1887

Abstract

Summary

Granite fractured basement reservoir contributes higher 40% of the world's oil and gas reserves. However, geological modeling of fractured reservoirs is complex and presents unique challenges in comparison with conventional reservoirs. It is extremely difficult to achieve the best results for a future development plan. This research presented the new workflow to enhance the accuracy of porosity and permeability models for a fractured reservoir in offshore Vietnam by using Artificial Neural Network (ANN) and co-kriging method. ANN was employed to solve problems that conventional modeling has not been successful. The seismic attributes selection was used for initial ANN generation. Then, the prediction property model was established through ANN training process. Well log data was used for correlation to cross-validation the predictive models. Next, the co-kriging algorithm was created the porosity and permeability models. Also, the Drill Stem Test (DST) data was used for history matching models to confirm the Co-kriging approach. The history matching was iterated until the geological model achieved the best matching with DST data. The history match shown the excellent fitting between simulation model and measurement data. Overall, we conclude that ANN and co-kriging are useful method for developing reliable workflow in fracture basement reservoir.

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/content/papers/10.3997/2214-4609.201900706
2019-06-03
2024-03-29
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References

  1. Kamalyar, K., Y.Sheikhi, and M.Jamialahmadi
    . 2011. “Using an Artificial Neural Network for Predicting Water Saturation in an Iranian Oil Reservoir.”Petroleum Science and Technology30 (1): 35–45. doi:10.1080/10916461003752561.
    https://doi.org/10.1080/10916461003752561 [Google Scholar]
  2. Mahdavi, Ramin, and RiyazKharrat
    . 2009. “SPE 121214 Integration of 3D Seismic Attributes and Well Logs for Electrofacies Mapping and Prediction of Reliable Petrophysical Properties.”
    [Google Scholar]
  3. Min, B. H., C.Park, J. M.Kang, H. J.Park, and I. S.Jang
    . 2011. “Optimal Well Placement Based on Artificial Neural Network Incorporating the Productivity Potential.”Energy Sources, Part A: Recovery, Utilization and Environmental Effects33 (18): 1726–38. doi:10.1080/15567030903468569.
    https://doi.org/10.1080/15567030903468569 [Google Scholar]
  4. Mirzaei-Paiaman, A., and S.Salavati
    . 2012. “The Application of Artificial Neural Networks for the Prediction of Oil Production Flow Rate.”Energy Sources, Part A: Recovery, Utilization and Environmental Effects34 (19): 1834–43. doi:10.1080/15567036.2010.492386.
    https://doi.org/10.1080/15567036.2010.492386 [Google Scholar]
  5. Qi, Lianshuang, and Timothy R.Carr
    . 2006. “Neural Network Prediction of Carbonate Lithofacies from Well Logs, Big Bow and Sand Arroyo Creek Fields, Southwest Kansas.”Computers and Geosciences32 (7): 947–64. doi:10.1016/j.cageo.2005.10.020.
    https://doi.org/10.1016/j.cageo.2005.10.020 [Google Scholar]
  6. Silakorn, P., T.Puncreobutr, T.Rakthanmanon, S.Punpruk, and C.Chanvanichskul
    . 2016. “IPTC-18658-MS The Application of ANN Artificial Neural Network to Pipeline TOLC Metal Loss Database.” In International Petroleum Technology Conference. Bangkok, Thailand, 14–16 November 2016.
    [Google Scholar]
  7. Wong, P. M., F.X.Jian, and I.J.Taggart
    . 1995. “A Critical Comparison of Neural Networks and Discriminant Analysis in Lithofacies, Porosity and Permeability Predictions.”Journal of Petroleum Geology18 (2): 191–206. http://onlinelibrary.wiley.com/doi/10.1111/j.1747-5457.1995.tb00897.x/abstract.
    [Google Scholar]
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