What’s the payout from AI?

The media repeatedly claims some of our producer competitors are devoting significant resources to exploring AI. However, these peers are vague about their payout. We don’t see a return.

That’s a short-sighted view. AI applications can demonstrate a payout. For example, one producer booked a massive reserve increase using AI to win a land sale bid.

Many producers are skeptical that the AI gee-whiz factor can add to their bottom line or even their top line. How does AI add production? How does it cut operating costs? How does it reduce required capital?

To address these questions, various oil and gas AI applications are accelerating exploration, enhancing production, reducing costs, and improving cash flow. Many AI applications can quantify payout.

We don’t want to be the first to use AI

Some producers only see cost and risk for the AI first movers. They believe being fast followers will still achieve most of the benefits and significantly reduce costs.

It’s a misconception to say that only a few companies in the oil and natural gas industry utilize AI. For example, many oil and gas companies have quietly embraced multiple AI applications.

Why should operators be on the bleeding edge of this technology? We’ve watched our leading-edge peers spend copious amounts of money and invest considerable time in some oil and gas projects with little return. Being a fast follower, just behind the leading edge, has rewarded us in other oil and gas ventures. Why not this new technology?

You won’t be the first if you start now. Some oil and gas operators routinely use AI to monitor edge oil and gas production operations such as natural gas huff and puff, SAGD, and microbial floods.

AI risks appear higher than returns

Some producers see a significant risk of losing proprietary information to AI services and a risk of inaccurate AI recommendations.

These AI risks are relatively small compared to the familiar risks associated with drilling and fracking a new well. For most multi-fracked wells, the financial return is modest due to the immense capital expenditure. Nothing in AI compares remotely to the new well risks producers routinely accept.

We don’t want to surrender our competitive advantage by contributing even questions and prompts to an AI service. If our data is used to train someone’s AI software to improve, will the AI owner also sell our information to our competitors?

Producers are better served when they use an AI service from a vendor with a for-profit model that specifically excludes training on their input data.

AI hallucinations undermine value

Some producers are concerned that AI sometimes produces inaccurate, biased or nonsensical outputs that appear authentic. The frequency of these hallucinations is unclear. Spotting them can be difficult. We conclude AI is not ready for prime time.

For example, when we ask ChatGPT or Gemini a technical question on which we are experts, the answer can be wrong, misleading or superficial. Even more dangerous, it is typically written to sound authoritative.

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 trustworthy.

We aim to utilize AI applications developed in the oil and gas industry, which require the technical depth specific to the industry. We want to guarantee trustworthy results. How can we reach this goal?

For example, some AI applications use only information generated by the oil and gas industry and adjudicated by the government. That differs markedly from how AI services such as Gemini and ChatGPT are trained. They are trained on the vast, contradictory, misleading and confusing data on the World Wide Web.

Rapid AI developments are overwhelming

Producers see large and small AI application vendors making supposedly exciting announcements every day. It suggests the technology is in flux and not ready for routine, productive use. We’ll wait for clarity.

This clarity is available now. Some AI applications perform rigorous quality control of AI output, enabling oil and gas operators to rely on the accuracy of their results.

A trustworthy AI must be built on dependable, industry-specific data. A reliable AI application must be trained on data scrutinized by multiple oil and gas authorities to ensure accuracy and completeness.

How can so much AI software be developed and tested so quickly? Is there a lack of quality control? If so, isn’t that useless or even dangerous?

Yes, brand-new software can come with various risks. It’s best to use AI applications based on a solid foundation of many technical end-user suggestions contributed over multiple years.

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Yogi Schulz has over 40 years of experience in information technology in various industries. He writes for Engineering.com, EnergyNow.ca, EnergyNow.com and other trade publications. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts, and geotechnical systems. He manages projects that arise from changes in business requirements, the need to leverage technology opportunities, and mergers. His specialties include IT strategy, web strategy, and systems project management.

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