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

  • M. Z. DoghmaneEmail author
  • B. Belahcene
  • M. Kidouche
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 62)


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.


Artificial neural networks Lithofacies classification Industrial software Algerian Sahara Graphical user interface 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Reservoir Evaluation Department, Exploration and Production DivisionSonatrachHassi MessaoudAlgeria
  2. 2.Gassi Touil Division ProductionSonatrachHassi MessaoudAlgeria
  3. 3.Department of Automation, Faculty of Hydrocarbons and Chemistry (Ex-INH)University M’hamed Bougara, BoumerdesBoumerdesAlgeria

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