Oil and gas companies operate in a complex, high-stakes environment where efficiency, safety, and regulatory compliance are critical. Managing vast infrastructure, optimizing drilling operations, and ensuring equipment reliability require precision-driven solutions. However, traditional approaches to data analysis and decision-making often fall short, and even advanced artificial intelligence (AI) models struggle to meet industry-specific demands.
Large language models (LLMs), while powerful, are not trained on the specialized knowledge required for technical tasks such as drilling optimization, pipeline monitoring, and regulatory reporting. Small language models (SLMs), which can be designed with industry-specific training, can provide more accurate insights, stronger data security, and greater efficiency. As oil and gas companies seek AI solutions that align with their operational realities, SLMs present an opportunity to truly capture the power of GenAI.
Bridging the AI gap in oil and gas
Oil and gas companies face increasing pressure to optimize drilling, reduce downtime, and maintain compliance while managing vast amounts of operational data. The challenge lies in extracting precise, actionable insights from this data without compromising security, efficiency, or cost-effectiveness. While AI has the potential to help, not all AI models are built for the industry’s unique demands.
LLM’s broad, generalized training lacks the precision required for complex tasks such as drilling optimization, predictive maintenance, and emissions monitoring. SLMs, by focusing on industry-specific data, deliver greater accuracy, stronger security, and improved efficiency, making them a more practical and scalable AI solution. SLMs can operate securely on-premises and provide targeted insights that align with oil and gas workflows.
Optimizing operations with smarter AI
Oil and gas companies rely on predictive maintenance and drilling optimization to reduce downtime and improve efficiency. SLMs, when integrated with machine learning (ML), help refine these processes by analyzing seismic data, detecting anomalies, and providing engineers with precise recommendations for real-time drilling adjustments. In asset management, SLMs process data from IoT sensors to detect early equipment failures and support proactive maintenance that extends asset lifespan.
Misinformation in AI-generated insights — often called “hallucinations” — poses a serious risk in technical industries. By training on verified industry data, SLMs generate reliable and transparent outputs that reduce errors and maintain compliance. Their ability to cite original sources further strengthens trust in AI-driven decision-making and establishes them as a safer choice for oil and gas applications.
Why now?
Several factors make this the ideal time for oil and gas companies to invest in SLMs.
First, AI agents are becoming more sophisticated. SLMs now integrate seamlessly with ML algorithms to build up predictive analytics capabilities, which allows companies to analyze drilling performance, optimize production schedules, and detect equipment failures before they occur.
Second, data readiness has improved. Many companies are digitizing operational records, creating a wealth of structured data that can train and refine AI models. With proprietary datasets becoming more accessible, organizations can develop highly customized SLMs that align with their unique workflows.
Third, open computing standards are making AI integration more feasible. Edge computing and 5G networks enable real-time AI processing at field sites, reducing latency and improving decision-making speed. These advancements allow AI-powered insights to be deployed where they matter most — in active drilling sites, refineries, and offshore rigs.
Further, SLMs’ modular design allows for incremental expansion, enabling companies to start with specific applications and scale AI usage over time. For example, an initial deployment might focus on automating maintenance work orders. Once proven effective, the same model can be fine-tuned to support asset integrity monitoring or real-time drilling adjustments. By taking a phased approach, companies can maximize ROI while gradually expanding AI-driven efficiencies.
Finally, the rise of SLMs is lowering the barrier to entry, allowing smaller and mid-sized companies to compete. Historically, advanced AI solutions have been the domain of major oil and gas corporations with extensive R&D budgets. However, SLMs make GenAI more accessible and cost-effective. As a result, companies without large IT teams can more easily implement AI-driven solutions tailored to their operational needs.
What’s next?
As the oil and gas industry continues its digital transformation, SLMs will play a vital role in shaping the future of AI adoption. Their ability to provide industry-specific, secure, and cost- effective solutions makes them an invaluable asset for energy companies seeking to be more efficient and maintain a competitive edge.
By leveraging SLMs, oil and gas firms can move beyond generic AI applications and harness technology tailored to their unique challenges. Moving in this direction marks more than just an evolution in AI — it’s a fundamental step toward more innovative, safer, and more sustainable industry practices.
About the Author
Dr. Lakshmikantha Rao Hosur
Senior Partner – Energy, Resources, and Decarbonization
Lakshmikantha (Kantha) has over 20 years of consulting experience related to energy and the energy transition across Europe and North America. He has worked with clients and assets globally. Drawing on his deep knowledge of the energy value chain, Kantha is a strategist with a proven track record for delivering technology solutions—from ideation to go-to-market—and achieving delivery targets through consulting and portfolio management. Kantha holds a master’s degree in Geotechnical Engineering and a PhD in Soil and Rock Mechanics. He has previously worked with Repsol and Schlumberger (SLB) and is based in Amsterdam.
