Sustainability appears frequently in our industry’s vocabulary, yet its practical meaning inside reservoir management often remains broad. At ATCE 2025 in Houston, I had the opportunity to moderate a panel titled “Sustainable Reservoir Management: Leveraging Technology, AI, and Cross-Industry Innovation for Maximum Value.” The session brought together experts from operators, technology developers, and academia, creating a rare chance to compare how different parts of the industry view the same challenge.
What emerged was a clear theme: sustainability is not a separate initiative layered on top of reservoir engineering. It is the natural outcome of disciplined workflows, integrated decision-making, and informed use of digital tools. This article captures the reflections that carried the most weight and insights that point toward a more coherent and practical future for reservoir management.
Sustainability Begins With Better Reservoir Management
Across perspectives, sustainability was described less as a destination and more as the byproduct of efficient, technically grounded operations. One statement distilled this thinking:
Make every barrel count and all emissions accountable.
Underlying this is a simple truth: when waste is minimized, uncertainty is reduced, and decisions are grounded in physics and data, sustainability follows naturally. It is not an extra task; it is reservoir management done with clarity, discipline, and consistency.
From Recommendation to Control: Learning From Other Industries
One comparison that stood out was the reference to hyperscaler data centers. Their digital twins do far more than monitor; they act. Cooling loads and energy flows are continuously sensed, predicted, and adjusted without waiting for manual review, which cuts energy use substantially. These systems operate in genuine closed loop.
Reservoir management, in contrast, remains predominantly open loop. Models provide recommendations, engineers interpret them, field teams implement them. The delays, handoffs, and variability are embedded in the process.
We’re still recommending. Others are already controlling.
The point isn’t to automate the subsurface. It’s recognizing that closed-loop thinking is already routine elsewhere, and the components for it exist in our own workflows: integrated models linking reservoir, wells, and network behavior, edge intelligence capable of acting at the wellsite, and shared KPIs across teams.
The real barrier is not technology. It is alignment. The shift ahead is about coherence and trust, enabling decisions to evolve from periodic adjustments to more continuous, informed control.
Living Models: Reducing Fragmentation
Another theme that resonated strongly was the need for a living model, a single, continuously updated view of the reservoir and its connected systems. Instead of each discipline maintaining its own version and assumptions, a living model creates a shared foundation for interpretation and action.
When everyone works from the same model, collaboration becomes effortless.
A living model reduces rework, minimizes conflicting interpretations, and accelerates decision-making. The concept naturally extends into research environments as well, where shared data platforms and jointly developed projects help innovations transition more effectively from academia to field application.
Ultimately, integrated models lead to integrated decisions.
Evolving Skills: Data Fluency at the Center
As digital tools continue to reshape workflows, the skillset required in reservoir management is changing. Technical fundamentals remain essential, but engineers now need a deeper understanding of how digital systems behave and how to interrogate their outputs.
The engineer of the future is a data-fluent translator, not a data scientist.
Data fluency means recognizing where models are strong, where they fail, how to validate them with physics, and how to communicate insights clearly across disciplines. It is not about coding; it is about judgment. This competency will determine how effectively organizations turn digital potential into operational impact.
AI’s Role: Acceleration, Not Autopilot
AI already accelerates many reservoir tasks by exposing patterns, reducing analysis time, and processing large datasets. But speed does not equal certainty. A caution echoed repeatedly:
AI is excellent at interpolation. It breaks when you ask it to extrapolate.
This distinction is critical. AI enhances engineering workflows but cannot replace the physical understanding required for complex subsurface systems. Hybrid approaches that pair machine learning with physics-based models offer the most reliable path forward. AI expands awareness; physics anchors decisions. Together, they create workflows that are faster without sacrificing integrity.
A More Integrated Path Forward
The discussions at ATCE converged on a simple, pragmatic reminder:
Sustainable reservoir management advances when digital technologies are used where they add real value, and when collaboration is practiced, not just promised.
Several principles emerged consistently.
- Sustainability results from coherent, transparent reservoir practices, not standalone initiatives.
- Closed-loop workflows offer a meaningful step toward more responsive, informed optimization.
- Living models reduce fragmentation and create shared understanding across teams.
- Data fluency is becoming a foundational engineering skill.
- AI's value lies in acceleration, not replacement, of judgment.
The broader insight is clear: Technology is not the bottleneck; fragmentation is.
When teams work from shared data, shared models and shared intent, sustainability becomes a measurable outcome. Digital tools then shift from experimental add-ons to practical enablers of reservoir performance.
The insights shared in this article were shaped by the thoughtful contributions of the distinguished panelists who joined the ATCE 2025 session. Their breadth of experience across real-world operations, digital innovation, systems thinking, and academia enriched the discussion and helped illuminate what practical, sustainable reservoir management can look like today.
Panelists, all SPE members: Sam Perkins (ExxonMobil), Sathish Sankaran (Kosmos Energy), Carlos Granado (CMG), and Hamid Emami-Meybodi (Penn State)
