Back allocation from Tachyus
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Fully automated hydrocarbon back allocation using physics embedded machine learning, integrating well test, pressure and temperature data of your surface network.
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- Estimate daily oil, water and gas production at well heads and network nodes
- Integrate all available data such as well tests, pressures and temperatures
- Allocate production honoring three phase fluid flow physics in the surface network
- Account for measurement errors and uncertainties for robust back allocation
- Monitor and identify potential surface network and well issues proactively
- Identify and flag bad well tests and other measurements, optimize your well test program
- Back Allocation provides precise, continuous dynamic fluid allocation over the surface network
- Uses well known physical models and correlations, and state-of-the-art machine learning techniques
- Very fast and independent of interpretation bias, full field allocations can be done in hours
- Can run locally or on Tachyus Cloud, with no infrastructure footprint
- Data can be exported easily to other visualization and analysis tools
How it works
Hydrocarbon allocation from the sales-point to the wellheads is one of the most important processes needed for field management and operational decisions. Traditional products use simplistic mass balance and statistical methods to calculate allocation factors, which are then applied over periods of time, until the next well-test is available. As a result, such approaches are quite inaccurate and have a few key limitations:
- Inability of current methods to account for the physics of fluid flow in the surface network from the wellhead to the delivery point can lead to inaccurate allocations.
- Current approaches mainly use well-test data to calculate allocations, which are then applied over periods of time, until the next well-test is available. They don't use all available data such as pressure and temperatures over the surface network, which are usually more frequently available than well-test data.
- Current approaches assume that allocation factors remain constant over periods of time when in reality, several factors like well head pressure, injection-producer interaction generate significant changes making the allocation a dynamic process which needs to be continuously calculated.
Using state-of-the-art machine learning and data assimilation approaches with well-known physical models and correlations, Tachyus’ Back Allocation continuously calculates daily oil, water and gas rates at every wellhead and other network nodes that match all measured historical well tests, pressure and temperature data. The workflow runs very fast and can be powered by the cloud or local computing. Additionally, by combining with Tachyus Subsurface Back Allocation app, robust and accurate allocations can be extended all the way to the reservoir layers.