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The increased scrutiny on greenhouse-gas (GHG) emissions from regulators and investors has led producers to seek better ways to effectively manage methane and reduce emissions from oil and gas operations. Methane regulations have become more stringent in the wake of widespread focus on GHGs and their impact on the environment and climate.

Methane is considered to be 84 times more potent as a GHG than CO2 over a 20-year period and accounts for approximately 20% of global emissions.

New Canadian regulations have been introduced that require leak detection and repair (LDAR) surveys to be conducted at facilities one to three times per year with requirements to repair and verify fixes when methane leaks are detected.

In the U.S., regulations vary by regulatory jurisdiction but can require LDAR surveys to be completed up to 12 times per year (Understanding LDAR Requirements for Oil and Gas Operators). These regulations are prescriptive, vary in requirements and frequency, and can be ineffective at minimizing fugitive emissions due to the infrequent “snapshot” images that they create of a facility. Traditional technologies used to perform LDAR surveys are intermittent, inaccurate and expensive, and they can lead to undetected leaks for months or years before repairs are conducted and verified. In addition, snapshot measurements are incapable of providing the continuous data required to quantify emissions and enable oil and gas producers to highlight their improved emissions performance to investors.

Gathering emissions data

Qube Technologies
Qube uses artificial intelligence and machine learning to detect, locate, quantify, and classify emissions. (Source: Qube Technologies)

Qube Technologies has developed a technology to address these shortfalls and provides producers with a solution that reduces GHG emissions and enables rapid LDAR due to the continuous monitoring capabilities of Qube’s system. Qube uses an Industrial Internet of Things device to continuously monitor environmental conditions such as wind speed, wind direction, temperature, pressure and humidity, and it uses gas sensors to measure up to five gases from a facility. All captured data are sent to a cloud-based platform where it is analyzed using artificial intelligence and machine learning to infer site-level insights and detect, locate, quantify and classify emissions in real time. Emissions data are displayed on a web-based dashboard to allow operators to understand their site-level emissions and provide a reporting base to meet regulatory requirements and provide accurate and transparent data for ESG reporting.

Regulators allow alternative technologies to be used for methane emissions reduction and management; however, the technologies must show equivalence in emissions reduction when compared to traditional methods.

Case studies

Qube has successfully demonstrated technology equivalence with the Alberta Energy Regulator for a pilot program using Qube’s technology as an approved Alternative Fugitive Emissions Management Program (Alt-FEMP Regulatory Approval). As part of the approved application, Qube, in conjunction with Highwood Emissions, used a LDAR Simulator (LDAR-Sim) to create a virtual replication of oil and gas emissions from facilities and demonstrate the value of continuous monitoring to achieve emissions reductions compared to a traditional LDAR survey program conducted one to three times per year, depending on the regulatory requirements of each facility.   

The LDAR-Sim models showed emissions reduction estimates under different deployment timing scenarios for Qube devices and compared these against a baseline program with no LDAR, and against a traditional OGI survey program based on the prescribed regulatory frequency of surveys, either annually or tri-annually, for a period of two years. The facilities that were included in this pilot used prior data from the previous year’s LDAR surveys and then received an estimate leak duration where: (i) the leak emitted continuously for one year (365 days) under the baseline condition; (ii) the leak emitted for either 75 or 195 days, depending on the facility at which the leak was found under the standard OGI survey condition, and (iii) the leak emitted for 35 or 68 days, regardless of the facility at which it was found, under the Qube deployment condition with continuous monitoring.

Results from the standard program suggest that emissions gradually increase over time until OGI is performed, at which point, emissions drop back to near-zero, shown in the blue curves in Figure 1. In contrast, emissions under the Qube alternative program remain low because leaks are quickly resolved instead of building up between surveys, shown in the gold curves.

A traditional OGI program is estimated to reduce emissions relative to a baseline case by approximately 50%, whereas the reduction estimates obtained by Qube’s devices are estimated to be 60% to 86% greater than the baseline case, depending on the deployment timing. 

FIGURE 1. Emissions between regulatory LDAR and the proposed Qube Alt-FEMP are compared, assuming different launch dates. The blue line represents the standard OGI-based reference program, while the gold line represents the alternative program. (Source: Highwood Emissions LDAR-Sim).

Qube will be comparing the actual results of this case study and pilot program once all devices for this approved Alt-FEMP have been deployed in 2021 and expects to demonstrate emissions reduction improvements relative to a traditional OGI program. 

The average annual mitigation ratio, or cost per tonne of CO2e per site for this pilot program, was evaluated through LDAR-Sim (Figure 2).

FIGURE 2. The cost of mitigation per year from Qube’s alternate program and traditional OGI program in Alberta, Canada, are compared. (Source: Highwood Emissions LDAR-Sim)

Qube continues to demonstrate success in the detection, localization, quantification and classification of fugitive gas emissions, and its technology will enable operators to cost effectively reduce emissions for regulatory requirements and achieve equivalence to traditional LDAR methods.