Production allocation


The aim is to develop a method to monitor the relative contributions of individual streams to production flow, to allocate production from different wells within the same reservoir unit, or from wells completed in different reservoir zones, without the requirement for expensive downhole meters.

An additional benefit of production monitoring is that changes over time that may lead to undesirable problems (e.g. deasphalting or wax deposition) can be spotted in time to take avoiding action (Elsinger et al. 2007).

Production allocation requires identification of compositional differences between each stream to permit deconvolution of the final mixture into proportions of the various feeds. An initial study of end-members is undertaken to establish a suitable analytical protocol, which is subsequently used to monitor variations in production allocation. Studies usually involve oil, but the same principles apply to gas.

It is not entirely necessary to know the identity of chromatographic peaks that are used to differentiate contributions, if it is assumed that the composition of individual streams will remain constant over time. However, it can be useful to identify compounds so that the influence of various processes can be taken into account (e.g. thermal maturity, organofacies and biodegradation; Jokanola et al. 2010).

When dealing with heavy oils and oil sands, compositional variations are mostly associated with biodegradation, and it is important to consider components that are not susceptible to fractionation, preferential production during cold production or evaporation. So Bennett et al. (2009) suggest dibenzothiophenes rather than naphthalenes (4MDBT is biodegraded more rapidly than 1MDBT).

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Sample requirements

Ideally, petroleum samples representative of end-member compositions can be obtained for characterisation, so that a suitable analytical protocol can be established to estimate the proportional contribution of each stream to the final production flow. Thereafter regular sampling of the main flow is all that is required at a suitable location and time intervals.

1 mL of oil suffices for GC and GC-MS analyses, although larger samples are advisable to ensure a representative sample has been obtained, and also to cater for any other analyses that may be desirable, such as physical properties.

Production allocation without end-member samples is possible but requires a wide range of mixtures, ideally encompassing as much of the range between end-members as possible (e.g. Peters et al. 2008; Zhan et al. 2016).

Analytical procedure

The type of hydrocarbons involved determines the precise analytical protocol. For gases, molecular and C isotopic composition should be obtained, as routinely undertaken for test gas characterization. For oils molecular compositional analysis is the norm, but the type of analysis depends upon the volatility ranges of comingled streams.

If it is relatively wide, it is necessary to monitor across the entire volatility range, so a combination of whole-oil GC to monitor fluctuations in light hydrocarbon composition, together with GC-MS analysis of biomarker distributions in the C15+ fraction may be required.

An initial study is required to establish key differences in compositions of contributing streams, which is the most time consuming and expensive part of the study. Once a number of key peaks have been identified across the volatility range, variations can be monitored using either concentrations of the individual compounds, or ratios of pairs of closely eluting peaks.

Where isotopic data are used – which is particularly applicable to gases – values are weighted according to the concentration of each compound in order to overcome non-linearity in mixing models.

APT has developed a HR-GC-MS method for whole-oil analysis, in which samples are analysed in triplicate over a mass range of m/z 100–500 (i.e. C7+) at a resolution of 3000. An automated computer procedure then identifies significant differences between pairs of end members in terms of peak retention time and m/z values.

This permits a number of ions to be selected, and further triplicate analyses are performed using just these ions to improve signal:noise ratio. This permits either peak ratios or concentrations to be used to estimate end-member contributions to mixed oils.

It is advisable to test the final mixing model by analyzing mixtures of end-members of varying known proportions. Assuming analytical precision of 1–3%, variations of at least 5% in concentrations or peak ratios are required to infer statistically significant differences between samples (e.g. Kaufman et al. 1987, 1990; Hwang et al. 2000).

Mixing of two end-members is straight-forward and extension to greater numbers is possible, although advanced statistical methods may be required that are beyond the scope of this document (McCaffrey et al. 1996, 2010). More difficulty is presented if one or more of the end members cannot be sampled.

Potential problems

  • lack of available representative samples of end-members
  • unknown number of end-members
  • large number of end-members


Bennett B., Adams J.J., Larter S.R. (2009) Oil fingerprinting for production allocation: exploiting the natural variations in fluid properties encountered in heavy oil and oil sand reservoirs. Frontiers and Innovation, CSPG CSEG CMLS Convention 2009, Calgary, Alberta, 157–160.

Elsinger R.J., Gelin F., Leenaarts E. (2007) Multiple applications of geochemical production allocation in North Sea operations. IMOG 2007, poster P45-MO.

Hwang R.J., Baskin D.K., Teerman S.C. (2000) Allocation of commingled pipeline oils to field production. Organic Geochemistry 31, 1463–1474.

Jokanola O., Michael G.E., Estrada E., Roberts N., McWhite C. (2010) Application of gas geochemistry in production allocation and well performance monitoring. AAPG Search and Discovery article #90110, AAPG Hedberg Conference, June 8–11, 2010, Vail, Colorado.

Kaufman R.L., Ahmed A.S., Hempkins W.B. (1987) A new technique for the analysis of commingled oils and its application to production allocation calculations. Paper IPA 87-23/21, 16th Annual Meeting Indonesian Petroleum Association 247–268.

Kaufman R.L., Ahmed A.S., Elsinger R.J. (1990) Gas Chromatography as a development and production tool for fingerprinting oils from individual reservoirs: applications in the Gulf of Mexico. In (ed. Schumaker D., Perkins B.F.) Proceedings of the 9th Annual Research Conference of the Society of Economic Paleontologists and Mineralogists , New Orleans, 263–282.

McCaffrey M.A., Legarre H.A., Johnson S.J. (1996) Using biomarkers to improve heavy oil reservoir management: An example from the Cymric field, Kern County, California. AAPG Bulletin 80, 904-919.

McCaffrey M.A., Baskin D.K., Patterson B.A., Dahl J.E., Weissenburger K.S. (2010) Geochemical allocation of commingled oil production and/or commingled gas production from 2–6 pay zones. AAPG Search and Discovery Article #90110, AAPG Hedberg Conference, June 8–11, 2010, Vail, Colorado. Application Publication #2013/0138360A1.

Peters K.E., Ramos L.S., Zumberge J.E., Valin Z.C. and Bird K.J. (2008) De-convoluting mixed crude oil in Prudhoe Bay Field, North Slope, Alaska. Organic Geochemistry 39, 623–645.

Zhan Z.-W., Zou Y.-R., Shi J.-T., Sun J.-N., Peng P. (2016) Unmixing of mixed oils using chemometrics. Organic Geochemistry 92, 1–15.