The Oil and Gas industry is under pressure to maximize recovery, reduce unplanned downtime, and ensure safe, cost-effective operations across complex production networks. To understand how artificial intelligence (AI) is transforming day-to-day decision-making in this sector, we sat down with Pandurang Kulkarni, Sr. Product Manager at Beyond Limits. In this interview, Kulkarni discusses how Operations Advisor (OA) leverages Hybrid AI to support intelligent operations across the Oil and Gas value chain.
Operations Advisor is a real-time, Hybrid AI-powered decision-support platform built specifically for complex industrial environments like Oil and Gas. Unlike traditional tools that require clean data and time-consuming physics- based workflows, OA is built for the reality of the field. Production operations face dynamic, constraint-driven decisions every day—from lift optimization to flow assurance and asset reliability. OA was developed to deliver AI enhanced decisions. It integrates data from disparate sources with physics-informed models, machine learning, and cognitive reasoning into a unified, no-code platform that gives operations teams timely, actionable guidance and task automation.
Production optimization in Oil and Gas often suffers from fragmented systems and reactive workflows. OA changes that. A recent pilot with a leading oilfield service provider used OA to optimize ESP frequency set points across 98 wells. While traditional ML models failed due to limited frequency variation in the historical data, OA’s physics-informed Hybrid AI modeled the impact of frequency changes even with sparse data. By applying ESP affinity laws and causal reasoning, OA delivered high-confidence recommendations across the entire field—resulting in measurable production gains, reduced power consumption and fewer equipment failures.
In Oil and Gas, field operators often depend heavily on engineers to interpret data, apply situational intelligence and make decisions that align with production objectives. The legacy solutions- such as integrated production models- are heavily desgined around engineer persona often creating a disconnect with field operators at the front line. Additionally, engineers alone cannot monitor hundreds of wells or facilities on a daily basis. OA enables engineers to easily embed their expert knowledge directly into the platform, allowing it to push real-time, well reasoned prioritized actions to the field. Operators get specific guidance—like adjusting a choke or changing lift set points—based on current conditions and constraints. Engineers can spend less time resolving/debugging incidents and more time optimizing. The result is better alignment, faster execution, and improved performance.
OA leverages Machine learning to monitor critical systems like compressors, pumps, and injection networks using real-time sensor data and advanced anomaly detection algorithms. But it goes further than prediction by combining that data with rule-based logic and underlying physics. This helps flag issues early and offers pre-validated mitigation steps. For example, OA helped one customer detect instability at a compressor station, recommend corrective actions, and prevent a shutdown. This kind of early intervention improves asset uptime and prevents production deferment.
Physical sensors are expensive to maintain and sometimes fail entirely. In Oil and Gas, where some assets are remote or aging and sensors are exposed to harsh conditions, this is a real problem. OA’s virtual sensors fill the gap. They predict values like flow rate, pressure, or temperature using historical and lab data. Engineers can train and deploy these models without support from a data scientist. Once active, virtual sensors provide continuous, real-time estimates—reducing the need for manual sampling or costly replacements. This improves data coverage and increases the reliability of operational decisions.
OA connects the dots between planning, execution, and learning. Every action taken in the field is tracked, and its outcomes are recorded. Through retrospective analysis, engineers and planners can see what worked, what didn’t, and why. This feedback loop helps refine operating strategies and supports better decisions in the next cycle. Whether it’s optimizing field development plans or adjusting operating envelopes, OA ensures that lessons learned don’t get lost—they become part of the system as knowledge to further drive efficiency.
The stakes in Oil and Gas are high. You’re dealing with safety, environmental exposure, and multi-million-dollar assets. Operators and engineers need to understand the ‘why’ behind any recommendation. OA includes cognitive traces that clearly outline how each recommendation was generated—what data triggered it, what constraints were applied, and what actions were ranked. This transparency builds trust in the system and accelerates user adoption across all levels.
One of OA’s biggest advantages is its rapid time to value. In Oil and Gas, time is money. We’ve seen deployments deliver initial returns in a matter of weeks and full value in months. For example, during our ESP optimization pilot, the entire system was live across nearly 100 wells within a month, and it immediately identified performance gaps that traditional tools had missed. Because OA doesn’t rely on perfect data and doesn’t require complex coding, adoption is faster and more scalable than traditional AI platforms.
Most AI platforms focus solely on data contextualization, visualization or predictive alerts. OA goes beyond. Leveraging a Hybrid AI approach that integrates ML-derived set points and domain-specific logic, it actively drives true optimization opportunities. For instance, OA can recommend strategies to reduce the total power across the field without compromising overall oil production. Similarly, in scenarios such as a compressor trip, it can dynamically redistribute lift gas to optimize oil throughput. It’s not just about spotting a problem—it tells you what to do about it and why that action makes sense. That’s what Oil and Gas teams need: a system that works with them in real time to support decisions that impact production, safety, and cost.
Artificial intelligence for Oil and Gas must be more than theoretical. It needs to be practically usable, explainable, and built for operational complexity. That’s exactly what Operations Advisor delivers. Whether you're running upstream fields, managing midstream infrastructure, or optimizing downstream performance, OA brings real-time intelligence to the heart of your operations. It empowers your team to make better decisions faster—with confidence.
If you’re looking to apply AI in Oil and Gas operations, Operations Advisor is where you start.
If you're exploring how AI can unlock greater value across your Oil and Gas production operations, there's no better way to understand the impact than seeing it live.
Book a demo today and discover how your team can make faster, smarter, and more confident decisions with real-time guidance from Hybrid AI.
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