1887
2nd Australasian Exploration Geoscience Conference: Data to Discovery
  • ISSN: 2202-0586
  • E-ISSN:

Abstract

Summary

Seismic reservoir characterization is the stat-of-art in using various source of data. Generally, seismic data, due to their low resolution are randomly used in the final steps of reservoir characterization. However, large coverage of 3D seismic data, compared to well data, make it possible to be applicable for distribution of characters through the whole reservoir. In this regard, seismic data should be inverted to illustrate desired characters through the media. Conventionally, seismic inversion is performed using well logs which have defects in its derivation steps, such as wavelet extraction and its propagation through media. The proposed strategy to resolve such deficiencies, is the genetic inversion. However, genetic inversion has its own deficiency in accuracy. In this study, we propose an integrated strategy for using various source of data in an iterative manner for resolving this obstacle. The proposed strategy, uses combined related attribute to evaluate initial acoustic impedance inverted model by genetic inversion. The model then would be updated to satisfy well data. The proposed strategy was applied on a heterogenous reservoir from south west of Iran. Three seismic attributes were integrated to produce a unique attribute for initial model evaluation. The final model then was evaluated by well data and compared with the conventional method of seismic inversion. Result of the proposed strategy in genetic inversion depicted improvement in final acoustic impedance model and porosity distribution model.

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2019-12-01
2026-01-12
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References

  1. Al bulushi, N.I., King, P.R., Blunt, M.J., and Kraaijveld, M., (2012) Artificial neural networks workflow and its application in the petroleum industry: Neural comp and appl, 21 (3), 409-421 https://doi.org/10.1007/s00521-010-0501-6.
  2. Chen, Y., and Durlofsky, L.J., (2006) Adaptive local-global upscaling for general flow scenarios in heterogeneous formations, Trans Por Med, 62(2), 157-185. https://doi.org/10.1007/ s11242-005-0619-7.
  3. Balouchi, S., Moradi, S., Masihi, M., and Erfaninia, A.A., (2013) A Novel combinatorial approach to discrete fracture network modeling in heterogeneous media. Iranian Journal of Oil and Gas Science and Technology, 2(1), 42-56. https://doi.org/10.22050/IJOGST.2013.3037
  4. Nozohour-leilabady, B. and Fazelabdolabad, B. (2015), On the application of Artificial Bee Colony (ABC) algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the Particle Swarm Optimization (PSO) methodology. Petroleum, 2(1), 79-82. https://doi.org/10.1016/j.petlm.2015.11.004
  5. Ahmadi M.A., Soleimani, R., Lee, M., Kashiwao, T., and Bahadori A.R., (2015) Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool. Petroleum. 1 (2), 118-132. https://doi.org/10.1016/j.petlm.2015.06.004
  6. Daraei M, Bayet-Golla A, Ansari M, (2017) An integrated reservoir zonation in sequence stratigraphic framework: A case from the Dezful Embayment, Zagros, Iran. J of Petro Sci and Eng. 154, 389-404, https://doi.org/10.1016/j.petrol.2017.04.038
  7. Maffucci R, Bigi S, Corrado S, Chiodi A, Di Paolo L, Giordano G, Invernizzi C, (2015) Quality assessment of reservoirs by means of outcrop data and discrete fracture network models: The case history of Rosario de La Frontera (NW Argentina) geothermal system. Tectonophysics, https://doi.org/10.1016/j.tecto.2015.02.016
  8. Soleimani M, (2017b), Well performance optimization for gas lift operation in a heterogeneous reservoir by fine zonation and different well type integration. J of Nat Gas Sci and Eng, 40, 277-287. http://dx.doi.org/10.1016/j.jngse.2017.02.017
  9. Noorbakhsh SS, Rasaei MR, Heydarian A, Behnaman H, (2014) Single-phase near-well permeability upscaling and productivity index calculation methods. Iranian J of Oil and Gas Sci and Tech, 3(4), 55-66. https://doi.org/10.22050/IJOGST.2014.7522
  10. Oyeyemi KD, Olowokere MT, Aizebeokhai AP (2019). Prospect analysis and hydrocarbon reservoir volume estimation in an exploration field, shallow offshore Depobelt, western Niger delta, Nigeria. Nat Resour Res., 28(1), 173-185. https://doi.org/10.1007/s11053-018-9377-4
  11. Soleimani M, (2017), Naturally fractured hydrocarbon reservoir simulation by elastic fractures modeling. Petro Sci, 14, 286–301. https://doi.org/10.1007/s12182-017-0162-5
  12. Abdel-Rasoul RR. Daoud A. El-Tayeb E.S.A. (2014) Production allocation in multi-layers gas producing wells using temperature measurements with the application of a genetic algorithm, Pet. Sci. Tech. 2014; 25-28 https://doi.org/10.1080/10916466.2011.586958
  13. Ahmadi MA. Soleimani R. Lee M. Kashiwao T. Bahadori AR. (2015) Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool. Pet., 1(2), 118-132; https://doi.org/10.1016/j.petlm.2015.06.004
  14. Iliev O. Rybak I. (2008) On numerical upscaling for flows in heterogeneous porous media. Comp. Meth. In App. Math. 8(1), 60-76, https://doi.org/10.2478/cmam-2008-0004.
  15. Guerriero V, Mazzoli S, Iannace A, Vitale S, Carravetta A, Straussc C. A., (2012), Permeability model for naturally fractured carbonate reservoirs. Mar petrol Geol. 40, 115-134, https://doi.org/10.1016/j.marpetgeo.2012.11.002
  16. Vatandoust M, Farzipour Saein A, (2019) Fracture analysis of hydrocarbon reservoirs by static and dynamic well data, case study: The Aghajari oil field (the Zagros Fold-Thrust Belt). Develop in Struc Geol and Tect, 3, 1-16. https://doi.org/10.1016/B978-0-12-815048-1.00001-9
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