Skip to main content
SpringerLink
Log in

A Novel Sparsity Deploying Reinforcement Deep Learning Algorithm for Saturation Mapping of Oil and Gas Reservoirs

  • Research Article-Petroleum Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The 4IR technology has assumed critical importance in the oil and gas industry, enabling automation at an unprecedented level. Advanced algorithms are deployed in enhancing production forecast and maximize sweep efficiency. A novel sparsity-based reinforcement learning algorithm, utilizing a surface response model approach, was developed for the estimation of hydrocarbon saturation in the interwell region. Application of the novel algorithms on a realistic reservoir box model exhibited strong performance in the estimation of the interwell saturation as well as the quantification of uncertainty. The results outline the broader application of the framework for interwell saturation mapping.

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

Reference

  1. Ertekin, T.; Sun, Q.: Artificial intelligence applications in reservoir engineering: a status check. Energies 12(15), 2897, 2019

    Article  Google Scholar 

  2. Lim, J.-S.: Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea. J. Pet. Sci. Eng. 49(3–4), 182–192, 2005

    Article  Google Scholar 

  3. Wood, D.: Predicting porosity, permeability and water saturation applying an optimized nearest-neighbour, machine-learning and data-mining network of well-log data. J. Pet. Sci. Eng. 184, 106587, 2020

    Article  Google Scholar 

  4. Tariq, Z.; Mahmoud, M.; Abdulraheem, A.: An Artificial Intelligence Approach to Predict the Water Saturation inCarbonate Reservoir Rocks. In: SPE-195804-MS, SPE Annual Technical Conference and Exhibition, Calgary, (2019)

  5. Marsala, A.; Ma, S.: Crosswell electromagnetic tomography: from resistivity mapping to interwell fluid distribution. In: IPTC 12229 presented at the International Petroleum Technology Conference, Kuala Lumpur, (2008)

  6. Katterbauer, K.; Hoteit, I.; Sun, S.: Synergizing crosswell seismic and electromagnetic techniques for enhancing reservoir characterization. SPE-174559-PA - SPE Journal, vol. 21, no. 3, (2016)

  7. Katterbauer, K.; Hoteit, I.; Sun, S.: A time domain update method for reservoir history matching of electromagnetic data. In: Offshore Technology Conference, (2014)

  8. Marsala, A.; Lyngra, S.: Crosswell electromagnetic induction between two widely spaced horizontal wells: coiled-tubing conveyed data collection and 3D inversion from a carbonate reservoir in Saudi Arabia. In: SEG International Exposition and 85th Annual Meeting, New Orleans, (2015).

  9. Marsala, A.; Lyngra, S.; AlSaif, S.: 3D Inversion practice for crosswell electromagnetic surveys in horizontal wells in Saudi Arabia. In: SEG International Exposition and 85th Annual Meeting, New Orleans, (2015)

  10. Marsala, A.; Lyngra, S.; Ma, M.; Zhang, P.: Workflow to integrate geophysical deep EM and reservoir simulation for inter-well saturation mapping presented. In: 79th EAGE Conference & Exhibition, Paris, (2017)

  11. Saif, S.; Marsala, A.: Uncertainty quantification algorithms for reservoir characterization of interwell volumes. In: AAPG International Conference and Exhibition, Capte Town, (2018)

  12. Burden, F.; Winkler, D.: Bayesian regularization of neural networks. In: David, J.L. (ed.) Artificial Neural Networks. Methods in Molecular Biology™, Humana Press, pp. 23–42 (2008)

Download references

Author information

Authors and Affiliations

  1. Saudi Aramco, Dhahran, Saudi Arabia

    Klemens Katterbauer & Alberto Marsala

Authors
  1. Klemens Katterbauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alberto Marsala
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Klemens Katterbauer.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 872 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Katterbauer, K., Marsala, A. A Novel Sparsity Deploying Reinforcement Deep Learning Algorithm for Saturation Mapping of Oil and Gas Reservoirs. Arab J Sci Eng 46, 6859–6865 (2021). https://doi.org/10.1007/s13369-020-05023-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-020-05023-2

Keywords

Search

Navigation