Skip to main content

Optimizing the location of the gas injection well during gas assisted gravity drainage in a fractured carbonate reservoir using artificial intelligence


Gas assisted gravity drainage (GAGD) is a novel subdivision of gas injection method. In this method the injection wells are located in the upper bed of the oil zone, and the production wells are drilled at the bottom bed of the oil zone. Reservoir simulation is among the decision tools for investigating production rate and selecting the best scenarios for developing the oil and gas fields. Selecting the location of the injection wells for reaching the optimized pressure and production rate is one of the most significant challenges during the injection process. Recent experiences have shown that artificial intelligence (AI) is a reliable solution for taking the mentioned decision appropriately and in a least possible time. This study is attributed to the investigation of applying the artificial neural network (ANN) as an artificial intelligence method and a potent predictor for choosing the most proper location for injection in a GAGD process in a fractured carbonate reservoir. The results of this investigation clearly show the efficiency of the ANN as a powerful tool for optimizing the location of the injection wells in a GAGD process. The comparison between the results of ANN and black oil simulator indicated that the predictions obtained from the ANN is highly reliable. In fact the production flow rate and pressure can be obtained in every possible location of the injection well.

This is a preview of subscription content, access via your institution.


  1. Mehrotra, K., Elements of Artificial Neural Networks (Complex Adaptive Systems), Cambridge, Mass.: MIT Press, 1996.

    Google Scholar 

  2. Mohaghegh, D., Virtual intelligence applications in petroleum engineering: Part 1—Artificial neural networks, J. Pet. Technol. 2000, vol. 52, no. 9, p. 8.

    Article  Google Scholar 

  3. Mohaghegh, D., Quantifying uncertainties associated with reservoir simulation studied using surrogate reservoir models, in SPE Annual Technical Conference and Exhibition, San Antonio, Tex, 2006.

    Google Scholar 

  4. Rumgulam, A., Ertekin, T., and Feleming, P.B., Utilization of artificial neural network in the optimization of history matching, SPE 107468.

  5. Sampaio, T.P., Ferreira, V.J.M., and Sa Neto, A., An application of neural network as nonlinear proxies for the use during the history matching phase, Latin American & Caribbean Petroleum Engineering Conf., Cartagena de Indias, Colombia, 2009.

    Google Scholar 

  6. Silva, P.C., Maschio, C., and Schiozer, D.J., Evaluation of neuro-simulation techniques as profixes to reservoir simulation, Rio Oil Gas Expo. Conf., Rio de Janiero, Brazil, 2006.

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Naser Akhlaghi.

Additional information

The article is published in the original.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akhlaghi, N., Kharrat, R. & Rezaei, F. Optimizing the location of the gas injection well during gas assisted gravity drainage in a fractured carbonate reservoir using artificial intelligence. Theor Found Chem Eng 51, 65–69 (2017).

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: