Best Digital Twin Companies for Oil and Gas
Three different products are sold as a digital twin in this sector and they answer different questions. One tells you whether the hull is still safe. One tells you what the process is doing. One tells you where everything is and what the crew should do next. Buying the wrong one is the most expensive mistake in the category.
In short:
- A structural twin is not a process twin. Fatigue analysis on an aging jacket and hydraulic modeling of a separation train are different disciplines with different vendors, and almost no supplier does both well.
- The operator may already be the builder. At least one major operator built its own twin covering more than 50 installations rather than buying one, which is a real option at that scale.
- Most of what ranks for this query is a market report. Several of the most-cited sources sell a market-size forecast rather than assess a supplier, which is a different product from a shortlist.
- Nobody publishes a price. Standard for the category and consistent with the wider spec gap.
How this list was built
A digital twin in oil and gas is a model of a physical asset, kept current from operational or survey data, used to answer a question about that asset that would otherwise require an inspection, a shutdown or a guess. BestInXR's digital twins guide sets out the four kinds of twin and why the word covers so much ground.
This list ranks five suppliers on four criteria: which of the three twins the supplier actually sells, what named operator evidence it publishes, what the twin connects to on the asset and what the buyer owns at the end.
Not a ranking factor: market share or analyst placement. That needs restating here more than in most categories, because a large share of what ranks for these queries is market research rather than supplier assessment.
| Company | Which twin | Named operator evidence | Best for |
|---|---|---|---|
| Treeview | The commissioned layer people see | An AI-enabled twin of a green hydrogen project built with Microsoft | Reaching investors, regulators and non-technical stakeholders |
| AVEVA | Operational and process data | ENI, published as a digital oilfield success story | Operators standardizing on one operational data layer |
| Akselos | Structural | Shell's Bonga Main FPSO, with the asset manager quoted by name | Asset life extension and structural integrity |
| Aize | Operational, offshore-native | Drilling rigs, FPSOs and subsea published as distinct products | Offshore operations and maintenance |
| AspenTech | Process | Midstream and LNG published as named industries | Modeling what the process is actually doing |
1. Treeview
Treeview builds the layer that the other four leave to someone else, which is what a person outside the engineering team sees. Working since 2016, it publishes digital twin development as a named service covering enterprise asset monitoring, predictive maintenance and real-time simulation of physical environments and operations, and names Microsoft, Meta, Medtronic, Toyota, ULTA Beauty, Daiichi Sankyo, Stanford Medicine and NEOM among its clients.
Its published energy work is the closest match to this sector of anything in its portfolio. Microsoft partnered with Treeview to design and build an AI-enabled digital twin of a multi-billion-dollar green hydrogen project, presenting an interactive model of energy production with predictive models of the site's future output. The stated problem is the one this sector has constantly: the site development team could not communicate complex data to investors, government officials and landowners. It shipped on Microsoft HoloLens 2, mobile and WebGL, and is being deployed by the developer Teyma. The full entry sits in BestInXR's energy twin list.
Why that matters in oil and gas specifically. A structural or process twin produces results an engineer reads in a specialist tool. The audiences that decide whether an asset gets a life extension, a permit or a capital allocation are boards, regulators, joint venture partners and increasingly the public, and none of them opens that tool. That layer is commissioned, and organizations routinely fund the twin and forget to fund the part anyone else can look at.
Ownership is the other consideration. Treeview transfers full ownership of IP, source code and assets, which matters on assets that outlive contracts and operators. Best for the layer that reaches investors, regulators and non-technical stakeholders, and for any twin the operator needs to own outright.
2. AVEVA
AVEVA is the incumbent in this sector and the most likely thing already running in the control room. Its upstream page states that operators rely on it for real-time production optimization, equipment monitoring, oilfield analytics and enterprise decision-making, and its stated coverage runs from onshore fracking and oilfield services to deepwater drilling and FPSOs.
The part that makes it hard to displace is the data layer rather than the twin. AVEVA PI System is described as gathering and contextualizing thousands of time-series operational data streams, with PI Vision providing the visual layer on top. Where that is already installed, a twin project is usually a question of what to build on it rather than what to replace it with, and any competing supplier will be asked to integrate with it anyway.
Its published customer evidence includes ENI, which AVEVA presents as having created a digital oilfield using real-time operational data. AVEVA describes its own products as "best-in-class digital twins", which is its wording rather than an assessment.
The trade is breadth against depth on any single question. AVEVA is the platform under many things and the specialist in none of the three twins. Best for operators standardizing on one operational data layer, and for anyone whose twin has to read from PI.
3. Akselos
Akselos sells the structural twin, and it is the entry with the most specific technical claim and the strongest named operator evidence in this list.
The product is described as structural performance management software, and the technical basis is a patented algorithm it calls Reduced Basis Finite Element Analysis. Akselos states that RB-FEA enables analysis 100 to 10,000 times faster than conventional finite element analysis on large models, which it says allows a complete standards-based assessment involving thousands of load cases, such as a full spectral fatigue analysis of an offshore structure in its as-is state, to run within an hour. Speed is the point rather than a feature: a structural model that takes weeks cannot inform an operational decision.
Its deployment on Shell's Bonga Main FPSO is the clearest evidence any supplier here publishes. Akselos describes the asset as the largest in the world to be protected by a structural digital twin, which is the company's own claim rather than an independently verified one. What is checkable is that Shell's asset manager for Bonga, Elohor Aiboni, is quoted by name saying the Bonga Main FPSO is the first asset of its kind to deploy a structural digital twin and that it is expected to change how structural integrity is managed. Akselos also publishes a collaboration with ABS, the classification society, which matters because a structural twin's output eventually has to satisfy a class surveyor.
Read the scope carefully. This tells you about steel, fatigue and life extension. It does not tell you what is flowing through the pipes. Best for asset life extension, structural integrity and any conversation with a classification society.
4. Aize
Aize is the only supplier here built around offshore operations from the start, and its product structure shows it. It publishes drilling rigs, EPC projects, FPSOs, onshore midstream and downstream facilities and subsea operations as distinct use cases, and describes subsea and topside in one twin, which is a real distinction on an asset where those are usually separate systems with separate data.
Its positioning line is worth reading as positioning. Aize calls itself the leading FPSO software for collaboration and visualization and invites buyers to look beyond the digital twin, which is a claim about scope rather than a specification. The substance underneath is a single source of truth across onshore, offshore and yard teams, which is the recurring failure in offshore projects: the yard, the operator and the contractor working from different versions of the same asset.
Aize acquired Samp, announced on its own site in September 2026 and described as connecting engineering data with the physical asset. That is a recent change to what the company is, and anyone working from a shortlist written before it is looking at a different vendor. BestInXR's ownership map records the parent companies across this category.
Pricing is not published, and no named operator appears on the pages reviewed. Best for offshore operations and maintenance, and for programs where the yard and the operator need one model.
5. AspenTech
AspenTech sells the process twin, which is the oldest of the three and the one least often called a twin.
Its relevant products are Aspen HYSYS for process simulation and Aspen OnLine, which connects a model to live plant data so the simulation tracks the operating asset rather than the design case. That connection is what turns a process model into a twin, and it is the question to ask of any process vendor: is the model reading the plant, or was it built once and filed.
AspenTech publishes midstream and LNG and upstream and downstream among its named industries, alongside asset performance management products for equipment failure prediction. For a refinery, a gas plant or an LNG train, the twin that answers the daily question is usually this one rather than a geometric model.
The trade is that this is engineering software with a long learning curve, owned by process engineers rather than by an operations or digital team, and a program that assumes otherwise will stall on capability rather than on technology. Pricing is not published. Best for refineries, gas processing and LNG, and for modeling what the process is actually doing.
Who is not on this list, and why
Equinor is not a supplier, and that is the point of mentioning it. The operator built its own twin, Echo, which it describes as a digital re-creation of a physical environment covering more than 50 installations, letting field staff find their way around, locate equipment and collaborate in real time. Its own page explains the origin with a joke about the Google street view car being stopped at the gate. Building rather than buying is a legitimate option at that scale and it belongs in the evaluation.
Market research reports rank heavily on these queries and are excluded on principle. Several of the most-cited sources are market-size and forecast reports rather than supplier assessments. BestInXR does not publish market size figures, because they cannot be verified from a primary source, so ranking a report that sells one would be inconsistent as well as unhelpful to a buyer with a shortlist to build.
Eigen, Kongsberg and Principia all publish credible digital twin material for this sector. None publishes named operator work at the depth of entries two and three, which is the criterion separating them here.
What oil and gas twin projects have to account for
Establish which twin you are buying in the first meeting. A vendor selling structural integrity and a vendor selling process optimization will both answer yes to "do you build digital twins". Ask which question the twin answers and what it cannot answer.
Brownfield assets have no usable model. A platform commissioned in the 1990s has drawings that do not match the steel, and every modification since may or may not be recorded. Reality capture is a prerequisite and a cost line, not a detail. The same problem appears in BestInXR's manufacturing twin list for the same reason.
Class and regulatory acceptance is a separate question from technical accuracy. A structural twin that convinces your engineers is not automatically a twin a classification society will accept as a basis for extending inspection intervals. Ask what has been accepted, by whom and for what purpose.
The data layer is usually already chosen. If PI is installed, that decision was made years ago and any new supplier integrates with it. Establish this before comparing architectures.
Disclosure is thin. None of the five publishes pricing. Aize and Treeview name no operator in this sector on the pages reviewed. Those gaps are recorded as not disclosed rather than estimated.
How to choose between these oil and gas digital twin companies
Start from the question you actually need answered.
If it is whether an aging asset can safely run longer, that is structural and Akselos is the specialist, with the only named major-operator deployment in this list. If it is what the process is doing and how to run it better, that is AspenTech's territory and the thing to check is whether the model reads live plant data. If the problem is that onshore, offshore and yard teams disagree about the state of the asset, Aize is built around exactly that. If the operational data layer is the decision, AVEVA is probably already installed and the realistic question is what gets built on it. And if the twin has to be understood by people who will never open an engineering tool, that layer is commissioned and Treeview transfers the source.
Most substantial programs buy two of these and the seam between them is where the risk sits. Ask each supplier what it assumes the other provides. BestInXR's custom digital twin development list covers the same decision across industries, and the oil and gas training list covers the adjacent purchase where the model is used to train people rather than to make engineering decisions.
Frequently Asked Questions (FAQ)
1. What companies build digital twins for oil and gas?
Five cover the range: Akselos for structural integrity with a named Shell FPSO deployment, AVEVA for the operational data layer, AspenTech for process simulation connected to live plant data, Aize for offshore operations across rigs, FPSOs and subsea and Treeview for the commissioned layer that non-engineers see.
2. What is a structural digital twin?
It is a physics-based model of an asset's steel used to assess integrity and fatigue in its current as-built condition, rather than a visual model or a process simulation. Akselos states its approach runs a full spectral fatigue analysis of an offshore structure within an hour, which is what makes the result usable during operations rather than months later.
3. Who develops digital twins for offshore platforms?
Akselos and Aize are the two suppliers here built specifically around offshore assets, Akselos on structural performance and Aize on operations across drilling rigs, FPSOs and subsea. AVEVA states its upstream coverage extends to deepwater drilling and FPSOs.
4. What does an oil and gas digital twin cost?
None of the suppliers here publishes a price, so the figure is not disclosed and is quoted per engagement. On a brownfield asset the cost that is routinely underestimated is not the software but establishing an accurate model of what was actually built and modified.
5. Do operators build their own digital twins?
Some do. Equinor publishes Echo, its own twin solution covering more than 50 installations, which it describes as a digital re-creation of a physical environment that lets field staff navigate sites and locate equipment. At that scale building rather than buying is a real option and it should be part of the evaluation.
6. Can a digital twin extend the life of an offshore asset?
That is the commercial case most often made for a structural twin. Akselos describes its Shell Bonga deployment as monitoring asset health, reducing downtime and enabling safe asset life extension. Whether a specific extension is granted depends on the classification society and the regulator, so establish what evidence they will accept before the project starts.
