Story Map

The Leak Detection and Repair Simulator (LDAR-Sim) is a virtual world that replicates methane emissions from the oil and gas industry. Highwood’s President, Thomas Fox, is the lead inventor of LDAR-Sim alongside colleagues from the University of Calgary. LDAR-Sim is an open-source tool that can be applied to many diverse problems, with applications for industry, regulators, innovators, researchers, and others. At Highwood, we leverage the power of LDAR-Sim for many of our projects. We also contribute regularly to the open-source LDAR-Sim framework as part of our commitment to innovation. To learn more about LDAR-Sim, explore the educational LDAR-Sim Story Map (below) or read the original LDAR-Sim journal article.

To learn how LDAR-Sim can be used to help achieve your emissions management or innovation goals, reach us through our Contact Page or by email: [email protected]


Evaluating Methane Mitigation Technologies, Programs, and Policies

Appraising Innovation

One challenge, many solutions

Over the past decade, many new technologies have arisen to measure and reduce methane emissions. Choosing from this growing diversity of solutions amid a rapidly evolving innovation landscape can be intimidating.

A 2019 review published by the University of Calgary’s Centre for Smart Emissions Sensing Technologies examined many of these emerging technologies in the context of methane Leak Detection and Repair (LDAR). Images (right) adapted from this paper show how varied the information produced by different technologies can be.

In recent years, governments and oil and gas (O&G) companies have signaled interest in new solutions. However, most solutions remain poorly understood. To ensure that mitigation targets are met, regulators often require evidence that new solutions perform at least as well as legacy methods. This concept is known as ’emissions reduction equivalence‘ and is the basis of many regulatory frameworks across Canada and the United States.

But how can equivalence be demonstrated among such different solutions? Over 100 regulators, O&G producers, technology developers, and academics came together during three workshops in Canada and the USA to answer this question. A peer-reviewed framework developed by consensus during these conversations recommends the use of simulation modeling to demonstrate equivalence:

Simulation modeling is a fast and cost-effective way to evaluate and explore a range of LDAR program configurations, forecast performance over long periods, and develop programs with cost- or mitigation-optimized technology deployment.


Enter LDAR-Sim

What is LDAR-Sim?

The LDAR Simulator (LDAR-Sim) is an agent-based numerical modeling framework that enables the precise definition and evaluation of a broad range of possible LDAR programs. LDAR programs may consist of multiple different technologies, work practices, survey frequencies, and repair practices that can be simultaneously deployed at the jurisdictional, basin, or individual facility scales.

Built to evaluate very specific LDAR configurations, LDAR-Sim can integrate individual facility characteristics, environmental conditions like rain and wind, geospatial data like roads and airports, and different source categories like legal venting, combustion, and fugitive emissions.

LDAR-Sim development began at the University of Calgary in 2017. It was released to the public in July 2020 (click here for the article describing LDAR-Sim and here for the code).

What can LDAR-Sim do?

LDAR-Sim can be used in different ways by O&G companies, regulators, technology developers, investors, and researchers. Here are some examples of questions it can help to answer:


  • What configuration of available LDAR technologies could be implemented to maximize emissions reductions, achieve compliance, and minimize costs?
  • How might different technologies be deployed to reflect regional differences in regulations, environment, and asset base?


  • What is the potential deployment niche for my solution? Where, when, and how might it be most effective, given environmental conditions, facility densities, infrastructure, and current regulations?
  • How do I negotiate tradeoffs among detection sensitivity, cost, and deployment mode to be competitive in the LDAR space?
  • How does my technology compare to other technologies?


  • Is a proposed LDAR program with one or more new technologies able to achieve equivalent emissions reductions compared to an approved program?
  • Are current or proposed policies likely to be effective at achieving methane mitigation targets?
  • What research activities and initiatives are required to increase confidence in model outputs?
  • How might different technologies contribute to compliance efforts?


  • Are candidate technologies competitive and do they have potential to achieve performance and cost claims?

What is LDAR-Sim not able to do?

LDAR-Sim is a heuristic tool that can provide guidance but not proof. All models are imperfect representations of the real world. LDAR-Sim should be used as one tool in a broader decision-making strategy that may also include expert advice, empirical data, or other types of information.

There are many applications for which LDAR-Sim should not be used. If you are unsure, please reach out with any questions (contact details below).

How does LDAR-Sim work?

In LDAR-Sim, one or more technologies may be deployed simultaneously as part of an LDAR program. For example, a program may include an aircraft company, a truck-based company, and an optical gas imaging (OGI) camera company. Each company is able to manage one or more inspection crews (agents) that travel among facilities to detect methane emissions.

Companies and their crews can intelligently choose where to conduct LDAR, and can even work together. For example, a program may consist of a truck crew and an aircraft crew that conduct rapid facility-scale screening surveys and dispatch follow-up OGI inspectors to only the highest-emitting facilities. Emissions can be detected and/or quantified at multiple spatial scales, including entire facility (e.g. satellite) and individual component (e.g. OGI camera).

Crews may be constrained by environmental conditions (e.g. wind, rain, daylight), work hours, regulations, and access (e.g. roads and airports), among other factors. Sometimes crews are unable to visit a facility because of the weather, and must come back another day. In other cases, high winds, snow, or cloud cover may impact detection probabilities and lead to more leaks being missed during inspections.

Real facilities are input with geographic coordinates and facility characteristics to establish local environmental context, topology, and regulations. LDAR-Sim can use a single facility, all of a company’s assets, or all facilities in a basin or jurisdiction.

Custom survey frequencies can be assigned to each facility for each company used in a program. For simple programs, survey frequencies can be uniform (e.g. all facilities receive 2 aircraft and 1 OGI survey). For more sophisticated LDAR programs, survey frequency can depend on facility type or risk factors such as production, age, or historical LDAR data. The time required to survey each individual facility can also be specified by company or even by crew.

Simulations are generally run over many years. Each day, leaks arise stochastically and inspection crew may be deployed (if required by the program and if conditions are favourable). When leaks are found, they may be repaired immediately or tagged for repair at a later date. Over time, emissions can be tracked to help understand the effectiveness of a given LDAR program or policy.

For full details on how LDAR-Sim works, please see Fox et al 2020 [link].

Tailor-Made Programs

Managing your assets

LDAR-Sim allows users to test a range of LDAR programs on a specific set of assets. Knowledge of facility location, type, size, regulatory requirements, and historical emissions, among other variables, is crucial for developing cost-effective programs. Using custom user inputs can dramatically improve program design, leading to cost-optimized tailor-made solutions.

Mapping your results

This map of Alberta provides an example of how LDAR-Sim can manage, simulate, and display facility-specific emissions data. The map is interactive – go ahead and explore! At first, the heat map shows where most of the emissions are originating. As you zoom in, individual facilities can be seen, with larger red diamonds representing larger emission rates. Click on any facility to learn about it. If you zoom in to a facility, individual leaks will appear. Click on them to learn more.

To preserve anonymity, the facilities presented here are assigned random locations and will not correspond to the satellite imagery.

Comparing Programs

Demonstrating equivalence

To get new technologies and programs approved, many regulators and producers want to see evidence that new ‘alternative’ programs achieve equivalent emissions reductions to existing ‘regulatory’ programs. LDAR-Sim was built for this purpose.

The figure on the right, adapted from Fox et al 2020 [link], shows how multiple different programs can be compared in LDAR-Sim over many years. These data show that programs must be compared over long time periods, because emissions and performance can change seasonally with the environment or as dictated by different survey frequencies and modeling assumptions. This research shows that even OGI-based programs that are quite similar can result in very different mitigation outcomes.

Minimizing costs

In addition to estimating emissions, LDAR-Sim can provide estimates of total program costs, including deployment costs for different companies and repair costs. Deployment cost estimates can become quite sophisticated, but are generally dictated by a daily fee charged by each service provider multiplied by the number of days it takes each company to perform required inspections. Travel times, labour availability, environmental constraints, and other factors are all implicitly accounted for in this costing method.

Technology Assessment

Technology innovators can use LDAR-Sim to evaluate where and when their solution is likely to work. Each time LDAR-Sim is run, maps like these are automatically generated to inform users of where each technology thrives and struggles.

These maps show how two technologies differ in their environmental suitability for a set of assets in Alberta, Canada. On the left, Technology 1 is sensitive to cold weather, and therefore performs worse in the mountains, but relatively well where the O&G assets are located.

On the right, Technology 2 is primarily sensitive to wind. Southern Alberta tends to be very windy, and the map shows that deployment suitability declines in this region. Fortunately, the O&G assets are mostly located outside of the windiest regions.

This is a simple example of what could be a much more complex exercise that incorporates multiple technology constraints such as temperature, wind, precipitation, snow cover, cloud cover, topography, airport access, and so on.

Building a community

LDAR-Sim is a modeling framework that is open, free, and continuously evolving. We encourage scientists, regulators, O&G companies, innovators, and solution providers to contribute to the continued development and evolution of LDAR-Sim. If you are motivated to discover the best ways to deploy methane mitigation technologies, please join us in our efforts!

More information

To learn more about how LDAR-Sim works


LDAR-Sim development began in 2017 at the University of Calgary Centre for Smart Emissions Sensing Technologies (2017-2020). The lead inventor is Thomas Fox, originally at the University of Calgary and now at Highwood Emissions Management Inc. The core LDAR-Sim team at the University of Calgary is Mozhou Gao, Thomas Barchyn, and Chris Hugenholtz.

Contact information

For professional services and general inquiries, please contact Thomas Fox at Highwood Emissions Management ([email protected]).

For research inquiries, please contact Chris Hugenholtz at the University of Calgary ([email protected]).