Skip to main content

Advertisement

SpringerLink
Log in

An AI-based workflow for estimating shale barrier configurations from SAGD production histories

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

An artificial intelligence (AI)-based workflow is deployed to develop and test procedures for estimating shale barrier configurations from SAGD production profiles. The data employed in this project are derived from a set of synthetic SAGD reservoir simulations based on petrophysical properties and operational constraints representative of Athabasca oil sands reservoirs. Initially, a two-dimensional reservoir simulation model is employed. The underlying model is homogeneous. Its petrophysical properties, such as the porosity, permeability, initial oil saturation and net pay thickness, have been taken from average values for several pads in Suncor’s Firebag project. Reservoir heterogeneities are simulated by superimposing sets of idealized shale barrier configurations on the homogeneous model. The location and geometry of each shale barrier is parameterized by a unique set of indices. The resulting heterogeneous model is subjected to flow simulation to simulate SAGD production. Next, a two-step workflow is followed: (1) a network model based on AI tools is constructed to match the output of the reservoir simulation (shale indices are inputs, while production rate is the output) for a known training set of shale barrier configurations; (2) for a new SAGD production history generated via reservoir simulation with a shale barrier configuration that is unknown to the AI model generated in Step 1, an optimization scheme based on a genetic algorithm approach is adopted to perturb the shale indices until the difference between the target production history and the production history predicted from the AI model is minimized. A number of cases have been tested. The results show a good agreement between the shale barrier configurations predicted by the AI model with the configurations used to generate production histories in the reservoir simulation model (i.e., the “true” model). Thus, this optimization workflow offers potential to become an alternative tool for indirect inference of the uncertain distribution of shale barriers in SAGD reservoirs from data capturing field performance. This work highlights the potential of an AI-based workflow to infer the presence and distribution of heterogeneous shale barriers from field SAGD production time series data. It presents an innovative parameterization scheme suitable for representing heterogeneous characteristics of shale barriers. If this approach proves to be successful, it could allow the distribution of shale barriers to be inferred together with the impact of these barriers on SAGD performance. This would provide a basis for developing operating strategies to reduce the impact of the barriers.

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

Similar content being viewed by others

Abbreviations

C :

An objective function that compares the similarity between two production profiles

h i :

Historical production profile at time i

i :

A single time point (monthly) along the production profile

m :

Total number of time points in the production profile

f i :

Forecasted production profile at time i

∆x :

Grid size in \(x\)-direction

∆z :

Grid size in \(z\)-direction

AI:

Artificial intelligence

ANN:

Artificial neural network

BP:

Back propagation

ELM:

Extreme learning machine

EnKF:

Ensemble Kalman filter

GA:

Genetic algorithm

LA:

Levenberg–Marquardt

LI:

Lean zone indicator

MLP:

Multilayer perceptron

NMSE:

Normalized mean squared error

RBF:

Radial basis function

SA:

Simulated annealing

SAGD:

Steam-assisted gravity drainage

SASP:

Simultaneous perturbation stochastic approximation

SI:

Shale indicator

SOR:

Steam-to-oil ratio

References

  1. Akin S (2005) Mathematical modeling of steam assisted gravity drainage. SPE Reserv Eval Eng 8(05):372–376

    Article  Google Scholar 

  2. Amirian E, Leung JY, Zanon S, Dzurman P (2015) Integrated cluster analysis and artificial neural network modeling for steam-assisted gravity drainage performance prediction in heterogeneous reservoirs. Expert Syst Appl 42(2):723–740

    Article  Google Scholar 

  3. Ballester PJ, Carter JN (2007) A parallel real-coded genetic algorithm for history matching and its application to a real petroleum reservoir. J Pet Sci Eng 59(3):157–168

    Article  Google Scholar 

  4. Brun B, Gosselin O, Barker JW (2004) Use of prior information in gradient-based history matching. SPE J 9(01):67–78

    Article  Google Scholar 

  5. Butler R, McNab G, Lo H (1981) Theoretical studies on the gravity drainage of heavy oil during in-situ steam heating. Can J Chem Eng 59(4):455–460

    Article  Google Scholar 

  6. Butler RM (1985) A new approach to the modelling of steam-assisted gravity drainage. J Can Pet Technol 24(03):42–51

    Article  Google Scholar 

  7. Butler RM (1991) Thermal recovery of oil and bitumen. Prentice Hall, Englewood Cliffs

    Google Scholar 

  8. Chen Q, Gerritsen MG, Kovscek AR (2008) Effects of reservoir heterogeneities on the steam-assisted gravity-drainage process. SPE Reserv Eval Eng 11(05):921–932

    Article  Google Scholar 

  9. CMG (2015) STARS: users’ guide, advanced processes & thermal reservoir simulator (version 2015). Computer Modeling Group Ltd, Calgary

    Google Scholar 

  10. de Sousa SHG (2007) Scatter search metaheuristic applied to the history matching problem. Paper presented at SPE annual technical conference and exhibition. Anaheim, California, USA

  11. Deutsch CV, Journel AG (1998) GSLIB: Geostatistical software library and user’s guide. Oxford University Press, New York

    Google Scholar 

  12. Ding S, Guo L, Hou Y (2017) Extreme learning machine with kernel model based on deep learning. Neural Comput Appl 28(8):1975–1984

    Article  Google Scholar 

  13. Elzwayie A, El-Shafie A, Yaseen ZM, Afan HA, Allawi MF (2017) RBFNN-based model for heavy metal prediction for different climatic and pollution conditions. Neural Comput Appl 28(8):1991–2003

    Article  Google Scholar 

  14. Fedutenko E, Yang C, Card C, Nghiem LX (2014) Time-dependent neural network based proxy modeling of SAGD process. Paper presented at the SPE heavy oil conference-Canada, Calgary, Alberta, Canada

  15. Feizabadi SA, Zhang XK, YangP (2014). An integrated approach to building history-matched geomodels to understand complex long lake oil sands reservoirs, part 2: simulation. Paper presented at the SPE heavy oil conference-Canada, Calgary, Canada

  16. Gao G, Li G, Reynolds AC (2007) A stochastic optimization algorithm for automatic history matching. SPE J 12(2):196–208

    Article  Google Scholar 

  17. Ghasemi M, Whitson CH (2013) Modeling steam-assisted gravity drainage with a black-oil proxy. SPE Reserv Eval Eng 16(02):155–171

    Article  Google Scholar 

  18. Hadamard J (1902) Sur les problèmes aux dérivées partielles et leur signification physique. Princeton Univ Bull 13:49–52

    Google Scholar 

  19. Hajizadeh Y, Christie M, Demyanov V (2011) Ant colony optimization for history matching and uncertainty quantification of reservoir models. J Pet Sci Eng 77(1):78–92

    Article  Google Scholar 

  20. Haykin S (2008) Multilayer perceptron. In: Horton MJ, Disanno S (eds) Neural networks and learning machines, 3rd edn. Prentice Hall, Englewood Cliffs, pp 197–199

  21. Hiebert AD, Morrish IC, Card C, Ha H, Porter S, Kumar A, Sun F, Close JC (2013) Incorporating 4D seismic steam chamber location information into assisted history matching for A SAGD simulation. Paper presented at the SPE heavy oil conference-Canada, Calgary, Canada

  22. IHS Energy (2015) AccuMap software, 321 Inverness Drive South Englewood CO 80112, USA. Retrieved June 25, 2015, from http://www.ihsenergy.com

  23. Ito Y, Chen J (2010) Numerical history match of the Burnt Lake SAGD process. J Can Pet Technol 49(5):40–49

    Article  Google Scholar 

  24. Jacquard P (1965) Permeability distribution from field pressure data. Soc Pet Eng J 5(04):281–294

    Article  Google Scholar 

  25. Jia X, Cunha L, Deutsch C (2009) Investigation of a stochastic optimization method for automatic history matching of SAGD processes. J Can Pet Technol 48(01):14–18

    Article  Google Scholar 

  26. Kabanikhin SI (2008) Definitions and examples of inverse and ill-posed problems. J Inverse Ill Posed Probl 16(4):317–357

    Article  MathSciNet  Google Scholar 

  27. Kingsford C, Salzberg SL (2008) What are decision trees? Nat Biotechnol 26(9):1011–1013

    Article  Google Scholar 

  28. Kisman K.E. & Yeung K.C. (1995). Numerical study of the SAGD process in the Burnt Lake oil sands lease. Paper presented at the SPE international heavy oil symposium, Calgary, Alberta, Canada

  29. Kruger WD (1961) Determining areal permeability distribution by calculations. J Pet Technol 13(07):691–696

    Article  Google Scholar 

  30. Le Ravalec M, Morlot C, Marmier R, Foulon D (2009) Heterogeneity impact on SAGD process performance in mobile heavy oil reservoir. Oil Gas Sci Technol Rev IFP 64(4):469–476

    Article  Google Scholar 

  31. Liu F, Wang J, Morris P (2013) A practical approach to history-matching large, multi-well SAGD simulation models: a mackay river case study. Paper presented at the SPE heavy oil conference-Canada, Calgary, Canada

  32. Liu J, Jaiswal A, Yao K, Raghavenda CS (2015) Autoencoder-derived features as inputs to classification algorithms for predicting well failures. Paper presented at the SPE western regional meeting, Garden Grove, California, USA

  33. Liu N, Oliver DS (2003) Evaluation of Monte Carlo methods for assessing uncertainty. SPE J 8(02):188–195

    Article  Google Scholar 

  34. Liu N, Oliver DS (2005) Ensemble Kalman filter for automatic history matching of geologic facies. J Pet Sci Eng 47(3):147–161

    Article  Google Scholar 

  35. Ma Z, Leung JY, Zanon S, Dzurman P (2015) Practical implementation of knowledge-based approaches for steam-assisted gravity drainage production analysis. Expert Syst Appl 42(21):7326–7343

    Article  Google Scholar 

  36. Maschio C, Vidal AC, Schiozer DJ (2008) A framework to integrate history matching and geostatistical modeling using genetic algorithm and direct search methods. J Pet Sci Eng 63(1):34–42

    Article  Google Scholar 

  37. McDonald JH (2009) Handbook of biological statistics. Sparky House Publishing, Baltimore

    Google Scholar 

  38. Mojarad M, Dehghanpour H (2016) Analytical modeling of emulsion flow at the edge of a steam chamber during a steam-assisted-gravity-drainage process. SPE J 21:353–363

    Article  Google Scholar 

  39. Noble WS (2006) What is a support vector machine? Nat Biotechnol 24(12):1565–1567

    Article  Google Scholar 

  40. Patel RG, Rahim S, Li Z (2015) Initial sampling of ensemble for steam-assisted-gravity-drainage-reservoir history matching. J Can Pet Technol 54:424–441

    Article  Google Scholar 

  41. Pooladi-Darvish M, Ali SM (1994). Steam heating of fractured formations containing heavy oil: basic premises and a single-block analytical model. Paper presented at the SPE annual technical conference and exhibition, New Orleans, Louisiana, USA

  42. Ouenes A, Brefort B, Meunier G, Dupere S (1993). A new algorithm for automatic history matching: application of simulated annealing method (SAM) to reservoir inverse modeling. Paper SPE 26297

  43. Oyedotun OK, Khashman A (2017) Deep learning in vision-based static hand gesture recognition. Neural Comput Appl 28(12):3941–3951

    Article  Google Scholar 

  44. Reis JC (1992) A steam-assisted gravity drainage model for tar sands: linear geometry. J Can Pet Technol 31(10):14–20

    Article  Google Scholar 

  45. Rose PE (1993). The steam-assisted gravity drainage of oil sand bitumen. Ph.D. thesis, Department of Chemicals and Fuels Engineering, The University of Utah, USA

  46. Scheidt C, Caers J (2009) Representing spatial uncertainty using distances and kernels. Math Geosci 41(4):397–419

    Article  Google Scholar 

  47. Sharma J, Gates ID (2010) Multiphase flow at the edge of a steam chamber. Can J Chem Eng 88(3):312–321

    Google Scholar 

  48. Suncor Energy (2012) Suncor Firebag 2012 ERCB performance presentation. Retrieved July 16, 2015, from https://www.aer.ca/documents/oilsands/insitu-presentations/2012AthabascaSuncorFirebagSAGD8870.pdf

  49. Suncor Energy (2013) Suncor Firebag 2013 ERCB performance presentation. Retrieved July 16, 2015, from https://www.aer.ca/documents/oilsands/insitu-presentations/2013AthabascaSuncorFirebagSAGD8870.pdf

  50. Suncor Energy (2014) Suncor Firebag 2014 AER Performance Presentation. Retrieved July 16, 2015, from https://www.aer.ca/documents/oilsands/insitu-presentations/2014AthabascaSuncorFirebagSAGD8870.pdf

  51. The MathWorks Inc., MATLAB and Global optimization toolbox release 2014b, Natick, Massachusetts, USA

  52. TOP Analysis (2015) TOP Analysis software, 144 - 4th Avenue SW Calgary AB T2P 3N4, Canada. Retrieved Aug 20, 2015, from http://www.topanalysis.com

  53. Wang C, Leung J (2015) Characterizing the effects of lean zones and shale distribution in steam-assisted-gravity-drainage recovery performance. SPE Reserv Eval Eng 18:329–345

    Article  Google Scholar 

  54. Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4:65–85

    Article  Google Scholar 

  55. Yang C, Nghiem LX, Card C, Bremeier M (2007). Reservoir model uncertainty quantification through computer-assisted history matching. In: SPE annual technical conference and exhibition. Anaheim, California, USA

  56. Yang G, Butler R (1992) Effects of reservoir heterogeneities on heavy oil recovery by steam-assisted gravity drainage. J Can Pet Technol 31(08)

  57. Yu T, Wilkinson D, Castellini A (2008) Constructing reservoir flow simulator proxies using genetic programming for history matching and production forecast uncertainty analysis. J Artif Evol Appl 2008:2

    Google Scholar 

  58. Zhang XK, Feizabadi SA, Yang P (2014). An integrated approach to building history-matched geomodels to understand complex long lake oil sands reservoirs, part 1: geomodeling. Paper presented at the SPE heavy oil conference-Canada, Calgary, Canada

  59. Zhang L, Tian F (2014) Performance study of multilayer perceptrons in a low-cost electronic nose. IEEE Trans Instrum Meas 63(7):1670–1679

    Article  Google Scholar 

  60. Zhang L, Zhang D (2016) Robust visual knowledge transfer via extreme learning machine-based domain adaptation. IEEE Trans Image Process 25(10):4959–4973

    Article  MathSciNet  Google Scholar 

  61. Zhang L, Zhang D (2017) Evolutionary cost-sensitive extreme learning machine. IEEE Transon Neural Netw Learn Syst 28(12):3045–3060

    Article  MathSciNet  Google Scholar 

  62. Zheng J, Leung JY, Sawatzky RP, Alvarez JM (2016) A proxy model for predicting SAGD production from reservoirs containing shale barriers. Paper presented at the SPE Canada heavy oil technical conference, Calgary, Canada

Download references

Acknowledgements

This research was supported by InnoTech Alberta under a PhD Pilot Program administered by the University of Alberta. Academic licenses for STARS are provided by Computer Modeling Group (CMG).

Author information

Authors and Affiliations

  1. University of Alberta, Edmonton, Canada

    Jingwen Zheng & Juliana Y. Leung

  2. InnoTech Alberta, Edmonton, Canada

    Ronald P. Sawatzky & Jose M. Alvarez

Authors
  1. Jingwen Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juliana Y. Leung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ronald P. Sawatzky
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jose M. Alvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juliana Y. Leung.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, J., Leung, J.Y., Sawatzky, R.P. et al. An AI-based workflow for estimating shale barrier configurations from SAGD production histories. Neural Comput & Applic 31, 5273–5297 (2019). https://doi.org/10.1007/s00521-018-3365-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3365-9

Keywords

Search

Navigation