Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Abbreviations

T 1 :

Longitudinal relaxation time, seconds

T 2 :

Transverse relaxation time, seconds

T 2LM :

Logarithmic mean of T 2 distribution spectrum

T 2_DIST:

T 2 distribution of all fluids

T 2gm :

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

References

  • Aïfa T, Baouche R, Baddari K (2014a) Neuro-fuzzy system to predict permeability and porosity from well log data: a case study of Hassi R’Mel gas field, Algeria. J Pet Sci Eng 123:217–229

    Article  Google Scholar 

  • Aïfa T, Ali Zerrouki A, Baddari K, Geraud Y (2014b) Magnetic susceptibility and its relation with fractures and petrophysical parameters in the tight sand oil reservoir of Hamra quartzites, southwest of the Hassi Messaoud oil field, Algeria. J Pet Sci Eng 123:120–137

    Article  Google Scholar 

  • Al-Anazi AF, Gates ID (2010) Support vector regression for porosity prediction in a heterogeneous reservoir: a comparative study. Comput Geosci 36(12):1494–1503. https://doi.org/10.1016/j.cageo.2010.03.022

    Article  Google Scholar 

  • Bakhorji A, Schmitt D (2008) Velocity of P- and S-waves in Arab-D and WCSB (Western Canada Sedimentary Basin, Canada) carbonates. In: Proceedings of the Canadian Society of Petroleum Geology (CSPG), Canadian Society of Exploration Geophysicists (CSEG) and Canadian Well-logging Society (CWLS) Convention, Calgary, pp 368–372

  • Bhatt A, Helle HB (2002) Committee neural networks for porosity and permeability prediction from well logs. Geophys Prospect 50:645–660

    Article  Google Scholar 

  • Boudjemaa A (1987) Evolution Structurale du Bassin Triasique dans le Nord Est du Sahara (Algérie). PhD Thesis, Université Paris-Sud, Orsay, 279p

  • Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278

    Google Scholar 

  • Coates GR, Xiao L, Prammer MG (1999) NMR logging. Principles and applications. Gulf Publishing Company, Halliburton Energy Services, Houston 234p

    Google Scholar 

  • Courel L, Aït Salem H, Ben Ismail H, El Mostaïne M, Fekirine B, Kamoun F, Mami L, Oujidi M, Soussi M (2000) An overview of the epicontinental, Triassic series of the Maghreb (NW Africa). In: Bachmann GH & Lerche I (eds): Epicontinental Triassic. Zbl Geol Paläont 2(9–10):1145–1166

  • Dominic S, Das R, Whitley D, Anderson C (1991) Genetic reinforcement learning for neural networks. IJCNN-91-Seattle International Joint Conference on Neural Networks, Washington, Seattle, USA. IEEE Neural Netw 2:71–76

    Google Scholar 

  • Eastwood RL, Castagna JP (1987) Interpretation of Vp/Vs ratios from sonic logs. Shear-wave exploration, SHDanbom& SN Domenico(eds), SEG, Geophysical Development Series no. 1. Tulsa, pp 139–153

  • Fung C, Wong K, Eren H (1997) Modular artificial neural network for prediction of petrophysical properties from well log data. IEEE Trans Instrum Meas 46(6):1295–1299. https://doi.org/10.1109/19.668276

    Article  Google Scholar 

  • Galeazzi S, Point O, Haddadi N, Mather J, Druesne D (2012) The Illizi and BerkineBasins in Southern Algeria [abstract]. In: Roberts DG, Bally AW (eds) Regional geology and tectonics: phanerozoic passive pargins, cratonic basins and global tectonic maps. Elsevier, Amsterdam, pp 662–729

    Chapter  Google Scholar 

  • Hahn EL (1950) Spin Echoes. Phys Rev 80(4):580–594. https://doi.org/10.1103/PhysRev.80.580

    Article  Google Scholar 

  • Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New York. https://doi.org/10.1007/978-0-387-21606-5

    Book  Google Scholar 

  • Hawkins DM (2004) The problem of overfitting. J Chem Inf Comput Sci 44(1):1–12. https://doi.org/10.1021/ci0342472

    Article  Google Scholar 

  • Helle H, Ursin B (2001) Porosity and permeability prediction from wireline logs using artificial neural networks: a North Sea case study. Geophys Prospect 49(4):431–444. https://doi.org/10.1046/j.1365-2478.2001.00271.x

    Article  Google Scholar 

  • Huang Z, Shimeld J, Williamson M, Katsube J (1996) Permeability prediction with artificial neural network modelling in the Venture gas field, offshore Eastern Canada. Geophysics 61(2):422–436. https://doi.org/10.1190/1.1443970

    Article  Google Scholar 

  • Huang Y, Gedeon TD, Wong PM (2001) An integrated neural-fuzzy-genetic- algorithm using hyper-surface membership functions to predict permeability in petroleum reservoirs. Eng Appl Artif Intell 14(1):15–21. https://doi.org/10.1016/S0952-1976(00)00048-8

    Article  Google Scholar 

  • Hurlimann MD (2001) Diffusion and relaxation effects in general stray field NMR experiments. J Magn Reson 148(2):367–378. https://doi.org/10.1006/jmre.2000.2263

    Article  Google Scholar 

  • Kadkhodaie-Ilkhchi A, Rezaee MR, Moallemi SA (2006) A fuzzy logic approach for the estimation of permeability and rock types from conventional well log data: an example from the Kangan reservoir in Iran offshore gas field, Iran. J Geophys Eng 3(4):356–369. https://doi.org/10.1088/1742-2132/3/4/007

    Article  Google Scholar 

  • Kadkhodaie-Ilkhchi A, Rahimpour-Bonab H, Rezaee MR (2008) A committee machine with intelligent systems for estimation of total organic carbon content from petrophysical data: an example from Kangan and Dalan reservoirs in South Pars gas field, Iran. Comput Geosci 35:459–474

    Article  Google Scholar 

  • Karimpouli S, Malehmir A (2015) Neuro-Bayesian facies inversion of prestack seismic data from a carbonate reservoir in Iran. J Pet Sci Eng 131:11–17. https://doi.org/10.1016/j.petrol.2015.04.024

    Article  Google Scholar 

  • Katahara KW (1995) Gamma ray log response in shaly sands. Log Anal 36(4):50–56

    Google Scholar 

  • Kenyon WE (1997) Petrophysical principles of applications of NMR logging. Log Anal 38(2):21–43

    Google Scholar 

  • Kenyon WE, Day PI, Straley C, Willemsen JF (1988) A three-part study of NMR longitudinal relaxation properties of water-saturated sandstones. SPE Form Eval 3(3):622–636 SPE-15643-PA

    Article  Google Scholar 

  • Malki HA, Baldwin J (2002) A neuro-fuzzy based oil/gas producibility estimation method. IEEE Int Joint Conf Neural Netw 1:896–901

    Google Scholar 

  • Mamdani EH (1976) Advances in the linguistic synthesis of fuzzy controllers. Int J Man-Mach Stud 8(6):669–678. https://doi.org/10.1016/S0020-7373(76)80028-4

    Article  Google Scholar 

  • Masoudi P, NadjarAraabi B, Aïfa T, Memarian M (2016) Clustering as an efficient tool for assessing fluid content and movability by resistivity logs. In: Numerical Proceedings of the 4th International Mine and Mining Industries Congress and Exposition, Tehran, 9p

  • Masoudi P, Aïfa T, Memarian M, Tokhmechi B (2017a) Uncertainty assessment of volume of investigation to enhance the vertical resolution of well-logs. J Pet Sci Eng 154:252–276

    Article  Google Scholar 

  • Masoudi P, Aïfa T, Memarian M, Tokhmechi B (2017b) Uncertainty assessment of porosity and permeability by clustering algorithm and fuzzy arithmetic. J Pet Sci Eng. https://doi.org/10.1016/j.petrol.2017.11.018

  • Matlab user’s guide (2014) Version 2, Fuzzy Logic Toolbox. Math Works, USA, 235p

  • Mohaghegh S (2000) Virtual-intelligence applications in petroleum engineering: part I: artificial neural networks. J Pet Technol 52(09):64–73. https://doi.org/10.2118/58046-JPT

    Article  Google Scholar 

  • Narushima Y, Hiroshi Y (2012) Conjugate gradient methods based on secant conditions that generate descent search directions for unconstrained optimization. J Comput Appl Math 236(17):4303–4317

    Article  Google Scholar 

  • Nguyen D, Widrow B (1990) Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In: Proceedings of the International Joint Conference on Neural Networks 3:21–26

  • Nikravesh M, Aminzadeh F (2003) Soft computing for intelligent reservoir characterization and modeling. In: Nikravesh M, Aminzadeh F, Zadeh LA (eds) Soft computing and intelligent data analysis, developments in petroleum science series, vol 51. Elsevier, Amsterdam, pp 3–32

    Google Scholar 

  • Ogilvie SR, Cuddy S, Lindsay C, Hurst A (2002) Novel methods of permeability prediction from NMR tool data. Dialog (magazine of the London Petrophysical Society), September

  • Petrolog (2010) Advanced log analysis software, V.10. Crocker data processing, Petroleum House, WA 6102, Australia

  • Rogers SJ, Fang JH, Karr CL, Stanley DA (1995) Determination of lithology from well logs using a neural network. AAPG Bull 76(5):731–739

    Google Scholar 

  • Sabaou N, Aït Salem H, Zazoun RS (2009) Chemostratigraphy, tectonic setting and provenance of the Cambro-Ordovician clastic deposits of the subsurface Algerian Sahara. Sciences et Technologies des Hydrocarbures 1(1):27–33

    Google Scholar 

  • Schlumberger (1988) Basic log interpretation, Schlumberger log interpretation charts. Schlumberger Educational Services, Texas

    Google Scholar 

  • Sherrod PH (2009) DTREG Predictive Modeling Software User’s Manual, Version 9.1, http://www.dtreg.com

  • Sugeno M (1985) Industrial applications of fuzzy control. Elsevier Science Pub. Co, North Holland

    Google Scholar 

  • Sugeno M, Kang GT (1988) Structure identification of fuzzy model. Fuzzy Sets Syst 28(1):15–33. https://doi.org/10.1016/0165-0114(88)90113-3

    Article  Google Scholar 

  • Tahmasebi P, Hezarkhani A (2011) Application of a modular feedforward neural network for grade estimation. Nat Resour Res 20(1):25–32. https://doi.org/10.1007/s11053-011-9135-3

    Article  Google Scholar 

  • Tahmasebi P, Javadpour F, Sahimi M (2017) Data mining and machine learning for identifying sweet spots in shale reservoirs. Expert Syst Appl 88:435–447. https://doi.org/10.1016/j.eswa.2017.07.015

    Article  Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans Syst Man Cybern 15(1):116–132

    Article  Google Scholar 

  • Timur A (1968) An investigation of permeability, porosity, and residual water saturation relationships for sandstone reservoirs. In: SPWA 9th Annual Logging Symposium, New Orleans

  • Timur A (1969) Effective porosity and permeability of sandstones investigated through nuclear magnetic principles. Log Anal 10(1):3

    Google Scholar 

  • Wong PM, Nikravesh M (2001) Field applications of intelligent computing techniques. J Pet Geol 24(4):381–387. https://doi.org/10.1111/j.1747-5457.2001.tb00681.x

    Article  Google Scholar 

  • Xiao L, Mao ZQ, Li GR, Jin Y (2013) Estimation of saturation exponent from nuclear magnetic resonance (NMR) logs in low permeability reservoirs. Appl Magn Reson 44(3):333–347. https://doi.org/10.1007/s00723-012-0366-1

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Département de Gisement, Laboratoire de Ressources Minérales et Energétiques, Université M’Hamed Bougara, 35000, Boumerdès, Algeria

    Rafik Baouche

  2. Géosciences-Rennes, CNRS UMR6118, Université de Rennes 1, Bat.15, Campus de Beaulieu, 35042, Rennes cedex, France

    Tahar Aïfa

  3. Laboratoire de Physique de la Terre (LABOPHYT), F.H.C., Université M’Hamed Bougara, 35000, Boumerdès, Algeria

    Kamel Baddari

Authors
  1. Rafik Baouche
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tahar Aïfa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kamel Baddari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tahar Aïfa.

Additional information

Highlights

- New statistical values of NMR porosity and permeability from core data were applied to Saharan oil field, Oued Mya Basin.

- Conventional well logs were combined with NMR log parameters.

- Fuzzy logic clustering radii for permeability and porosity improved the NMR parameters for further performances.

- SVM shows best performance (R 2 = 0.96) for NMR compared to BP-NN (R 2 ~ 0.94) and FL (R 2 ~ 0.92) either for MPHIE or MPRN.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baouche, R., Aïfa, T. & Baddari, K. Intelligent methods for predicting nuclear magnetic resonance of porosity and permeability by conventional well-logs: a case study of Saharan field. Arab J Geosci 10, 545 (2017). https://doi.org/10.1007/s12517-017-3344-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-017-3344-y

Keywords

Search

Navigation