Top

Neural Computing and Applications

Published in:

01-04-2012 | Review

Artificial neural networks workflow and its application in the petroleum industry

Authors: N. I. Al-Bulushi, P. R. King, M. J. Blunt, M. Kraaijveld

Published in: Neural Computing and Applications | Issue 3/2012

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

We develop a neural network workflow, which provides a systematic approach for tackling various problems in petroleum engineering. The workflow covers several design issues for constructing neural network models, especially in terms of developing the network structure. We apply the model to predict water saturation in an oilfield in Oman. Water saturation can be accurately obtained from data measured from cores removed from the oil field, but this information is limited to a few wells. Wireline log data are more abundantly available in most wells, and they provide valuable, but indirect, information about rock properties. A three-layered neural network model with five hidden neurons and a resilient back-propagation algorithm is found to be the best design for the saturation prediction. The input variables to the model are density, neutron, resistivity, and photo-electric wireline logs, and the model is trained using core water saturation. The model is able to predict the saturation directly from wireline logs with a correlation coefficient (r) of 0.91 and an error of 2.5 saturation units on the testing data.

next article Performance of fuzzy logic-based slope tuning of neural equaliser for digital communication channel

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

Ali JK (1994) Neural network: a new tool for petroleum industry. Proceedings of SPE European petroleum computer conference, UK, paper 27561

Olson TM (1998) Porosity and permeability prediction in low-permeability gas reservoirs from well logs using neural networks. Proceedings of SPE rocky mountain regional/low-permeability reservoir symposium and exhibition, USA, paper 39964

White AC, Molnar D, Aminian K, Mohaghegh S, Ameri S (1995) The application of ANN for zone identification in a complex reservoir. Proceedings of the SPE Eastern regional conference & exhibition, USA, paper 30977

Bhatt A, Helle HB (2002) Determination of facies from well logs using modular neural networks. Pet Geosci 8:217–228CrossRef

Helle HB, Bhatt A (2002) Fluid saturation from well logs using committee neural networks. Pet Geosci 8:109–118CrossRef

Al-Bulushi N, King P, Blunt M, Kraaijveld M (2009) Development of articial neural network for predicting water saturation and fluid distribution. J Petrol Sci Eng 68:197–208CrossRef

Archie GE (1941) The electrical resistivity log as an aid in determining some reservoir characteristics. Trans AIME 146:54–62

Waxman MH, Smits LJM (1968) Electrical conductivities in oil-bearing shaly sands. Soc Petrol Eng J 18(2):107–122

Leverett MC (1941) Capillary behavior in porous solids. Trans AIME 146:152–169

10.

Worthington PE (1985) The evolution of shaly sand concepts in reservoir evaluation. The Log Analyst 26(1):23–40

11.

Ipek I (2002) Log-derived cation exchange capacity of shaly sands: application to hydrocarbon detection and drilling optimisation. Doctoral diss., Louisiana State University

12.

Rezaee MR, Lemon NM (1996) Petrophysical evaluation of kaolinite-bearing sandstone: Water saturation (S_w), an example of the Tirrawarra sandstone reservoir, Copper Basin. Proceedings of the SPE Asia pacific oil & gas conference, Australia, paper 37023

13.

Haykin S (1998) neural networks, a comprehensive foundation. Prentice Hall PTR, 2nd edn. USA

14.

Essenreiter R, Karrenbach M, Treite S (1998) Multiple reflection attenuation in seismic data using back-propagation. IEEE Trans Signal Process 46(7):2001–2011CrossRef

15.

Maren AJ, Harston CT, Pap RM (1990) Handbook of neural computing applications. Academic Press, New York

16.

Hush DR, Horne BG (1993) Progress in supervised neural networks. IEEE Signal Process Mag 10(1):8–39CrossRef

17.

Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press

18.

Hagan M, Demuth HB, Beale M (1996) Neural network design. PWS Publishing Company, USA

19.

Reidmiller M, Braun H (1993) A Direct adaptive method for faster back-propagation learning: the PROP algorithm. Proceedings of the IEEE international conference on neural network, pp 586–591

20.

Cun YLe, Denker JS, Solla SA (1990) Optimal brain damage. Adv Neural Network Process Syst 2:168–177

21.

Hush DR, Horne B, Salas JM (1992) Error surfaces for multilayer perceptrons. IEEE Trans Syst Man Cybern 22 (5)

22.

Baum EB, Haussler D (1990) What size net gives valid generalization? Neural Comput 1(1):151–160CrossRef

23.

Hush DR (1989) Classification with neural networks: a performance analysis. IEEE Int Conf Syst Eng, pp 227–280

24.

Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximates. Neural Netw 2:359–366CrossRef

25.

Irie B, Miyake S (1998) Capabilities of there-layered perceptron. Proc IEEE Int Conf Neural Netw, pp 641–648

26.

Hecht-Nielsen R (1990) Neurocomputing. Addison-Wesley, New York

27.

Huang SC, Huang YF (1991) Bounds on the number of hidden neurons in multilayer perceptrons. IEEE Trans Neural Netw 2(1):47–55CrossRef

28.

Cheng B, Titterington DM (1994) Neural networks: a review from a statistical perspective. Stat Sci 9(1):2–54MathSciNetMATHCrossRef

29.

Chester DL (1990) Why two hidden layers are better than one. Neural Netw J 1:265–268

30.

Flood I, Kartam N (1994) Neural networks in civil engineering. I: Principles and understanding. J Comput Civil Eng 8(2):131–148CrossRef

31.

Maier HR, Dandy GC (2000) Neural Networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. J Environ Modeling Softw 15:101–124CrossRef

32.

Moody JE (1992) The effective number of parameters: an analysis of generalization and regularization in non-linear learning system. Adv Neural Netw Process Syst 4:841–854

33.

Rogers LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resour Res 30(2):457–481CrossRef

34.

Bann VDM, Jutten C (2000) Neural networks in geophysical applications. Geophysics 65(4):1032–1047CrossRef

35.

Masters T (1993) Practical neural network recipes in C ≶≶. Academic Press

36.

Amari S, Murata N, Muller KR, Finke M, Yang H (1997) Asymptotic statistical theory of overtraining and cross-validation. IEEE Trans Neural Netw 8(5):985–996CrossRef

37.

Hanson SJ, Pratt L (1989) Comparing biases for minimal network construction with back-propagation. Advances in neural information processing systems 1, (Morgan Kaufmann Publishers Inc, pp 177–185)

38.

Lachtermacher G, Fuller JD (1994) Back-propagation in hydrological time series forecasting, stochastic and statistical methodology in hydrology and environmental engineering. In Hipel KW, McLeod AI, Panu US, Singh VP (eds) Kluwer Academic Publisher, Dordrecht, pp 229–242

39.

Goda HM, Maier HR, Behrenbruch P (2005) The development of an optimal artificial neural network model for estimating initial, irreducible water saturation- Australian reservoirs. Proceedings of the SPE Asia Pacific Oil & Gas conference and Exhibition, Indonesia, paper 93307

40.

Stunder M, Al-Tuwaini JS (2001) How data-driven modeling like neural networks can help to integrate different types of data into reservoir management. Proceedings of the society of petroleum engineers middle east oil show, paper 68163

41.

Flood I, Kartam N (1994) Neural networks in civil engineering. I: Principles and understanding. J Comput Civil Eng 8(2):131–148CrossRef

42.

Burden FR, Brereton RG, Walsh PT (1997) Cross-validatory selection of test and validation sets in multivariate calibration and neural networks as applied to spectroscopy. Analyst 122(10):1015–1022CrossRef

43.

Kaastra I, Boyd MS (1995) Forecasting futures trading volume using neural networks. J Futures Mark 15(8):953–970CrossRef

44.

Tukey JW (1977) Exploratory data analysis. Addison-Wesley, New YorkMATH

45.

Burke LI, Ignizio JP (1992) Neural networks and operations research: an overview. Comput Oper Res 19:179–189MATHCrossRef

46.

Faraway J, Chatfield C (1998) Time series forecasting with neural networks: a comparative study using the airline data. Appl Stat 14(2):109–250

47.

Fortin V, Ouarda TBMJ, Bobee B (1997) Comment on ‘‘the use of artificial neural networks for the prediction of water quality parameters’’ by Maier HR and Dandy GC. Water Resour Res 33(10):2423–2424CrossRef

48.

Shi JJ (2000) Reducing prediction error by transforming input data for neural networks. J Comput Civil Eng 14(2):109–166CrossRef

49.

Dimopoulos I, Chronopoulos J, Chronopoulou-Sereli A, Lek Sovan (1999) Neural network models to study lead concentration in grasses and permanent urban descriptors in Athens city (Greece). Ecol Modell 120:157–165CrossRef

50.

Gevery M, Dimopoulos I, Lek Sovan (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Modell 160:249–264CrossRef

51.

Garson D (1991) Interpreting neural network connection weights. AL Expert l6(4): 46

52.

Lek S, Delacoste M, Baran P, Dimopulos I, Lauga J, Auglagnier S (1996) Application of neural network to modelling nonlinear relationships in ecology. Ecol Modell 90:9–52CrossRef

53.

Schlumberger (1991) Log interpretation principles/application. Schlumberger educational series seventh printing

54.

Schlumberger oil glossary http://www.glossary.oilfield.slb.com

Title: Artificial neural networks workflow and its application in the petroleum industry
Authors: N. I. Al-Bulushi
P. R. King
M. J. Blunt
M. Kraaijveld
Publication date: 01-04-2012
Publisher: Springer-Verlag
Published in: Neural Computing and Applications / Issue 3/2012
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0501-6

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 3/2012

A deterministic model selection scheme for incremental RBFNN construction in time series forecasting

A novel concept of embedding orthogonal basis function expansion block in a neural equalizer structure for digital communication channel

Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting

Synthesis of neural tree models by improved breeder genetic programming

A neural predictive coding feature extraction scheme in DCT domain for phoneme recognition

Triaxial compression behavior of sand and tire wastes using neural networks

Premium Partner