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The AAPG/Datapages Combined Publications Database

AAPG Bulletin


AAPG Bulletin, V. 105, No. 10 (October 2021), P. 2041-2062.

Copyright ©2021. The American Association of Petroleum Geologists. All rights reserved.

DOI: 10.1306/03122120021

The battle between Frankenstein and Gilligan and the Law of Increasing Reservoir Complexification: What matters in three-dimensional reservoir characterization modeling?

D. K. Larue1

1Retired, Newport Beach, California; [email protected]


Two end-member approaches to three-dimensional (3-D) reservoir characterization modeling exist. The “Frankenstein” approach integrates hard and soft conditioning data while incorporating geologic concepts and interpretations to construct a highly detailed deterministic model. The Frankenstein approach focuses on what is known about the reservoir and is so named because construction of the model can be time-consuming, which can negatively impact project timelines, sometimes “killing” its creator. The “Gilligan” approach uses available data to build suites of models that are simple, and strives to address what is not known about the reservoir. Gilligan models are simplified, at least in the view of the Frankenstein modeler, and focus on quantitative characterizations of uncertainty.

Reservoirs commonly become more complex with new data from wells and production, a concept referred to here as “the Law of Increasing Reservoir Complexification.” The Law of Increasing Reservoir Complexification should influence earth modeling workflow choices.

Currently, the Gilligan probabilistic modeling approach is more commonly employed in early phases of projects. The appearance of overly complex Frankenstein models when probabilistic models are needed remains an ongoing problem. Mature reservoirs typically possess uncertainties that could influence business decisions and may warrant a Gilligan probabilistic approach as well.

No single recipe exists for choosing the best 3-D modeling approach: a fit-for-purpose or Goldilocks modeling approach should depend on business needs and requirements. Recognition that uncertainty is a fundamental and prevalent driver of business value for all reservoirs in all phases of development demands widespread adoption of probabilistic or Gilligan approaches.

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