Cost saving metering station maintenance for allocation systems
Metering station maintenance, including calibration and sampling procedures, represent a significant operational cost. With the increased focus on cost-savings, intelligent maintenance strategies are more important than ever. Potential savings associated with a maintenance strategy need to be weighed against its consequences. In allocation systems, metering station maintenance impacts the allocation uncertainty and, consequently, the risk of lost revenue. The objective of this work is to provide a method for making informed decisions about metering station maintenance based on economic risk.
Illustration of uncertainty contributions to Field A allocation uncertainty. This example has three fields. Field A has five wells, three measured with multiphase meters (MFM) and two estimated based on performance curves. All production from Field B is measured through a first stage separator and include sampling and PVT analysis, while Field C production is partially measured by a subsea multiphase meter (MFM) and partially by a test separator.
Based on the measurement uncertainty of the individual metering stations, the allocation uncertainty for each field is calculated. Initially, the allocation system is modelled in a modular, scalable framework, allowing for arbitrarily complex systems. ISO GUM-compliant Monte Carlo methods are then applied to estimate the allocation uncertainty. This uncertainty is converted to risk of economic loss due to misallocation for each field, establishing a relation between measurement uncertainty and economic risk. This relationship is the basis for investigating possible changes to maintenance, calibration, and sampling procedures in metering stations and choosing the best overall strategy with respect to risk exposure.
The analysis described above is summarized in uncertainty budgets revealing which measurements contribute the most to the overall risk exposure. The uncertainty contributions can be visualized as shown in Figure 1 and allow to focus maintenance efforts. In cases where input measurement uncertainties are difficult to establish, a sensitivity analysis of the complete allocation system still gives valuable information regarding the relative importance of the different measurements. This indicates where in the system maintenance requirements may be relaxed without a significant change in the overall risk. To demonstrate the usefulness of the approach, results based on an industrial case are presented where frequent sampling and PVT-analysis is mandatory in order to reduce risk exposure. In a different case it is shown how large costs can be saved by relaxing the maintenance scheme of metering stations traditionally considered essential in the allocation system.
This work shows the importance of in-depth knowledge of the relation between maintenance and risk exposure for an effective and intelligent reduction of operational costs. It is not always intuitive to identify which measurements that affect the economic risk the most, and a thorough analysis of the allocation system may thus reveal unexpected and smart cost savings.
This work presents a novel way of efficient and intelligent maintenance planning with focus on allocation uncertainty and exposure to economic risk. The methodology presented in this work benefits the petroleum industry by identifying where costs related to metering can be reduced and where it matters with a clear view of impact on the economic risk of the field owners.