Architecting predictive intelligence in oil and gas

Praveen Shiveswara Sridharamurthy has spent more than two decades architecting cutting-edge solutions for oil and gas operators worldwide, helping them harness the power of operational data to improve efficiency, safety, and profitability. Currently a Principal Solution Architect at LTIMindtree, Praveen specialises in integrating real-time telemetry, historical maintenance intelligence, and machine learning to enable predictive reliability for midstream assets.

Under his leadership, complex enterprise AI platforms have been deployed on AWS and Azure, providing operators with early-warning insights, actionable risk indicators, and operational efficiency gains that were previously unattainable. His work reduces downtime and operational costs, and also supports environmental compliance and workforce safety, making reliability a strategic business driver rather than a reactive challenge.

In this interview, Jois shares his perspective on the evolution of midstream reliability, the practical applications of AI in operations, and the lessons he has learned from decades of experience in designing and implementing data-driven solutions for high-stakes energy infrastructure.

Ellen Warren (EW): Praveen, let’s start with some background. You’ve done significant work across oil and gas upstream real-time data management as well as midstream operations. Can you walk us through your career journey from early roles in operational data architecture to your current position as Principal Solution Architect at LTIMindtree?

Praveen Shiveswara Sridharamurthy (PS): I started my career working on operational data architecture, closely supporting field operations by integrating telemetry, historian to improve decision-making for field operators and engineers. That experience gave me a strong foundation in how data is actually used in asset-intensive environments.

Over time, I expanded into enterprise data platforms, cloud, and advanced analytics, leading initiatives that bridged OT and IT and enabled predictive use cases like reliability and maintenance optimisation. Today, as a Principal Solution Architect at LTIMindtree, I focus on designing end-to-end Enterprise AI and data solutions for industries like oil and gas, helping clients move from reactive operations to predictive intelligence with measurable business impact.

EW: Before your recent work in predictive reliability, you led major initiatives in upstream real-time data platforms. What were the core challenges you were solving in upstream operations, and how did those experiences shape your approach to large-scale data architecture?

PS: In upstream operations, the core challenges were data silos, latency, and adoption. Real-time telemetry, drilling data, production data, and maintenance information all lived in separate systems, making it difficult for engineers to get a single, reliable view of asset performance. Decisions were often reactive and based on partial data.

My role was to design real-time data platforms that could ingest high-frequency field data, standardise it, and make it available with the right context and governance. This meant solving for scale, reliability, and security while bridging OT and IT environments. Those experiences shaped how I approach large-scale data architecture today: start with operational reality, design for data quality and lineage from day one, and ensure the platform can support advanced analytics and AI without disrupting frontline workflows.

EW: Assets and equipment reliability remains a persistent challenge in midstream operations. What are the primary factors contributing to unplanned downtime, and why do traditional monitoring and SCADA-based approaches fall short?

PS: In midstream operations, unplanned downtime is typically driven by a combination of equipment degradation, process variability, and limited early-warning capability. Common factors include compressors and pump wear, instrumentation drift, control system misconfigurations, and operating conditions that slowly move assets outside their optimal envelopes.

Traditional SCADA and rule-based monitoring systems are effective at detecting threshold violations, but they are largely reactive. They tell you something has already gone wrong, not why it’s happening or what is likely to fail next. These systems also struggle with complex failure modes that develop over time and across multiple signals. The gap is that SCADA lacks context and learning, it doesn’t correlate real-time telemetry with historical behavior, maintenance history, or environmental conditions. As a result, operators are left responding to alarms instead of anticipating failures. This is why predictive, data-driven reliability approaches are increasingly necessary in midstream operations.

EW: How does integrating real-time telemetry with historical maintenance data enable predictive insight across complex assets? Can you share a concrete example from a deployment where this integration made a measurable difference?

PS: When you look at telemetry alone, you’re really just seeing what the equipment is doing at this moment. Maintenance data tells a very different story: what’s been worked on, what’s failed before, and how the asset has aged. The real value shows up when you put those two together.

On one midstream compression predictive project, we tied real-time vibration, temperature, and pressure data to maintenance work orders and failure history. What we noticed was that certain vibration patterns didn’t mean much by themselves, but when the compressor had recent seal or bearing work, those same patterns were strong early indicators of an upcoming trip. That gave the operations team weeks of advance warning instead of hours. They were able to plan maintenance during scheduled windows rather than reacting to failures. We expect fewer unplanned shutdowns, better availability, and far less firefighting, which is really the outcome everyone in operations cares about.

EW: You have extensive experience designing and deploying cloud-native solutions on AWS and Azure. What advantages do cloud platforms provide when building real-time, mission-critical data systems for oil and gas operations?

PS: Cloud platforms like AWS and Azure give you speed and scale that are very hard to achieve on-premises, especially for real-time operational systems. You can ingest high-frequency telemetry, process it in near real time, and scale up or down as asset counts and data volumes change, without over-engineering the infrastructure upfront.

They also give you built-in reliability and resilience. Things like managed streaming, high availability, automated backups, and disaster recovery are essentially table stakes in the cloud, which is critical when these systems are supporting live operations. Another big advantage is how quickly you can move from data to insight. Cloud-native services make it easier to integrate analytics, AI, and visualisation directly on top of real-time data, instead of building and maintaining everything yourself. Most importantly, the cloud lets you evolve safely. You can modernise in phases, keep critical OT systems isolated where needed, and still deliver real operational value without disrupting day-to-day operations. That balance is what makes cloud platforms so effective for mission-critical oil and gas systems.

EW: Across both upstream and midstream environments, how do you think about architecting data pipelines that can handle high-velocity sensor data while still supporting advanced analytics and machine learning?

PS: I usually start by separating concerns. High-velocity sensor data needs to be ingested and processed in a way that’s optimised for speed and reliability, while analytics and machine learning need clean, contextualised, and well-governed data. Trying to force both into a single layer is where a lot of architectures break down.

In practice, that means using a streaming layer to handle real-time ingestion and basic processing, then landing the data into a scalable, cloud-native data platform where it can be enriched with asset context, maintenance history, and operational metadata. From there, you can support everything from real-time dashboards to batch analytics and ML training without impacting live operations. Across upstream and midstream, the key is designing for evolution. You assume data volumes will grow, use cases will change, and models will mature. So you build pipelines that are resilient, loosely coupled, and observable and you always keep operational reliability as the top priority.

EW: Can you share a project where your personal architectural decisions or technical leadership directly drove improvements in operational reliability or performance? What made that work particularly impactful?

PS: One of the most impactful projects I led was as a Cloud Application Architect during AWS cloud transformation for an Energy customer. I played a key role in designing and implementing cloud-native systems that replaced legacy on-premises infrastructure, including the Operations and Maintenance ecosystem. This migration provided scalable, agile cloud infrastructure, enhanced operational uptime, and reduced IT overhead, enabling on-demand compute and storage while lowering total cost of ownership.

Beyond infrastructure, I enabled the use of AWS IoT, edge computing, and machine learning to optimise operations. Cloud-enabled analytics reduced incidents at power distribution facilities by around 40% and cut field service trips by roughly 50%, directly improving reliability and operational efficiency.

EW: Transitioning from reactive operations to predictive, data-driven decision-making often requires cultural change. How have you helped engineering and operations teams adopt AI insights and integrate them into daily workflows?

PS: In the midstream compressor predictive AI project, I helped engineering and operations teams move from reactive to predictive decision-making. I embedded AI insights directly into existing dashboards and maintenance workflows, providing alerts and recommended actions that were immediately actionable. We built trust in the models through hands-on workshops and early wins like identifying potential failures before they impacted production. This approach ensured that AI became a routine part of daily operations, not just an optional analytics tool.

EW: What is your future prediction? How do you see real-time data platforms, cloud architecture, and AI converging to shape the next generation of oil and gas operations over the next five to ten years?

PS: Over the next five to ten years, I see real-time data platforms, cloud architecture, and AI converging to fundamentally transform oil and gas operations. Today, data is often siloed across production, maintenance, and enterprise systems, and AI is applied in isolated pilots. The next generation will integrate these elements end-to-end: real-time streaming data from field assets will flow seamlessly into scalable cloud platforms, enabling AI and analytics to continuously monitor, predict, and optimise operations across entire facilities and even regional portfolios.

This convergence will enable predictive and prescriptive decision-making at scale from anticipating equipment failures before they occur, to optimising energy usage, chemical consumption, and production efficiency in real time. Cloud-native architectures will provide the flexibility, security, and scalability needed to handle massive data volumes, while AI will become deeply embedded in daily workflows, enhancing operator decisions rather than replacing them.

Ultimately, the industry will shift from reactive maintenance and manual optimisation to fully data-driven, predictive operations, unlocking both operational excellence and significant cost efficiencies while improving safety and environmental performance. The organiasations that embrace this convergence will set the standard for resilient, intelligent, and digitally optimised oil and gas operations.

Read the article online at: https://www.oilfieldtechnology.com/digital-oilfield/27012026/architecting-predictive-intelligence-in-oil-and-gas/