Measuring, estimating, and sampling methodologies for emissions quantification
Lord Kelvin has a famous phrase that reads “If you cannot measure it, you cannot improve it”. This phrase is very indicative of the behavior of systems given that what is not improved, eventuallydegrades, and the oil and gas production system is not the exception. In past years, abatement of climate change has focused heavily on managing emissions from the oil and gas sector. To effectively manage emissions in a hydrocarbon facility, first, you must know them, and to know them, you must measure them. Direct measurement provides the most accurate data. However, there are times when direct measurement of all equipment or facilities is not possible. In this one pager, we discuss alternatives to work around this issue.
Direct measurement results in more accurate data
Direct measurement makes it possible for companies to gather accurate information on the emissions present in each of their facilities. Technologies developed with this purpose in mind can help identify the point of emission to a component level in each piece of equipment. Knowing the exact origin of the emission, managers can make better decisions on how to control them. But, what happens when it isn’t feasible to directly measure every equipment or site within a facility? This situation is commonly found in natural gas transport and/or distribution systems, exploration, or extraction blocks with multiple wells, among others.
What to do when you cannot measure all equipment
At times, logistics, technical issues, safety issues, and costs, among other reasons, won’t allow direct measurement of every piece of equipment in the facility. The typical solution would be to switch to an estimation method through the application of generic emission factors. However, this is not recommended given that in most cases, the application of generic factors results in an overestimation of quantified emissions. Other times, these factors were established for height, temperature, humidity, and other conditions different from those present at the facility.
There are methodologies that allow us to optimize the data obtained from direct measurement, even if it’s not conducted in all of the equipment, and extrapolate it to achieve a more precise estimate. Methodologies like this, that combine direct and indirect measurement, provide us with facility-specific factors which can be applied with no reservations given that all equipmentoperates under the same conditions.
Below is the methodology we propose to optimize data obtained from direct measurement:
1. Conduct a preliminary estimate of emissions
In this step, we estimate emissions by groups of defined assets (compression stations, city gates, regulation stations, etc.). This estimate provides a general idea of which type of equipment would have the most emissions.
2. Select the sample of equipment that will be measured directly
Based on the data obtained from the previous step, we decide the group of assets in which we will conduct direct measurement. It is of utter importance in this step to use statistical sampling techniques that will allow us to draw inferences from a population, based on observations (measurements) from the selected sample.
The sample must have representativity so that we may extrapolate the data obtained in the field. Also, the size of the sample must consider the following:
3. Perform field work
Once the sample has been selected, we conduct direct measurement in the field with the technology of our choosing. In this step, data collection must be done homogenously.
4. Quantify emissions
The data we obtained through direct measurement is then used to assign a site-specific emission factor to all equipment left out of the sample, thus being able to quantify their emissions. If for any reason it is not viable to measure a sample of a specific type of equipment, we can then use generic emission factors to estimate emissions, combining both methodologies.
When it comes to emissions management, the more accurate the data on which we base our mitigation actions, the more effective these actions will be. That is why we give preference to directmeasurement. Understandably, direct measurement on the entirety of existing equipment may not always be feasible. By applying the proposed methodology, which makes use of on-site collected data togenerate site-specific emission factors, we will obtain estimates that are closer to the reality, and more accurate than through the use of generic emission factors which oftentimes do not reflect theparticular operative conditions of the facilities.
There is a wide array of techniques for selecting samples within a population. Some of the most common are:
- Simple Random Sampling: all elements of the population have the same probability of being selected.
- Stratified Random Sampling: when there are several groups or strata, and it is necessary to ensure that all of them are represented within the sample.
- Systematic Sampling: it begins with listing all of the elements in a population, then randomly selecting one, and continuing to select elements at systematic intervals.
- Conglomerate Sampling: when the elements that will become part of the sample are part of a collection of elements or analysis units.
- Other types of non probabilistic sampling are convenience sampling and snowball sampling.
At Talanza, we advise our clients on implementing the methodologies that will get them the best data for decision-making in the prevention and reduction of methane emissions.