The daily production meeting was built for yesterday’s operations, not today’s reality.

In this instalment of Capgemini’s Future of Oil & Gas series, we explore why the traditional daily production meeting is no longer fit for purpose, and how real‑time data and conversational intelligence can transform it from a retrospective ritual into a live engine for operational decision‑making.

For generations, the oil and gas industry’s morning production meeting has been a ritual of preparation and projection. Technical and commercial teams pore over spreadsheets and waterfall charts – cut and pasted from the latest Supervisory Control and Data Acquisition (SCADA) exports into Excel, then into Power BI or slide decks. By the time the slides hit the screen, the numbers reflect yesterday’s operational state rather than today’s emerging reality. It’s a static view in a dynamic world: valuable, but fundamentally retrospective.

Systems of record, not systems of foresight

In every corner of the upstream sector – from offshore platforms to continental basins – operators still rely on daily production reporting software that centralises data capture, hydrocarbon allocation, reconciliation, and regulatory filings.

Established platforms like IFS Merrick have made substantial progress in automating field data capture and consolidating run tickets, meter readings, and production allocation within a single system. Their primary strength, however, remains the accurate representation of past and current operations. While these platforms increasingly support exception‑based surveillance, trend analysis, and KPI tracking, they are principally designed to describe what has happened and what is happening now. Forward‑looking prediction, scenario exploration, and guidance on what is likely to happen next typically sit outside the core transactional layer and require additional analytical or intelligence capabilities.

Now, that status quo is shifting

Artificial intelligence (AI) in oil and gas has steadily matured from experimental pilots into hybrid systems that blend statistical reasoning with core engineering principles. Physics-based AI models – like Orbital from Applied Computing – don’t treat the subsurface or facility behaviour as a black box. Instead, they embed first-principles physics into the learning process, so that outputs respect conservation laws and production physics even when data is sparse or noisy.

By incorporating governing equations alongside historical and real-time data, these models deliver rapid, scientifically consistent predictions of process behaviour, enable thousands of “what-if?” scenarios to be evaluated in seconds, and significantly enhance forecast reliability compared with data-only approaches.  

The implication for oil and gas operations is seismic

No longer are operators bound to pre-built reports. Instead, they can engage with decision systems conversationally, retrieving insights and exploring futures in a fluid, iterative way. The business benefit is not only speed. Production forecasting, anomaly detection, and constraint analysis are delivered in real time but also depth. Systems that understand the physical dynamics of wells, facilities, and networks can contextualise responses in ways that generic analytics cannot.

Imagine asking a system at 7:45am how production across multiple fields compares to plan, what constraints are driving variance, and whether deferring a maintenance branch would materially impact monthly targets. Then in the same session, asking for a simulation of the next 48 hours under new choke settings. This capability is not fiction; it’s what’s being enabled today by hybrid large language models (LLMs), trained on physics-based simulation, real-time telemetry, P&IDs, maintenance logs, reservoir models, and hydrocarbon accounting rule sets.

Crucially, this is not about handing over control to an AI. Safety remains paramount. The systems discussed here don’t autonomously execute actions on assets. They act as augmented decision partners, aggregating data from historians and field capture platforms, normalising and reconciling it, and presenting granular analytical and predictive output. Users remain in command, setting scenarios, challenging assumptions, and applying domain judgement.

This is a departure from both traditional SCADA dashboards and generic AI chat interfaces. Analytical platforms in industrial sectors have long connected time series data to visualisations that help engineers spot trends, flag exceptions, and support meetings. Tools such as Seeq and similar analytics environments give structured visibility into historian data, enabling diagnostic and predictive insights. These are valuable, but they still require expert navigation and manual interrogation to answer complex, evolving questions.  

Conversational AI interfaces change the game

Instead of manually filtering data and building plots before a meeting, teams can query the system dynamically during discussions, shifting focus and depth as insights emerge. This transforms the meeting from a rehearsal of prepared content into a live exploration of operational reality. The intelligence is co-created in the moment.

How does physics-based AI elevate this further? By anchoring predictions in real world scientific constraints, these models reduce the risk of spurious or context-free answers. Traditional machine learning models are challenged when faced with conditions outside their training distribution or when data is noisy – common conditions in oil and gas operations. Physics-informed models, whether implemented as hybrid neural operators or advanced neural networks guided by governing equations, constrain predictions to what is physically plausible and interpretable. This leads to more reliable forecasts, better “what-if?” analyses, and a higher degree of confidence in operational decision support. 

The result is transformative for daily production intelligence. What was once a task of manually reconciling yesterday’s numbers becomes a process of interactive forecasting and scenario planning, where teams can test assumptions and explore the impact of different options in real time. The hard work shifts from report generation to insight exploration. Teams spend less time preparing, and more time understanding and acting.

This evolution does not replace existing operational systems; it sits atop them, federating data, embedding domain logic, and surfacing insight. Legacy production reporting and allocation engines remain critical for compliance and internal bookkeeping. Yet the next generation of operational governance will integrate conversational AI with physics-informed reasoning, giving companies the capacity to ask robust questions, receive coherent analytical responses, and iterate through options at the speed of business.

Capgemini is focused on helping organisations bridge the gap between experimentation and value realisation

Many companies have explored AI in isolated pilots or proofs-of-concept but have struggled to embed them into daily decision processes that drive material performance improvement. Working with partners in advanced computing and AI research, including Applied Computing, we’re supporting clients to integrate hybrid AI models into their operational intelligence stack. This means moving from theoretical experiments to predictive and prescriptive systems that inform daily production optimisation without compromising safety or accountability.

Again, we’re not talking about replacing engineers; we’re talking about amplifying intelligence, so that operators, production managers, and asset leaders can ask harder questions, see deeper into their data, and examine future outcomes without having to manually stitch together disparate sources.

The world of analytics has moved far beyond dashboards and static forecasts. Now, it’s all about conversation, insight, simulation, and collaboration. As this capability matures, the value of human judgement is multiplied by the speed and breadth of machine reasoning. For an industry under pressure to operate with higher performance, greater agility, and tighter margins, this is a huge opportunity: leverage AI to not just review what happened, but to explore what could happen and decide what should happen next.

Let’s say goodbye to the daily production meeting of yesterday. The next generation is live, interactive, and engineered for the unpredictable realities of energy operations.