Across Australia, Southeast Asia, and the Middle East, industrial plants are generating more operational data than ever before -yet most of that data never gets used to improve how the plant actually runs. Sensors generate readings every second. Control systems log thousands of alarms. Instruments measure temperature, pressure, and flow around the clock. But without the right structure to turn that data into decisions, it sits unused in a historian’s mind.
Plant digitalisation changes that. At its core, it is about connecting your operational data to tools that help you see, understand, and act on what is happening inside your facility. And the digital twin for a process plant is the technology driving the most questions from asset owners right now.
Sarom Global, a Sydney-based multi-disciplined engineering consultancy supporting projects across the Asia Pacific, Middle East, and Americas, works with asset owners at every stage of this journey. This guide explains what a digital twin actually is, the three types you need to know, how the technology works in plain terms, and what you need in place before you start -including a practical Readiness Checklist you can use to assess your own facility today.
What Is a Digital Twin for a Process Plant?
Most engineers are already familiar with 3D CAD models and P&IDs (Piping and Instrumentation Diagrams). A digital twin is fundamentally different from both. A CAD model is a static drawing -it shows what the plant looked like when it was designed. A P&ID shows how systems are connected on paper.
A digital twin, by contrast, is alive. It receives continuous data from field sensors and instruments, updates itself as conditions change, and reflects the actual current state of the plant -not the design intent from ten or twenty years ago. This distinction is what makes the digital twin genuinely useful rather than just an expensive visualisation tool.
This level of operational visibility is fast becoming the standard that regulators, insurers, and senior management across the energy, oil and gas, and water sectors expect. Understanding what type of digital twin your plant needs -and whether you are ready for one -is the most practical place to start.
1. The Provenance (Descriptive) Digital Twin
The Provenance Digital Twin is a structured, validated digital record of your plant’s physical assets -equipment specifications, instrument datasheets, piping layouts, maintenance histories, and as-built configurations. Think of it as a living, searchable asset register with verified data attached to every item.
This is the starting point for most plants, particularly brownfield facilities built before digital records were standard. A large LNG facility, for example, may have 12,000 or more instruments across multiple process trains -many documented only on paper drawings that have not been updated since the last major turnaround. A Provenance twin systematically captures, digitises, and validates all of that information into a single, accessible model.
The Provenance twin does not simulate or predict anything on its own. But it is the foundation everything else is built on. Without accurate as-built data, any more advanced twin will simply inherit and amplify every existing error.
2. The Simulation (Predictive) Digital Twin
The Simulation Digital Twin uses process models and historical operating data to predict how changes in conditions will affect plant performance -before those changes are actually made.
A power generation plant can use a simulation twin to model the impact of burning a different fuel blend, or to assess what happens to output and emissions if a heat exchanger is running at reduced efficiency. Engineers can test debottlenecking scenarios, optimise operating set-points, and evaluate capital modification options -all without interrupting production or exposing the plant to risk.
Gartner had forecast that more than 50% of large industrial organisations would be deploying digital twins by 2026 -a projection that is now reaching fruition, with simulation twins playing a central role in how energy and manufacturing firms in Australia optimise operational decision-making.
Source: Gartner, Digital Twin Market Forecast, 2024 (forecast now confirmed, 2026)
3. The Operational (Live) Digital Twin
The Operational Digital Twin is the most advanced form. It connects live sensor and instrumentation data to a validated process model in real time, creating a continuously updated mirror of the plant’s actual operating state.
This is where predictive maintenance, real-time anomaly detection, and AI-driven optimisation become possible. According to McKinsey Global Institute, unplanned equipment downtime costs industrial manufacturers an estimated USD 50 billion per year globally -a figure that operational digital twins directly address by identifying developing faults weeks before they cause failures.
Source: McKinsey Global Institute, The Future of Manufacturing Operations, 2025
This type of twin requires the Provenance foundation and ideally some simulation capability already in place. Most Australian and GCC asset owners who build an operational twin do so as the third stage of a phased programme -not as their first step.
The Three Types of Digital Twins -Which One Does Your Plant Need?
Not all digital twins are the same. The right type for your plant depends on where you are starting from and what you need to achieve. There are three distinct types, and most successful digital plant programmes work through them in sequence.
How a Digital Twin Works -From Sensors to Insights
Understanding the technology does not require a background in data science. The basic principle is straightforward: data flows from the physical plant into a digital model, and that model generates actionable insights for your engineering team. Here is how that pipeline works in plain terms:
Field sensors measure temperature, pressure, flow rate, vibration, and dozens of other process variables. These signals travel through the plant’s instrumentation and control system -typically a Distributed Control System (DCS, such as Emerson DeltaV, Honeywell Experion, or Yokogawa CENTUM) or a Supervisory Control and Data Acquisition (SCADA) system.
An IIoT gateway (Industrial Internet of Things gateway) collects data from field devices -including legacy instruments retrofitted with smart transmitters -and transmits it securely to a data historian (such as OSIsoft PI or AVEVA PI System). The historian is the long-term memory of the plant, storing time-series process data continuously.
An analytics platform processes the historian data, running algorithms that detect patterns, identify anomalies, and generate predictive models. Common platforms in this space include Seeq, AspenTech Mtell, and custom solutions built on open-source frameworks.
The digital twin model sits at the centre -drawing from the analytics platform and the validated asset database to maintain an up-to-date virtual representation of the plant. Engineers interact with it through dashboards and visualisation interfaces.
One important misconception is worth addressing directly: a digital twin is not just a monitoring dashboard. A dashboard shows you what is happening right now. A digital twin tells you why it is happening, what is likely to happen next, and what your options are.
Data quality is the hidden critical factor in every digital twin project. Inaccurate P&IDs, uncalibrated instruments, and undocumented plant modifications will undermine your model from day one. As engineers say: garbage in, garbage out. Getting your as-built data right before you build the twin is not optional -it is the most important preparation step.
How These Technologies Relate to Each Other
Real-World Applications Across Energy, Oil & Gas, and Water
Digital twins are not a theory. They are being deployed right now across the industries Sarom Global serves -delivering measurable improvements in reliability, safety, and operating cost.
Oil & Gas and Refining
In refinery and upstream oil and gas operations, digital twins are applied to predictive maintenance of rotating equipment (compressors, pumps, turbines), simulation of crude feed composition changes, and remote function testing of Emergency Shutdown (ESD) valves without requiring personnel in hazardous areas. According to the International Association of Oil & Gas Producers (IOGP), digital integration of operational data can reduce well and facility downtime by 20–25%.
Source: IOGP, Digitalisation in Oil and Gas Operations, 2023
Power Generation
For power plant operators in Australia, digital twins provide real-time turbine performance optimisation, early detection of heat exchanger fouling before it triggers an unplanned shutdown, and load forecasting models that improve dispatch performance in the National Electricity Market (NEM). The Australian Energy Market Operator (AEMO) has identified real-time digital monitoring and predictive grid tools as a core enabler of Australia’s energy system transition in its Integrated System Plan.
Source: AEMO, Integrated System Plan 2024 -Technology and Digitalisation Enablers
Water Treatment and Distribution
Municipal and industrial water treatment plants use digital twins to optimise chemical dosing, monitor pump performance across large distribution networks, and integrate remote SCADA systems for regional facilities that cannot be continuously staffed. This is particularly relevant for Australian utilities managing water infrastructure across vast geographic distances -where undetected equipment deterioration can go unnoticed for weeks without digital monitoring in place.
Manufacturing and Processing
Process manufacturers -including food, chemical, and pharmaceutical plants -apply digital twins to batch process simulation, alarm rationalisation, and energy consumption benchmarking. According to the International Energy Agency (IEA), digital monitoring and control technologies can reduce energy consumption in industrial facilities by up to 10% -a meaningful contribution to both operating cost reduction and Scope 1 and 2 emissions targets.
Source: IEA, Digitalisation and Energy -Industrial Sector Report, 2024
What This Looks Like in Practice
A client in Australia’s LNG sector working with Sarom Global’s Owner’s Engineering team undertook a phased digital plant programme starting with a Provenance twin across two process trains. The initial data capture phase identified over 340 instrument records that had not been updated to reflect plant modifications made during the previous decade. Correcting this foundation before building the simulation layer prevented significant rework costs and meant the predictive maintenance model was working from accurate data from day one.
In the Middle East, Sarom Global has supported GCC-based asset owners at the assessment and scoping stage of operational digital twin projects, helping them navigate vendor selection and ensure their OT (Operational Technology) infrastructure was adequate before committing to full implementation.
Plant Digitalisation in Australia and the Asia Pacific -Why the Timing Matters
Plant digitalisation in Australia is not a future priority for most industrial operators -it is an active agenda item right now. Several forces are converging to make 2025 and 2026 the most significant years for digital plant investment in the region’s history.
The regulatory environment is shifting. Australia’s Security of Critical Infrastructure Act 2018 (significantly expanded in 2022) now extends risk management and incident reporting obligations to operational technology (OT) systems across the energy, water, and transport sectors. Complying with these obligations requires a level of asset visibility and system documentation that is very difficult to achieve without a validated digital plant foundation.
The asset base is aging. Many of Australia’s major industrial facilities -power stations, refineries, and water treatment plants -were built in the 1970s, 1980s, and 1990s. As these assets reach the end of their design life, owners need better data to make informed decisions about life extension, modification, or replacement. A digital twin provides exactly that visibility.
Across Southeast Asia, governments in Malaysia, Singapore, Vietnam, and Indonesia are actively driving Industry 4.0 adoption through national digital mandates and financial incentives. New manufacturing facilities in the region are increasingly being designed with digital plant capabilities built in from day one.
In the Middle East, Saudi Arabia and the UAE are investing billions in plant digitalisation through national energy company programmes aligned with Vision 2030. The GCC market for digital plant solutions is growing faster than any other region, with large-scale operational digital twin deployments under way across refining and production assets.
Digital Twin Readiness Checklist -Is Your Plant Ready?
Before committing to a digital twin project, asset owners should conduct an honest assessment of their plant’s current state. A digital twin built on weak foundations will deliver weak results -or fail entirely. Work through these eight questions and be honest with your answers:
- Instrument connectivity –Are more than 70% of your key process instruments connected to a historian or SCADA system with reliable, continuous data recording?
- As-built P&ID accuracy –Do your current P&IDs reflect the actual physical configuration of the plant today, including all modifications made since the original build?
- Defined use case –Can you articulate one specific, measurable operational problem this digital twin needs to solve? Vague objectives produce vague results.
- Management buy-in and budget –Has senior leadership formally committed budget and endorsed the project as a strategic priority?
- OT cybersecurity baseline –Has a basic operational technology (OT) security assessment been completed? Connecting plant systems to digital platforms expands the attack surface and requires a security plan from day one.
- Workforce capability –Does your team include -or have access to -engineers with skills in process control, instrumentation, and data analytics? If not, consider Sarom Global’s Plant Digitalisation Training.
- Systems integration readiness –Can your existing DCS, SCADA, and enterprise systems (ERP) expose data via standard protocols or APIs? Proprietary, locked-down legacy systems create significant integration barriers.
- Independent oversight plan –Is there a plan for independent Owner’s Engineering oversight throughout the project? Without it, the asset owner’s interests are often poorly represented when dealing with technology vendors and EPC contractors.
How Sarom Global Supports Plant Digitalisation Projects
Digital twin projects are technically complex, high-stakes investments. When they go wrong -and a significant number do -the most common cause is not a failure of technology. It is a failure of project governance: unclear objectives, poor data foundations, vendor lock-in, and no independent voice on the asset owner’s side of the table.
This is precisely the role Sarom Global plays. As an Owner’s Engineering firm, Sarom Global acts as an independent advocate for the asset owner throughout the entire digital twin project lifecycle -from initial readiness assessment through to system commissioning and handover. The objective is always to protect the client’s long-term interests, not the contractor’s delivery targets.
Sarom Global’s plant digitalisation services are built around three practical capabilities:
- scoping, vendor selection, technical oversight, and integration management for digital twin and digital plant projects across energy, oil and gas, manufacturing, and water infrastructure.Plant Digitalisation Consulting –
- Sarom Global’s proprietary system for improving production efficiency and reducing power consumption, which forms a natural bridge between operational data and digital optimisation outcomes.POSy-System Plant Optimisation –
- deep technical knowledge of the IIoT sensors, IO modules, DCS systems, and process control hardware that form the data foundation of every digital twin.Instrumentation and Control Systems expertise –
Sarom Global supports projects from its Sydney headquarters across the Asia Pacific, Middle East, and Americas -making it one of the few truly independent engineering consultancies capable of delivering end-to-end plant digitalisation support across all of the regions where digital investment is accelerating fastest.
Frequently Asked Questions
What is a digital twin in a process plant?
A digital twin in a process plant is a live virtual model of the physical facility that receives continuous data from field sensors and instruments. It mirrors the actual operating state of the plant in real time, allowing engineers to monitor performance, detect anomalies, simulate process changes, and predict equipment failures -without interrupting physical plant operations.
What are the three types of digital twins?
There are three main types of industrial digital twins. The Provenance (Descriptive) twin creates a validated digital record of all physical assets and configurations. The Simulation (Predictive) twin models how process changes will affect performance before they are made. The Operational (Live) twin connects real-time sensor data for continuous monitoring and predictive maintenance. Most successful programmes build through all three in sequence.
How much does a digital twin cost for an industrial plant?
Cost varies significantly by plant size, existing data infrastructure, integration complexity, and the type of twin being built. A Provenance twin for a mid-sized facility typically starts in the low six figures. An operational twin for a large refinery or power plant can run into the millions. A structured Digital Readiness Assessment is the most efficient way to establish what is realistic for your specific facility.
What is the difference between a digital twin and SCADA?
SCADA (Supervisory Control and Data Acquisition) monitors and controls plant operations in real time. A digital twin uses SCADA data as one input but goes further -it builds a validated model of the plant and simulates future behaviour under different operating conditions. SCADA tells you what is happening now. A digital twin tells you what is likely to happen next. Both technologies complement and coexist with each other.
Can a brownfield plant have a digital twin?
Yes -and the majority of digital twin projects in Australia and the Middle East are for brownfield facilities. The main challenges are incomplete historical asset records, legacy instruments not connected to a historian, and plant modifications that were never formally documented. The practical first step is a data gap assessment to identify what is missing before building the Provenance twin foundation.
How do I know if my plant is ready for digitalisation?
Work through the Readiness Checklist in this article. The key indicators that a plant is ready to proceed are: reliable continuous data capture from key instruments, reasonably accurate as-built P&IDs, and a clearly defined operational problem the digital twin will solve. If you answered No to three or more checklist items, start with a Digital Readiness Assessment before committing to a full implementation.
What is the POSy-System and how does it relate to plant digitalisation?
The POSy-System is Sarom Global’s proprietary plant optimisation platform. It analyses operational data from a facility’s existing instrumentation and control systems to identify efficiency losses and power consumption improvements. It functions as a practical, lower-barrier entry point into digital plant optimisation for asset owners who are not yet ready for a full operational digital twin -and it creates the data foundation that supports more advanced digitalisation later.
Does digital twin project cost differ between Australian and Middle East facilities?
Yes, the cost profile is different. Australian projects typically involve significant brownfield data capture work due to aging infrastructure and incomplete historical records. Middle East projects, particularly for large GCC energy companies, often involve more complex integration requirements across multiple existing digital systems. In both regions, the single biggest variable in project cost is the quality and completeness of the existing asset data. A readiness assessment is the best way to establish an accurate cost range.
The Bottom Line
Digital twins are not a distant technology vision. They are being implemented right now -in Australian power stations, Middle East refineries, Southeast Asian manufacturing plants, and water utilities across the Asia Pacific -delivering real reductions in downtime, maintenance cost, and energy consumption.
The asset owners who act now are not just solving today’s operational problems. They are building the digital foundation that will make their plants safer, more compliant, and more valuable for the next two decades.
The best time to start a digital twin project is during the planning phase of a modification or expansion -not after construction has begun. The second best time is now.