APT Allomon - Production allocation and monitoring service
Production monitoring and allocation
APT has developed 'APT Allomon' - a service that enables effective production allocation and monitoring.
This involves analysis of a time series of production fluids from the same source (e.g. a pipeline, field, reservoir, well) and interpretation of the resulting data in order to look for changes in fluid chemistry caused by production-related processes over time.
The determination of the amount or portion of a commingled fluid to be assigned to two or more individual fluid sources (e.g. a pipeline, field, reservoir, well) at a particular moment in time, based on the fluid chemistry.
Why is it important?
The optimum recovery of petroleum resources requires an understanding of the recovery mechanisms and how their effectiveness varies with respect to the geology of the reservoir system.
Production logging tools comprise a range of techniques designed to monitor the flow rate and saturations along a well bore during field production.
Traditional production logging techniques aim to resolve a number of problems:
- Diagnose production problems and allocate production
- Select zones for recompletion
- Geochemically-based production methods do not require intervention: there is no risk to the well and none of the risk entailed in additional operational activity.
- They are incredibly cheap – orders of magnitude less than a conventional PLT logging program. There is no additional rig time, and no extra personnel required at the well site.
- Applicable to a wide range of fields irrespective of pressure, temperature, reservoir quality, reservoir fluid type etc.
- Data are from the actual production fluids themselves, not a surrogate such as a tracer or microseismic.
- Good quality fluid samples – some drilling fluid contamination can be tolerated.
- “End members” (usually) for production allocation (not for monitoring).
Production allocation objective: Determine the proportions of end-members that constitute a commingled production sample.
Fundamental basis of method: Some property (peak concentration, ratio etc.) of a mixture is expressed in terms of the end-members, one equation for each property, resulting in a (possibly large) set of linear equations.
Analytical methods selected:
- Different data types used (and combined if appropriate).
Data selection and pre-processing
- Optimise data pre-processing, flexible approach, potential benefits of different methods in differing circumstances.
End-member contributions to the mixture determined
- Solve equations (typically many more equations than end-members, so a least-squares "best-fit").
Estimate uncertainty, "plus-minus", confidence intervals etc.
- Monte Carlo simulation (requires replicate analyses to estimate data variance).
- Bootstrap simulation (replicates desirable but not essential).
Reporting is flexible and individually designed for each client.
Fully digital in any format designated by the client (pdf, Word, Excel, PowerPoint, etc.).
Regular reporting schedule throughout project life span.