Fueling the Future: The Intersection of AI and the Oil & Gas Industry

Introduction

The Texas Responsible AI Governance Act (the “Act”) is set to take center stage during the 89^th Texas legislative session, which began on January 14, 2025.¹ The Act proposes new legislation to regulate development and deployment of artificial intelligence (AI) systems² in Texas.³ Specifically, the Act attempts to limit the use of AI to protect consumers from known or reasonably foreseeable risks arising from algorithmic discrimination.⁴ In its current iteration, the Act seeks to impose specific obligations for deployers and developers⁵ of AI, including that of human oversight, prompt reporting of discrimination risks, regular assessments of AI tools, prompt suspension of noncompliant systems, frequent assessments of impact, and clear disclosure of AI use.⁶

While much of the Act has to do with consumer protection and less to do with operations in the oil and gas industry, the Act marks a pivotal moment in Texas Legislature with the regulation of artificial intelligence. This Act will likely be the first of many to land on the desks of the Texas legislature, indicating a significant shift in state regulation of artificial intelligence. This article will explore how AI is currently used by oil and gas producers and the pros and cons that come with doing so. This article will also examine how a clear understanding of AI’s operations in the oil and gas industry could help the sector navigate potential regulation to come.

Use of AI in the Oil & Gas Industry

While the intersection of AI and the oil and gas industry is rapidly evolving, one thing is clear: embracing AI in the sector could transform innovation and efficiency in the oilfield. Concentrating AI technologies in: (1) exploration and development; (2) safety (3) regulatory compliance; and (4) back-office processes could maximize output while minimizing costs.⁷

Exploration and Development

AI algorithms are currently used to analyze geological, geographical, and production data to create exceptionally precise models of subsurface reservoirs.⁸ Exploration has been revolutionized by the precision of these AI-driven algorithms, allowing operators to pinpoint potential drilling locations with greater accuracy.⁹ By applying such AI tools, companies are saving time, money, and resources while reducing the overall environmental impact of drilling and production.¹⁰ Drilling a dry hole¹¹ can have severe impacts including but not limited to a significant financial loss, delay of production and setbacks in overall project timelines, potential water contamination, and the unnecessary release of greenhouse gases.¹² The use of AI during exploration and development can help determine the most promising drilling and recovery techniques while preventing waste.

AI algorithms can also generate demand forecasts for oil and gas products.¹³ The ability to predict drilling schedules and determine production rates using real-time information minimizes overproduction or underproduction and, therefore, reduces waste.¹⁴ Predicting how the market is operating with the use of real-time data can provide companies with a better grasp on logistics planning, supply chain activities, and any needs that fall into the cost of drilling wells.¹⁵

Safety Improvement

The enhancement of safety measures for oilfield workers is at the forefront of the oil and gas industry, and many are starting to turn to artificial intelligence for help. AI systems have the ability to identify potential hazards with the continuous monitoring of operations, triggering early warnings by detecting anomalies and predicting hazardous situations.¹⁶ Meaning, AI is working to prevent problems and accidents before they happen. With its continuous monitoring, AI can also identify patterns of work, allowing for preemptive measures to take place in real time.¹⁷ This provides the opportunity for immediate corrective actions geared toward accident prevention.¹⁸

Further, AI enables predictive maintenance by monitoring equipment health through sensors and data analytics.¹⁹ The equipment monitors and optimizes production stages, and the system as a whole, predicting potential failures before they happen and allowing for preventative maintenance.²⁰ The advantage here is twofold. First, AI is monitoring the health of the equipment and allows operators to plan maintenance before there is a failure. Second, this information affords operators the opportunity to provide necessary maintenance and reduce overall downtime of production.²¹

Increased use of predictive maintenance can also help reduce the risk of oil spill, as these systems can monitor flow rates, pressures, production quantities, and overall health of the equipment. However, if a spill does occur, AI can assess the scope of the accident, enabling quicker, more targeted responses.²² All in all, this provides a lower environmental footprint for oil and gas production.

Regulatory Compliance

AI systems have already proven successful in their ability to help companies comply with industry regulations by processing numerous requirements and providing insight into compliance. One case study identified a midstream operator that implemented an AI-powered safety and environmental monitoring system across its storage facilities, improving regulatory compliance rate from 95% to 99.8%.²³ This use of AI assists in reporting obligations and requirements, providing faster and more accurate information. Additionally, AI can detect real-time compliance issues, allowing for companies to address them promptly.²⁴

Automated Processes for Back-Office Tasks

AI systems have the ability to automate processes for back-office tasks and mitigate risks in aging infrastructure.²⁵ By handling repetitive and time-consuming tasks, AI reduces human error and frees up company time to focus on optimizing operations.²⁶ As the saying goes, time is money. By reclaiming valuable time, businesses can redirect their resources toward more strategic initiatives, improving efficiency and productivity.

The Challenges of AI in the Oil & Gas Industry

The use of AI systems in the oil and gas industry has been stunted by the challenges of such use. These challenges include but are not limited to: (1) energy demands; (2) cybersecurity and confidentiality; (3) cost of implementation; and (4) cultural resistance.

Energy Demand

A large challenge that comes with the use of AI is the substantial increase in energy demand. For example, a single ChatGPT query requires 2.9 watt-hours of electricity, compared to that of a Google search requiring 0.3 watt-hours of electricity.²⁷ By the year 2030, Goldman Sachs Research has estimated that AI data centers will see power demand grow upwards of 160%.²⁸ With the use of AI, lawmakers have become weary of how the expansion of AI systems and the race to zero net carbon emissions will intersect. This concern has already prompted several states, including New York²⁹ and Michigan,³⁰ to implement further regulation on AI systems.

Cybersecurity and Confidentiality

Cybersecurity and confidentiality are significant challenges to the widespread adoption of AI in the oil and gas industry. Interconnected AI systems have increased vulnerability to cyber threats, causing many to shudder at the idea of integrating AI into their practices.³¹ Further, opening the floor for not only employees, but also machines to handle confidential information has deterred companies from the widespread use of AI. Much of AI works because the machine learns more and more information. With the additional information the AI system “gets smarter.” However, there are ethical and legal issues with using the information from one client to feed the machine for the benefit of another client. More simply, companies do not want to share their confidential and proprietary information with their competitors.

This is an overarching problem, not just limited to oil and gas. However, for operators, their research and information to determine specific reservoirs, depths, areas, and the like are valuable data points. The efforts to obtain this valuable information is not cheap, and understandably, any company that has invested time and money into developing and getting this information does not want it shared with a machine that could potentially provide the benefit of this information to any competitors.

Cost

Cost, like anything else, has stunted the introduction of AI systems into the industry. While the integration of AI into the exploration of production of oil and gas looks to be economically promising, high upfront costs associated with infrastructure, software, and specialized equipment have deterred companies from widespread adoption.³² The new AI technology has a difficult time working with older software. Further, the older machines do not have the processing speeds necessary to accommodate the AI systems, creating a situation where companies are forced to incur a significant upfront cost to use these advanced platforms. Along with initial costs comes the long-term maintenance costs, including maintaining, updating, and retraining AI systems.³³

Cultural Resistance

Finally, the oil and gas industry is traditionally conservative and some members live by the principle that ‘if it ain’t broke, don’t fix it’ — especially when current systems work effectively and there seems to be no immediate need for AI integration. Other employees have difficulty grasping how AI systems perform, with many of the AI tools and algorithms operating in the dark. Without a clear grasp of how AI technology reaches its conclusions, a lack of trust accompanies employees’ unease.³⁴ In absence of understanding the ins and outs of how AI works, some are skeptical that AI can properly account for all the what-ifs and review all the information to provide a reliable result.

Litigation and AI

The integration of artificial intelligence in the oil and gas industry will have a significant impact for litigation. First the positive. As identified herein, with the identification of potential hazards by the continuous monitoring of operations and preventative maintenance, hopefully less accidents will occur, reducing the need for litigation. Additionally, more targeted drilling could result in less waste, decreased financial issues, and overall fewer contract disputes.

Second, the less positive. Many litigated matters for oil and gas operators apply standard of generally accepted oilfield practices, or that which a reasonably prudent operator would do. With the rapid growth of AI in the industry, the question is not whether the use of AI and related technologies will alter how reasonably prudent operators perform and shifts industry standards, but when.

Finally, there is the question as to how juries will accept the (non)use of AI by oil and gas companies. While each jury is unique, there looks to be a broader concern: an increased skepticism toward all forms of evidence.³⁵ Familiarity with AI systems, understanding of their limitations, and clear instructions can help to alleviate some of this skepticism. However, overcoming this distrust will likely be the next significant hurdle courts and trial attorneys will face.

The Future of AI

The role of AI will continue to bloom in the oil and gas industry as technology continues to evolve.³⁶ So, what does that mean for us in the industry? We need to become comfortable with the use and integration of artificial intelligence in all facets of our jobs, facing AI head-on rather than hiding from it. AI use in the oil and gas industry looks promising, with the expectation of more widespread and sophisticated applications of AI to come.³⁷ Further, comfortability brings confidence and adaptability, allowing the industry to leverage AI’s full potential to enhance decision-making, streamline operations, and drive innovation.

The Texas Responsible AI Governance Act marks a significant step in the evolving landscape of technology governance in Texas. With the anticipation of further regulation, it is crucial to understand how AI operates in the oil and gas industry, as well as the challenges it presents. Doing so will position the industry to better navigate potential regulation more effectively and ensure that AI’s integration is both informed and responsible.

1. Kathleen D. Parker & Gregory T. Lewis, The Texas Responsible AI Governance Act and Its Potential Impact on Employers, The National Law Review (January 13, 2025), Texas Proposes AI Governance Act Targeting Employer Practices
2. A copy of HB 1709 is available at: https://capitol.texas.gov/tlodocs/89R/billtext/pdf/HB01709I.pdf (last visited: February 13, 2025). The Act defines ‘Artificial Intelligence Systems’ to mean “a machine-based system capable of: (A) perceiving an environment through data acquisition and processing and interpreting the derived information to take an action or actions or to imitate intelligent behavior given a specific goal; and (B) learning and adapting behavior by analyzing how the environment is affected by prior actions.” Id. at 2.
3. Id.
4. Id. at 1-2. The Act defines ‘Algorithmic Discrimination’ to mean “any condition in which an artificial intelligence system when deployed creates an unlawful differential treatment or impact that disfavors an individual or group of individuals on the basis of their actual or perceived age, color, disability, ethnicity, genetic information, national origin, race, religion, sex, veteran status, or other protected classification in violation of the laws of this state or federal law.”
5. Id. at 3. Deployers are parties doing business in Texas that put into effect or commercialize a high-risk artificial intelligence system. Developers are parties doing business in Texas that develop a high-risk artificial intelligence system or substantially or intentionally modify an artificial intelligence system.
6. Parker & Lewis, supra note 1.
7. Anand Ramachandran, The Transformative Impact of Advanced AI Technologies on the Oil and Gas Industry: A Comprehensive Analysis, IBM Research – Thomas J. Watson Research Center at 18 (April 2024), (PDF) The Transformative Impact of Advanced AI Technologies on the Oil and Gas Industry A Comprehensive Analysis
8. Chirag Bharadwaj, Unleashing the Potential of Artificial Intelligence in the Oil and Gas Industry – 10 Use Cases, Benefits, Examples, Appinventiv (January 2, 2025), Artificial Intelligence in Oil and Gas: Benefit, Use Cases, Examples
9. Id.
10. Id.
11. Ronnie Blackwell & Joseph Shade, Primer on the Texas Law of Oil and Gas, LexisNexis at 149 (2017). Dry Hole, as defined by Texas Law of Oil and Gas Primer, is a “well determined to be incapable of commercially producing either oil or gas.” Id.
12. Drilling: Unearthing Disappointment: The Dry Hole Dilemma in Drilling, Faster Capital (Updated June 14, 2024), https://fastercapital.com/content/Drilling–Unearthing-Disappointment–The-Dry-Hole-Dilemma-in-Drilling.html
13. Bharadwaj, supra note 8.
14. Id.
15. Id.
16. The Role of AI in the Oil and Gas Industry: Transforming Operations and Enhancing Efficiency, Engineering Skill Share (last visited: February 13, 2025), https://engineeringskillshare.com/the-role-of-ai-in-the-oil-and-gas-industry-transforming-operations-and-enhancing-efficiency/.
17. Bharadwaj, supra note 8.
18. Id.
19. Id.
20. Id.
21. Ramachandran, supra note 7 at 27.
22. Bharadwaj, supra note 8.
23. Id. at 63.
24. Bharadwaj, supra note 8.
25. Oil and Gas Innovation with Generative AI, SoftServe Inc. (last visited: February 13, 2025), Generative AI in Oil and Gas: Optimize Production, Safety, and Sustainability | SoftServe.
26. Bharadwaj, supra note 8.
27. Charles Martin, Renewable Energy and AI Data Centers, Husch Blackwell LLP via JDSupra (January 2, 2025), Renewable Energy and AI Data Centers | Husch Blackwell LLP – JDSupra.
28. Id.
29. Id. New York State representatives have “introduced the New York State Sustainable Data Centers Act, which would require data center operators to power their facilities with enough renewable energy to align with New York’s climate goals.”
30. Id. “Michigan’s legislature passed S.B. 237, which modifies the state’s tax code to exempt equipment employed in operating and constructing data centers from use tax. The law does require that all data center facilities, to the extent possible, procure or contract for power from renewable sources, and that certain facilities certify that they have or will procure clean energy equivalent to 90% of their forecasted electricity usage on an annual basis.”
31. Bharadwaj, supra note 8.
32. Id.
33. Ramachandran, supra note 7 at 73.
34. Id. at 10, 71.
35. Paul W. Grimm, Cary Coglianese, & Maura R. Grossman, AI in the Courts: How Worried Should We Be?, Judicature 107, No. 2 (2024), AI in the Courts: How Worried Should We Be?.
36. The Role of AI in the Oil and Gas Industry, supra note 16.
37. Ramachandran, supra note 7 at 73.