Ensuring they are adhering to environmental regulations is a top priority for oil and gas organisations. Achieving these standards remains a challenge to the industry, however, as regulations evolve and factors such as carbon emissions trading become more sophisticated in the mission to maintain best practice and safeguard the environment.
As oil and gas operators in the United Kingdom Continental Shelf (UKCS) continue to seek efficiency savings across their operations, the opportunities for innovative thinking, the development of new technologies and critically reviewing existing processes have never been better.
One area of the offshore process which offers financial and operational efficiency savings is the management of atmospheric emissions, namely carbon dioxide (CO2), in the European Union Emissions Trading Scheme (EU ETS). The scheme introduced a cap and trade system for industrial installations over 20MW/th, with the aim of a 21% reduction in CO2 emissions by 2020, as compared to 2005 levels.
Installations falling within the scheme are allocated a set number of CO2 tonnes, or credits, that they may discharge to the environment through combustion. If annually verified data confirms that this allocation has not been met, i.e., CO2 arising from combustion sources does not amount to the approved limit, unused credits can be sold to other operators or licensees through the European Registry. Conversely, if an installation has exceeded its annual allowance, the operator must purchase additional credits.
Although the legislation has been in place since 2005, in 2013 a step change progression to phase 3 was introduced. In the latest phase, the annual cap was decreased by 1.74% each year, putting additional pressure on operators to enforce tighter tolerance levels on the systems and processes they have in place to manage the emissions and data flow.
This and other new requirements within phase 3 raised the profile of the EU ETS from its original place as environmental legislation to a significant financial consideration for operators’ budget planning and in the forecasting of new developments.
The compliance cycle for the EU ETS includes monitoring, reporting, verification of emissions by a third party and the acceptance of the data by a competent authority. For the offshore industry, this is the Department of Energy and Climate Change (DECC). Monitoring processes need to be robust as the financial implications can be significant.
Following guidance from the DECC, operators must develop detailed procedures around data capture, typically for fuel gas, flare gas, diesel and data management. As with each engineering process, at every stage of the cycle there are elements of uncertainty. This is managed through the tiering system. Tiers, or levels of uncertainty to be achieved, are allocated to activity data capture for the fuels, that is volume or mass of gas or diesel, and in the calculation of emissions factors (EF), net calorific value (NCV) and oxidation factor. The European guidance document, The Monitoring and Reporting Regulation-General guidance for installations (MRR) of July 2012 states “the overarching rule is that the operator should apply the highest tier defined for each parameter”. This undoubtedly creates the highest level of accuracy and therefore the lowest degree of uncertainty. However, DECC guidance developed for the UKCS suggests indicative tier levels to be achieved by the offshore industry. These do not necessarily align with the MRR guidance, but failure to achieve the DECC tiers must be supported by a cost benefit analysis.
Two of the most significant sources of uncertainty, and therefore opportunities for improvement within the process, come from metering and calibration and gas composition analysis.
Uncertainties in measurement can be derived from various input factors, including the measurement device itself, the physical properties of the medium being measured, actual process conditions or from other sources of inputs to the measurement. These uncertainties can be calculated using known information about the process or by running intricate simulations on the measurement data. Time-proven calculations are used to establish the overall uncertainty for the systems which are required as part of the EU ETS regulations.
Various methods exist to reduce the uncertainty of a flow metering system and vary from traceable calibrations, adhering to proven calculations, good record keeping, planned maintenance and installing equipment to a recognised standard. It is only when the uncertainty of a measurement system is calculated that it can be concluded that the system is fit for purpose and meets the requirements of the EU ETS.
As emissions levels are prescribed in the greenhouse gas permit and significant costs are involved, there is a requirement to ensure that the measuring systems used are accurate, reliable and meet the specified tier of uncertainty stated in the DECC guidance. This then allows the third-party verifier to be confident in the evidence supporting annual submission data and sure that the system meets the approved monitoring plan and its permissible uncertainties.
For activity data of gaseous and liquid fuels, the highest tier achievable is tier 4. This requires a +/- uncertainty of 1.5%. DECC guidance permits an operator to work at tier 3, however, which reduces the uncertainty tolerance to +/- 2.5%. Again, this can only be confirmed by regular and thorough calibration and maintenance.
It is the confirmed uncertainty of the system that ensures the reported values for emissions meet the required EU specifications. Once the uncertainty for the system has been determined, the verified emissions are then reported at the agreed frequency as n ± x% where n is the verified quantity and x% is the calculated uncertainty of n.
In the EU ETS, only one value is given for the emissions in the annual emissions report and in the verified emissions table of the registry. The operator cannot surrender “n ± x%” allowances, but only the precise value of n. It is therefore essential to quantify and reduce the uncertainty “x” as far as is reasonably practical. This system is used in metering uncertainty for fiscal meters, again where significant financial implications arise from only minimal changes to uncertainty.
Where improvements to the system are identified in order to meet the required uncertainty, the financial implications of the improvements also need to be investigated. It is not always viable for the operator to implement the required improvements to meet the tier from a financial or operational standpoint. Where an operator is required to produce a cost-based analysis for the proposed improvements, the final calculated costs can be used to justify whether the required improvements are feasible. Such justification can result in a system being granted approval to operate with a higher level of uncertainty in order to comply with the EU ETS requirements.
One way to verify metering data is through offshore surveys and audits. In this case study, simulation software predicted the production throughput on an offshore installation to be 8,000 sm 3 / hr at the plant metering streams. The uncertainty was stated by the client to be at 2-5% for non-fiscal equipment and <1% for export. Real-time data showed the actual throughput to be around 6,800 sm 3 /hr, a 15% reduction on the predicted volume. Audit results estimated that the actual plant uncertainty was >14% and the export uncertainty was >2.5%. The audit concluded that a general lack of plant maintenance, wrongly specified equipment, out-of-date operational procedures and inadequately trained personnel were the causes of the high level of uncertainty.
Maintenance, strategies and operational routines were assessed and amended. This increased the reported throughput to about 7,600 sm 3 /hr. Flow meters and system parameters were inspected and anomalies recorded. These were corrected and the uncertainty decreased to ≤1% for installed meters. Along with corrected system parameters, the reported plant throughput was now circa 8,150 sm 3 /hr and the expected uncertainty for <1%. By reducing the uncertainty, the client was able to make savings equating to over €400,000 (£286,720). This highlights the need to incorporate the requirements of the EU ETS into existing fiscal metering regimes and apply best practice to achieve improved levels of performance, plant and cost efficiency.
Gas composition data is the other area where considerable improvements in data accuracy can be made without incurring prohibitive costs. By revising their sampling and analysis routine, operators may report significantly reduced levels of emissions in their annual submissions to the DECC.
It is often assumed that the composition of flare and fuel gas on an offshore installation is not significantly different. However, experience suggests that even with samples taken on the same day, the results show significant variance which directly impacts on the reported tonnes of CO2 emitted through the combustion of the two gas streams. A review of samples taken over four years from seven installations in the UKCS highlighted the potential need for separate sampling of fuel and flare gas. For each of the 67 samples, the theoretical amount of CO2 produced per tonne of gas combusted was calculated and the variance between the two samples calculated, as illustrated in figure 1. Although on average the amount of CO2 produced by fuel gas combustion was close to that produced by flare gas combustion, there was no consistent relationship between the two and different installations had different ratios.
There was up to 8.4% variance in the calculated tonnes of CO2 emitted from one tonne of combusted fuel, which in turn represents an 8.4% variance in the reported emissions. Using the example of an average offshore installation emitting 150,000 tonnes of CO2 in a year, this represents 12,600 tonnes of the total emissions. Based on the indicative pricing of €20 per tonne, this equates to an annual adjustment and therefore potential saving of €252,000 per year.
However, when looking at molecular weight, the figure typically used in the mass-to-volume conversions, a far higher degree of variability was found. This variance between fuel and flare gas molecular weight was up to 50.2%. This introduced a significant level of uncertainty to the overall reported value.
The second element around improving uncertainty in gas compositional data is to increase the frequency of gas sampling. The current requirement is for quarterly samples to be taken and analysed. Any emissions over a three month period, therefore, are calculated based on the EF and NCV of one sample. From a full annual data set, the emissions based on quarterly and monthly sampling were compared. Assuming an average 100 tonnes of fuel gas was combusted each day, there was a difference of 1,995 tonnes of CO2 between the two data sets. This represents a cost of €39,900.
The required sampling frequency will change depending on which measurement tier is applied. From an indicative data set of eight samples from one installation in the UKCS, there was a variance of 3.815% in the calculated emissions from one tonne of CO2 based on the compositional data. With this data and sampling frequency, the operator could only achieve tier 2 for NCV and emissions factor, one tier below the tier 3 expectation.
In order to achieve tier 3 for NCV based on this data, a total of 17 samples must be taken on an annual basis. To progress to the highest tier, this would increase to 46 samples per year. While this would of course incur additional costs, it provides a robust base case for any cost benefit analysis, as comparisons can be directly made between sampling costs and potential savings from more accurate compositional data.
Offshore oil and gas operators are vigilant in data acquisition and management for fiscal reporting. By applying the same systems and processes to the EU ETS, they could make significant cost savings on an annual basis. Operators must start to consider the financial implications of the trading scheme alongside the environmental impacts of CO2 on the receiving environment. The costs can be calculated through existing data sources with the benefit of reducing the uncertainty of measurement to the lowest level possible to achieve greater accuracy and lower running costs.
Published: 27th May 2015 in AWE International