Mathematical Geosciences

, 43:847 | Cite as

Comparison of Two Methods Used to Model Shape Parameters of Pareto Distributions

  • Chenglin LiuEmail author
  • Ronald R. Charpentier
  • Jin Su


Two methods are compared for estimating the shape parameters of Pareto field-size (or pool-size) distributions for petroleum resource assessment. Both methods assume mature exploration in which most of the larger fields have been discovered. Both methods use the sizes of larger discovered fields to estimate the numbers and sizes of smaller fields: (1) the tail-truncated method uses a plot of field size versus size rank, and (2) the log–geometric method uses data binned in field-size classes and the ratios of adjacent bin counts. Simulation experiments were conducted using discovered oil and gas pool-size distributions from four petroleum systems in Alberta, Canada and using Pareto distributions generated by Monte Carlo simulation. The estimates of the shape parameters of the Pareto distributions, calculated by both the tail-truncated and log–geometric methods, generally stabilize where discovered pool numbers are greater than 100. However, with fewer than 100 discoveries, these estimates can vary greatly with each new discovery. The estimated shape parameters of the tail-truncated method are more stable and larger than those of the log–geometric method where the number of discovered pools is more than 100. Both methods, however, tend to underestimate the shape parameter. Monte Carlo simulation was also used to create sequences of discovered pool sizes by sampling from a Pareto distribution with a discovery process model using a defined exploration efficiency (in order to show how biased the sampling was in favor of larger fields being discovered first). A higher (more biased) exploration efficiency gives better estimates of the Pareto shape parameters.


Oil and gas Simulation Discovery process Sensitivity analyses Resource appraisal 


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Copyright information

© International Association for Mathematical Geosciences 2011

Authors and Affiliations

  1. 1.Geoscience FacultyChina University of PetroleumChangping District, BeijingP.R. China
  2. 2.State Key Laboratory of Petroleum Resource and ProspectingChina University of PetroleumBeijingChina
  3. 3.Denver Energy Resource CenterU.S. Geological SurveyLakewoodUSA

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