How to Improve Reservoir Characterization Models Using Intelligent Systems

  • Soto B. Rodolfo
  • Fredy Torres
  • Sandro Arango
  • Gloria Cobaleda
  • Stephen Holditch
  • Ching Wu
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 80)


The degree of success of many oil and gas drilling, completion, and production activities depends upon the accuracy of the models used in a reservoir description. Soft Computing techniques (neural networks, fuzzy logic, genetic algorithms) have demonstrated that it is possible to get more confident models compared with conventional techniques. However, before developing a soft-computing model, engineers must solve the problem of dimensionality such as determining dominant and irrelevant variables. Principal component and factor analyses can be applied to reduce the dimensionality and help soft computing techniques formulate more realistic models.

This paper presents a methodology that looks to solve the inverse problem of predicting reservoir properties on uncored intervals/wells, using soft computing techniques (neural networks and fuzzy logic) and multivariate statistical analysis. Concepts of multivariate statistical analysis, neural network, and fuzzy logic are integrated to get better petrophysical models. The first step in this methodology is to preprocess data, for which Q-Q plots are used with 95% confidence ellipses for quality control. After that, the dominant and the optimum number of independent variables are identified from core and well log using principal components, factor analysis and fuzzy logic concepts. Then, neural networks are applied to model any target variables. To check model validity, residual plots, average absolute errors, and correlation coefficients are used.

An intelligent Software have been used, Oilfield Intelligence (OI), as an engineering tool in this methodology. OI integrates neural networks, fuzzy logic and multivariate statistical analysis. It is composed of five main subsystems: database, preprocessing, architecture design, graphics design, and inference engine modules. OI was validated by obtaining confident models in three different oil field problems: (1) a neural network permeability model using porosity and gamma ray and a neural network pseudo-gamma ray log model using 3D seismic attributes for the reservoir VLE 196 Lamar field located in Block V of south-central Lake Maracaibo (Venezuela), (2) neural network primary ultimate oil recovery (PRUR), initial waterflooding ultimate oil recovery (IWUR), and infill drilling ultimate oil recovery (IDUR) models using reservoir parameters for San Andres and Clearfork carbonate formations in west Texas, and (3) neural network permeability model for K1 unit represented by 33 progressive transgressional subunits and deposits of sand and clay of marine origin form of Suria and Reforma-Libertad fields in Colombia.


Fuzzy Logic Neural Network Model Multivariate Statistical Analysis Residual Plot Average Absolute Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Soto B. Rodolfo
    • 1
  • Fredy Torres
    • 2
  • Sandro Arango
    • 2
  • Gloria Cobaleda
    • 1
  • Stephen Holditch
    • 3
  • Ching Wu
    • 3
  1. 1.Ecopetrol-Instituto Colombiano del PetróleoBucaramangaColombia
  2. 2.American GeoexplorationUSA
  3. 3.Texas A &M UniversityUSA

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