This paper proposes and tests a practical way to use Data Validation and Reconciliation (DVR) to improve production allocation in commingled oil and gas systems by detecting bad measurements and reconciling data before allocation. It contrasts the traditional PSM + proportional (pro-rata) allocation approach, which balances only at export and does not integrate measurement quality, with a PSM-DVR workflow that uses process constraints, redundancy, and uncertainty weighting to “vote out” erroneous data and deliver allocations that remain physically consistent.
Using a simulated multi-tier facility (multiple inlet separators, two trains, intermediate nodes, export meter), the authors run several scenarios: moderate imbalance, adding export composition measurements, introducing a gross flowmeter bias, adding a sample composition error, replacing export composition with only density, calibrating meters and correcting sample errors, and finally losing an inlet flowmeter. Across cases, DVR provides diagnostics (global and individual penalties) that flag biased meters or bad samples, improves allocation robustness versus proportional allocation when gross errors exist, and can generate soft-sensor estimates for missing measurements when redundancy is sufficient. A key finding is that adding high-quality measurements (especially export composition) increases redundancy, improves reconciled estimates and uncertainties, and strengthens error localization, while reduced information (density only) weakens root-cause detection. The paper concludes that DVR is valuable at minimum as a surveillance layer to guide corrective actions before any allocation method, and that fully integrated PSM-DVR is preferred when DVR-based allocation is required to avoid model/EOS inconsistencies across separate software.
Paper on NFOGM
Generally, upstream oil and gas export measurements are made on separated and depressurized bulk oil and gas flow streams collected from a group of wells. In depressurized conditions, where phase separation can be ensured, single phase measurements can be made with the best possible accuracy. These measurements follow measurement standards and recommended practices, such as from the API MPMS, Chapter 20, Section 1.
Wherever export production contains fluids from more than one producer (or unique ownership group, whether it is for one well or several), there must be an equitable distribution of the production export to each and every contributing producer. The allocation process serves to determine in the fairest manner the quantities of oil and gas produced, flared, consumed for fuel, or otherwise spent out of the total export over a given time period for each contributing producer. The allocation process starts at the end of upstream production, from the point of custody transfer to the midstream transporter, and works back upstream to the source of production, the well. In determining each producer’s fair share of the export production, the resulting revenues and costs, such as production handling service fees, royalties and other costs, can be completely resolved.
The allocation process of quantifying the volume or mass of fluids produced from each well applies similarly to non-fiscal activities, such as the management of well performance, process facility operations, and reservoir recovery. While these applications are also important, they generally do not involve the resolution of inter- company financial transactions in accordance with an agreement. The elements of upstream metering and allocation carried out for fiscal allocation often encompass most of the needs for well or fluid allocation; however, the unique measurement requirements of reservoir and production management should be considered separately in order to get a complete set of metering and allocation requirements for all end uses of the flow measurement data.
Finally, if the allocation fully serves its purpose, it should be auditable and defensible. A good allocation minimizes disputes between partners in a production agreement. In practice, how is this achieved? Unmixing the mixed streams of hydrocarbons from different wells, zones and fields is not straightforward. It can be downright challenging, and it certainly can be done in different ways leading to different outcomes, which leads back to the possibility of dispute. A good allocation, therefore, is one that is agreeable to all parties involved.
For each producer to get a consistently good, equitable allocation, it requires:
- a written agreement that defines the objectives and methods of the allocation
- a metering system that can deliver the required flow and other measurements
- an auditable, independent execution of the allocation process
As with many physical phenomena, a deterministic approach to defining the outcome of certain pre-established procedures and agreements is not always realistic. Errors and uncertainties in measurements, processes, and models, visible and hidden, are also critical to the allocation process requiring attention and understanding by all parties involved, the producers and regulator alike. Identifying and understanding the sources of measurements errors is a necessary step to minimize their impact on allocation.
The risk of revenue loss by any of the parties, big or small, due to ill-defined statistical factors will strongly reflect on the sense of fairness felt by everyone. Such situation can trigger with individuals a sense of unfairness as they learn that the production allocated to their lease is deemed less certain than the one from next door despite the fact that they both use the same equipment to measure production! Can this knowledge be used to mitigate “unfairness” and reduce the exposure faced by producers and regulators in the execution of their duties?
Through the study of simulated production scenarios the paper highlights ways to detect and deal with errant data in production allocation data sets. It also proposes and evaluates a practical procedure that turns DVR error-qualifiable production data into allocated quantities the same way traditional PSM systems are used in production allocation. The difference is that in the latter approach the data qualification for potential data bias or imprecision is not integrated in the allocation process leaving room for production misallocation risks.
The evaluated approach is based on maximizing the use of all available process information (devices and fluids) in an attempt to “vote out” erroneous measurements once identified. Results are evaluated with and without the erroneous data in moderate cases where measurements cannot be replaced for cost or operational reasons. The Data Validation and Reconciliation (DVR), as the name implies, is evaluated for measurement error identification (surveillance functionality), and for its ability to make production estimates that qualify as allocated quantities of a relatively complex multi- tiered commingled production system. The current work builds on earlier effort that aimed at studying the use of measurement uncertainty in production allocation; Pro-rata, Uncertainty-based, and DVR-based methodologies [1].
DVR background and basic theory are first reviewed then followed by a review of the steps employed to perform traditional proportional allocation of volumetric quantities using Process Simulation Modeling (PSM). A PSM-DVR approach to allocation is proposed in the paper and tested by comparing the results to the “True Values” of a Reference simulation and to the results obtained from the PSM-Proportional Allocation methodology. However before performing allocation calculations, various production scenarios are simulated with various types of measurement and fluids property errors to test DVR’s surveillance capabilities. Proportional and DVR based allocation is carried out with and without errors and with varying amount of information to examine the robustness of allocation answers of each allocation methodology. The results are compared with the process “True Values”.
The paper continues in summarizing practical considerations and recommendations on the use of DVR in surveillance and/or allocation applications. It is also shown that if DVR-based allocation is not adopted for commercial or contractual reasons, other allocation methodologies will continue to benefit from a parallel DVR implementation for surveillance applications. Moreover, while the determination of measurements uncertainties has direct relevance to monetary arrangements if DVR is used for allocation, the constraints in quantifying the uncertainty values can be relaxed if the DVR process is used for surveillance only.
