Intelligent methods for predicting nuclear magnetic resonance of porosity and permeability by conventional well-logs: a case study of Saharan field

  • Rafik Baouche
  • Tahar AïfaEmail author
  • Kamel Baddari
Original Paper


In the well-log data processing, the principal advantage of the nuclear magnetic resonance (NMR) method is the measurement of fluid volume and pore size distribution without resorting to parameters such as rock resistivity. Preliminary processing of the well-log data allowed first to have the petrophysical parameters and then to evaluate the performances of the transverse relaxation time T 2 NMR. Petrophysical parameters such as the porosity of the formation as well as the effective permeability can be estimated without having recourse the fluid type. The well-log data of five wells were completed during the construction of intelligent models in the Saharan oil field Oued Mya Basin in order to assess the reliability of the developed models. Data processing of NMR combined with conventional well data was performed by artificial intelligence. First, the support vector regression method was applied to a sandy clay reservoir with a model based on the prediction of porosity and permeability. NMR parameters estimated using intelligent systems, i.e., fuzzy logic (FL) model, back propagation neural network (BP-NN), and support vector machine, with conventional well-log data are combined with those of NMR, resulting in a good estimation of porosity and permeability. The results obtained during the processing are then compared to the FL and NN regression models performed by the regression method during the validation stage. They show that the correlation coefficients R 2 estimated vary between 0.959 and 0.964, corresponding to the root mean square error values of 0.20 and 0.15.


Porosity/permeability estimation Intelligent reservoir characterization Saharan oil field NMR prediction 



Longitudinal relaxation time, seconds


Transverse relaxation time, seconds


Logarithmic mean of T 2 distribution spectrum


T 2 distribution of all fluids


Geometric mean relaxation time


Clay-bound fluid volume


Capillary-bound fluid volume


Very small pore fluid volume


Small pore fluid volume


Medium pore fluid volume


Medium-large pore fluid volume


Large pore fluid volume


Very large pore fluid volume


Nuclear magnetic resonance log


Fuzzy logic


Neural network


Support vector regression


Artificial neural networks


Scaled conjugate gradient


Committee machine with empirical formula


Genetic algorithm


Free fluid porosity


Free fluid volume


Bulk fluid volume


Irreducible water saturation


Absorption factor


Mean absolute error


Average absolute error


Leave-one-out cross-validation


Constant parameter of the kernel function


Insensitive loss function



We would like to thank Sonatrach Co. for providing the data and the Petrolog software program. A Matlab code was developed to better simulate and predict the porosity and the permeability. We are indebted to an anonymous reviewer for the fruitful comments which helped improve this manuscript.


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Copyright information

© Saudi Society for Geosciences 2017

Authors and Affiliations

  1. 1.Département de Gisement, Laboratoire de Ressources Minérales et EnergétiquesUniversité M’Hamed BougaraBoumerdèsAlgeria
  2. 2.Géosciences-Rennes, CNRS UMR6118Université de Rennes 1Rennes cedexFrance
  3. 3.Laboratoire de Physique de la Terre (LABOPHYT), F.H.C.Université M’Hamed BougaraBoumerdèsAlgeria

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