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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

Abstract

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.

Keywords

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

Nomenclature

T1

Longitudinal relaxation time, seconds

T2

Transverse relaxation time, seconds

T2LM

Logarithmic mean of T 2 distribution spectrum

T2_DIST

T 2 distribution of all fluids

T2gm

Geometric mean relaxation time

Bin1

Clay-bound fluid volume

Bin2

Capillary-bound fluid volume

Bin3

Very small pore fluid volume

Bin4

Small pore fluid volume

Bin5

Medium pore fluid volume

Bin6

Medium-large pore fluid volume

Bin7

Large pore fluid volume

Bin8

Very large pore fluid volume

NMR

Nuclear magnetic resonance log

FL

Fuzzy logic

NN

Neural network

SVR

Support vector regression

ANNs

Artificial neural networks

SCG

Scaled conjugate gradient

CMEF

Committee machine with empirical formula

GA

Genetic algorithm

FFP

Free fluid porosity

FFV

Free fluid volume

BFV

Bulk fluid volume

Swir

Irreducible water saturation

PE

Absorption factor

MAE

Mean absolute error

AAE

Average absolute error

LOO

Leave-one-out cross-validation

σ

Constant parameter of the kernel function

ε

Insensitive loss function

Notes

Acknowledgements

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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