The challenge of combining all available flow rate information
Many oil and gas production systems have a varying degree of instrumentation for different wells. Some wells are highly instrumented with both multiphase flow meters (MPFM) and virtual flow meters (VFM) that measure flow rates in real time. Other wells have their flow rates measured only intermittently, e.g. when they are routed to a test separator. In some cases, there might even be wells where there is no direct way of measuring their flow rates. For example when the produced hydrocarbons are mixed with production from other wells before this commingled flow is measured.
There are numerous possible setups, but there exists a method that can be applied to all kinds of topologies and instrumentation setups to provide the most accurate flow rates.
Solution - unifying all measurements
Flow rate reconciliation is a method that exploits the mass balance constraints of the system and leverages redundant measurements. You can say that it unifies all of the measurements and looks at the system as a whole. The most important input is the system typology. That means a precise and accurate mapping of the produced hydrocarbons from each well through the production system.
Imagine a production system like the one in the illustration. A is a well with a MPFM, and B is a well with no direct measurements. The flow from well A and B are commingled before the combined flow is measured first by a MPFM in C and then a separator in D. Assuming steady-state production, we know by the law of mass balance that the flow through MPFM C and separator D must be the same. Knowing this, we can calculate the most likely flow through MPFM C and separator D as a weighted average of the two measurements, depending on the uncertainties of the MPFM and the separator. Also, from the mass balance, we know that the sum of the flow from well A and well B must be equal to the commingled flow. We now calculate the estimated flow rate of well B as the commingled flow minus the MPFM measurement of well A.
Naturally, these computations can become complex for large production systems. This was just a toy-example and in practice we formulate the problem with matrices and use methods of sensor fusion, like Kalman filters or Sequential Monte Carlo to provide the most probable flow rates.
Outcome - one high quality soft-tag for each flow phase
Flow rate reconciliation provides value for production teams, regardless if the asset is small and well-instrumented or large with less sensors per well. For a well with redundant measurements, it can help the engineer combine the measurements into one single source of truth for each of the phases. For sparsely instrumented systems the value is even greater, where this approach can help estimate flow rates in parts of the system where there is little or no (real-time) knowledge of the flow rates.