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A Self-Adaptive Artificial Neural Network Technique to Predict Total Organic Carbon (TOC) Based on Well Logs

  • Research Article - Petroleum Engineering
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Abstract

Determination of total organic carbon (TOC) is a key method of characterizing shale reservoirs. The conventional method for TOC determination using cores from shale reservoirs is time-consuming and costly. TOC can be estimated by an indirect method using petrophysical well logs. The existing models assume a linear relationship between the well logs and TOC and have a high error and low correlation coefficients (CC) between the actual and predicted TOC. The first goal of this study is to apply a self-adaptive differential evolution (SaDE) optimizing method to determine the best combination of artificial neural network (ANN) parameters (number of hidden layers, number of neurons in each layer, training function, transferring function, and the training over testing ratio). The second goal is to develop a new empirical correlation that can be used to estimate TOC using well logs based on the optimized SaDE-ANN model. Four-hundred and sixty data points from Barnett shale were used for training and testing the developed SaDE-ANN model. Another set of data (29 data points) of Duvernay shale was used to compare the developed TOC correlation with the previous models. The obtained results show that the developed SaDE-ANN model predicted the TOC using only well logs: gamma ray (GR), compressional time (DT), deep resistivity (RD), and bulk density (RHOB) with a high accuracy (a CC of 0.99 and the average absolute percentage error (AAPE) of 6%). The developed TOC correlation outperformed the models proposed by Wang et al. (Mar Pet Geol 70:304–319, 2016. https://doi.org/10.1016/j.marpetgeo.2015.11.023) and Abdulhamid et al. (Int J Coal Geol 179(15):72–80, 2017). The new empirical correlation for TOC estimation reduced AAPE by 67% as compared with the ANN model developed by Abdulhamid et al. (2017) for the Duvernay formation. The developed TOC correlation is simple and can be applied using any computer without the need for the ANN model or special software. The developed technique will help reservoir engineers and geologists to estimate the TOC values using only the well logs with a high accuracy.

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Abbreviations

SaDE:

Self-adaptive differential evolution

ANN:

Artificial neural network

AI:

Artificial intelligence

CC:

Correlation coefficient

AAPE:

Average absolute percentage error

TOC:

Total organic carbon, wt%

DT:

Compressional time, us/ft

RD:

Deep resistivity, ohm m

RHOB:

Bulk density, \(\hbox {g/cm}^{3}\)

GR:

Gamma ray, API

N :

Number of neurons

w :

Weight associated with a layer and a hidden layer

i :

The index of each neuron in a hidden layer

b :

Bias associated with a hidden layer

References

  1. Passey, Q.R.; Bohacs, K.; Esch, W.L.; Klimentidis, R.; Sinha, S.: From oil-prone source rock to gas-producing shale reservoir-geologic and petrophysical characterization of unconventional shale gas reservoirs. In: International Oil and Gas Conference and Exhibition in China. Society of Petroleum Engineers (2010)

  2. Sondergeld, C.H.; Ambrose, R.J.; Rai, C.S.; Moncrieff, J.: Micro-structural studies of gas shales. In: SPE Unconventional Gas Conference. Society of Petroleum Engineers (2010)

  3. Altowairqi, Y.; Rezaee, R.; Evans, B.; Urosevic, M.: Shale elastic property relationships as a function of total organic carbon content using synthetic samples. J. Pet. Sci. Eng. 133, 392–400 (2015)

    Article  Google Scholar 

  4. Montgomery, S.L.; Jarvie, D.M.; Bowker, K.A.; Pollastro, R.M.: Mississippian Barnett Shale, Fort Worth basin, north-central Texas: gas-shale play with multi-trillion cubic foot potential. AAPG Bull. 89(2), 155–175 (2005)

    Article  Google Scholar 

  5. Ross, D.J.; Bustin, R.M.: Impact of mass balance calculations on adsorption capacities in microporous shale gas reservoirs. Fuel 86(17), 2696–2706 (2007)

    Article  Google Scholar 

  6. Sone, H.; Zoback, M.D.: Mechanical properties of shale-gas reservoir rocks—part 1: static and dynamic elastic properties and anisotropy. Geophysics 78(5), D381–D392 (2013)

    Article  Google Scholar 

  7. Zhang, T.; Ellis, G.S.; Ruppel, S.C.; Milliken, K.; Yang, R.: Effect of organic-matter type and thermal maturity on methane adsorption in shale-gas systems. Org. Geochem. 47, 120–131 (2012)

    Article  Google Scholar 

  8. Wang, P.; Chen, Z.; Pang, X.; Hu, K.; Sun, M.; Chen, X.: Revised models for determining TOC in shale play: example from Devonian Duvernay Shale, Western Canada Sedimentary Basin. Mar. Pet. Geol. 70, 304–319 (2016). https://doi.org/10.1016/j.marpetgeo.2015.11.023

    Article  Google Scholar 

  9. Ding, J.; Xiaozhi, C.; Xiudi, J.; Bin, W.; Jinmiao, Z.: Application of AVF inversion on shale gas reservoir TOC prediction. In: SEG Annual Meeting: Society of Exploration Geophysicists (2015)

  10. Schmoker, J.W.: Determination of organic content of Appalachian Devonian shales from formation-density logs. Am. Assoc. Pet. Geol. Bull. 63, 1504–1509 (1979). https://doi.org/10.1306/2F9185D1-16CE-11D7-8645000102C1865D

    Google Scholar 

  11. Schmoker, J.W.: Organic content of Devonian shale in Western Appalachian Basin. AAPG Bull. 64(12), 2156–2165 (1980)

    Google Scholar 

  12. Schmoker, J.W.; Hester, T.C.: Organic carbon in Bakken formation, United States portion of Williston basin. AAPG Bull. 67(12), 2165–2174 (1983)

    Google Scholar 

  13. Passey, Q.R.; Creaney, S.; Kulla, J.B.; Moretti, F.J.; Stroud, J.D.: A practical model for organic richness from porosity and resistivity logs. AAPG Bull. 74(12), 1777–1794 (1990)

    Google Scholar 

  14. Abdulhamid, A.; Elkatatny, S.M.; Mahmoud, M.A.; Aburesh, M.; Abdulraheem, A.; Ali, A.: Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network. Int. J. Coal Geol. 179(15), 72–80 (2017)

    Google Scholar 

  15. Arabjamaloei,; Shadizadeh, S.: Modeling and optimizing rate of penetration using intelligent systems in an Iranian Southern Oil Field (Ahwaz Oil Field). Pet. Sci.Technol. 29(16), 1637–1648 (2011). https://doi.org/10.1080/10916460902882818

    Article  Google Scholar 

  16. Lippmann, R.: An introduction to computing with neural nets. IEEE ASSP Mag. 4(2), 4–22 (1987)

    Article  Google Scholar 

  17. Jain, A.K.; Mao, J.; Mohiuddin, K.M.: Artificial neural networks: a tutorial. Computer 29(3), 31–44 (1996)

    Article  Google Scholar 

  18. Goyal, S.; Goyal, G.K.: Cascade and feedforward backpropagation artificial neural network models for prediction of sensory quality of instant coffee flavoured sterilized drink. Can. J. Artif. Intell. Mach. Learn. Pattern Recognit. 2(6), 78–82 (2011)

    Google Scholar 

  19. Vineis, P.; Rainoldi, A.: Neural networks and logistic regression: analysis of a case-control study on myocardial infarction. J. Clin. Epidemiol. 50, 1309–1310 (1997). https://doi.org/10.1016/S0895-4356(97)00163-7

    Article  Google Scholar 

  20. Burbidge, R.; Trotter, M.; Buxton, B.; Holden, S.: Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem. 26, 5–14 (2001)

    Article  Google Scholar 

  21. AlAjmi, M.D.; Alarifi, S.A.; Mahsoon, A.H.: Improving multiphase choke performance prediction and well production test validation using artificial intelligence: a new milestone. SPE-173394-MS, presented at the SPE Digital Energy Conference and Exhibition, held in the Woodlands, Texas, USA, 3–5 March 2015

  22. Elkatatny, S.; Mahmoud, M.; Tariq, Z.; Abdulraheem, A.: New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligence network. Neural Comput. Appl. 1–11 (2017)

  23. Elkatatny, S.; Tariq, Z.; Mahmoud, M.: Real time prediction of drilling fluid rheological properties using artificial neural networks visible mathematical model (white box). J. Pet. Sci. Eng. 146, 1202–1210 (2016)

    Article  Google Scholar 

  24. Van, S.L.; Chon, B.H.: Effective prediction and management of a CO\(_2\) flooding process for enhancing oil recovery using artificial neural networks. ASME J. Energy Resour. Technol. (2017). https://doi.org/10.1115/1.4038054

  25. Van, S.L.; Chon, B.H.: Evaluating the critical performances of a CO\(_2\)-enhanced oil recovery process using artificial neural network models. J. Pet. Sci. Eng. 157(2017b), 207–222 (2017)

    Article  Google Scholar 

  26. Pollastro, R.M.; Hill, R.J.; Jarvie, D.M.; Henry, M.E.: Assessing undiscovered resources of the Barnett-Paleozoic total petroleum system, Bend Arch-Fort Worth basin province, Texas. Search and Discovery Article (2003). Accessed 15 Dec 2014

  27. Pollastro, R.M.; Jarvie, D.M.; Hill, R.J.; Adams, C.W.: Geologic framework of the Mississippian Barnett Shale, Barnett-Paleozoic total petroleum system, Bend Arch-Fort Worth Basin, Texas. AAPG Bull. 91(4), 405–436 (2007). https://doi.org/10.1306/2F10300606008

    Article  Google Scholar 

  28. Jarvie, D.M.; Hill, R.J.; Ruble, T.E.; Pollastro, R.M.: Unconventional shale-gas systems: the Mississippian Barnett Shale of north-central Texas as one model for thermogenic shale-gas assessment. Am. Assoc. Pet. Geol. Bull. 91(4), 475–499 (2007)

    Google Scholar 

  29. Abouelresh, M.O.; Slatt, R.M.: Shale depositional processes: example from the Paleozoic Barnett Shale, Fort Worth Basin, Texas, USA. Cent. Eur. J. Geosci. 3(4), 398–409 (2011)

    Google Scholar 

  30. Abouelresh, M.O.; Slatt, R.M.: Lithofacies and sequence stratigraphy of the Barnett Shale in east-central Fort Worth Basin, Texas. AAPG Bull. 96(1), 1–22 (2012)

    Article  Google Scholar 

  31. Loucks, R.G.; Ruppel, S.C.: Mississippian Barnett Shale: lithofacies and depositional setting of a deepwater shale-gas succession in the Fort Worth Basin, Texas. AAPG Bull. 91(4), 579–601 (2007). https://doi.org/10.1306/11020606059

    Article  Google Scholar 

  32. Bowker, K.A.: Barnett Shale gas production: Fort Worth Basin-issues and discussion. AAPG Bull. 91(4), 523–533 (2007). https://doi.org/10.1306/06190606018

    Article  Google Scholar 

  33. Bowker, K.A.: Recent development of the Barnett Shale play, Fort Worth Basin. West Texas Geol. Soc. Bull. 42(6), 4–11 (2003)

    Google Scholar 

  34. Fu, Q.; Horvath, S.C.; Potter, E.C.; Roberts, F.; Tinker, S.W.; Ikonnikova, S.; Fisher, W.L.; Yan, J.: Log-derived thickness and porosity of the Barnett Shale, Fort Worth basin, Texas: implications for assessment of gas shale resources. AAPG Bull. 99(1), 119–141 (2015)

    Article  Google Scholar 

  35. Heslop, K.A.: Generalized method for the estimation of TOC from GR and Rt. In: AAPG Search Discov. Article. Article #80117 (2010)

  36. Liu, Y.; Chen, Z.; Hu, K.; Liu, C.: Quantifying total organic carbon (TOC) from well logs using support vector regression. GeoConvention 2013 Integr. 6 (2013)

  37. Luning, S.; Kolonic, S.: Uranium spectral gamma-ray response as a proxy for organic richness in black shales: applicability and limitations. J. Pet. Geol. 26, 153–174 (2003). https://doi.org/10.1111/j.1747-5457.2003.tb00023.x

    Article  Google Scholar 

  38. Jacobi, D.; Gladkikh, M.; Lecompte, B.; Hursan, G.; Mendez, F.; Longo, J.; Ong, S.; Bratovich, M.; Patton, G.; Hughes, B.; Shoemaker, P.: Integrated petrophysical evaluation of shale gas reservoirs. Paper “SPE-114925” Presented at CIPC/SPE Gas Technology Symposium 2008 Joint Conference held in Calgary, Alberta, Canada, 16–19 June 2006 (2008). https://doi.org/10.2118/114925-MS

  39. Gonzalez, J.; Lewis, R.; Hemingway, J.; Grau, J.; Rylander, E.; Pirie, I.: Determination of formation organic carbon content using a new neutron-induced gamma ray spectroscopy service that directly measures carbon. In: Unconventional Resources Technology Conference (2013). https://doi.org/10.1190/urtec2013-112

  40. Zhao, T.; Verma, S.; Devegowda, D.: TOC estimation in the Barnett Shale from triple combo logs using support vector machine. SEG New Orleans Annual Meeting (2015). https://doi.org/10.1190/segam2015-5922788.1

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  1. Department of Petroleum Engineering, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    Salaheldin Elkatatny

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Elkatatny, S. A Self-Adaptive Artificial Neural Network Technique to Predict Total Organic Carbon (TOC) Based on Well Logs. Arab J Sci Eng 44, 6127–6137 (2019). https://doi.org/10.1007/s13369-018-3672-6

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