Mathematical Geosciences

, Volume 50, Issue 3, pp 317–335 | Cite as

Exploratory Factor Analysis of Wireline Logs Using a Float-Encoded Genetic Algorithm

  • Norbert Péter Szabó
  • Mihály Dobróka


In the paper, a novel inversion approach is used for the solution of the problem of factor analysis. The float-encoded genetic algorithm as a global optimization method is implemented to extract factor variables using open-hole logging data. The suggested statistical workflow is used to give a reliable estimate for not only the factors but also the related petrophysical properties in hydrocarbon formations. In the first step, the factor loadings and scores are estimated by Jöreskog’s fast approximate method, which are gradually improved by the genetic algorithm. The forward problem is solved to calculate wireline logs directly from the factor scores. In each generation, the observed and calculated well logs are compared to update the factor population. During the genetic algorithm run, the average fitness of factor populations is maximized to give the best fit between the observed and theoretical data. By using the empirical relation between the first factor and formation shaliness, the shale volume is estimated along the borehole. Permeability as a derived quantity also correlates with the first factor, which allows its determination from an independent source. The estimation results agree well with those of independent deterministic modeling and core measurements. Case studies from Hungary and the USA demonstrate the feasibility of the global optimization based factor analysis, which provides a useful tool for improved reservoir characterization.


Float-encoded genetic algorithm Factor analysis Shale volume Permeability Hungary USA 



This research was supported by the GINOP-2.3.2-15-2016-00010 “Development of enhanced engineering methods with the aim at utilization of subterranean energy resources” project in the framework of the Széchenyi 2020 Plan, funded by the European Union, cofinanced by the European Structural and Investment Funds. The first author thanks the support of the GINOP project. The research was partly supported by the National Research Development and Innovation Office (Project No. K109441), and as the leader of the project, the second author thanks the Office for its support. Both authors thank the Geokomplex Ltd. for providing well logs and grain-size data from Well-3.


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Copyright information

© International Association for Mathematical Geosciences 2017

Authors and Affiliations

  1. 1.Department of GeophysicsUniversity of MiskolcMiskolc-EgyetemvárosHungary
  2. 2.MTA–ME Geoengineering Research GroupUniversity of MiskolcMiskolc-EgyetemvárosHungary

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