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Application of Improved Artificial Neural Network Algorithm in Hydrocarbons’ Reservoir Evaluation

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Book cover Renewable Energy for Smart and Sustainable Cities (ICAIRES 2018)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 62))

Abstract

The aim of this work is to develop an artificial neural network software tool in Matlab which allows the well logging interpreter to evaluate hydrocarbons reservoirs by classification of its existing facies into six types (clay, anhydrite, dolomite, limestone, sandstone and salt), the advantage of such classification is that it is automatic and gives more precision in comparison to manual recognition using industrial software. The developed algorithm is applied to eleven wells data of the Algerian Sahara where necessary curves (Gama Ray, density curve Rhob, Neutron porosity curve Nphi, Sonic curve dt, photoelectric factor curve PE) for realization of this technique are available. A graphical user interface is developed in order to simplify the use of the algorithm for interpreters.

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Author information

Authors and Affiliations

  1. Reservoir Evaluation Department, Exploration and Production Division, Sonatrach, Hassi Messaoud, Algeria

    M. Z. Doghmane

  2. Gassi Touil Division Production, Sonatrach, Hassi Messaoud, Algeria

    B. Belahcene

  3. Department of Automation, Faculty of Hydrocarbons and Chemistry (Ex-INH), University M’hamed Bougara, Boumerdes, Boumerdes, Algeria

    M. Z. Doghmane & M. Kidouche

Authors
  1. M. Z. Doghmane
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  2. B. Belahcene
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  3. M. Kidouche
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Corresponding author

Correspondence to M. Z. Doghmane .

Editor information

Editors and Affiliations

  1. EPST-CDER, Unité de Développement des Equipements Solaires, Bou-Ismail, Algeria

    Mustapha Hatti

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Doghmane, M.Z., Belahcene, B., Kidouche, M. (2019). Application of Improved Artificial Neural Network Algorithm in Hydrocarbons’ Reservoir Evaluation. In: Hatti, M. (eds) Renewable Energy for Smart and Sustainable Cities. ICAIRES 2018. Lecture Notes in Networks and Systems, vol 62. Springer, Cham. https://doi.org/10.1007/978-3-030-04789-4_14

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