1887

Abstract

Summary

For any reservoir engineering issue or manage production from the petroleum reservoir, it is required to have seismic characterizations in quantitative manner, rather than qualitative geological interpretations. Herewith, seismic inversion could assist reservoir engineer as the technique to transform seismic data to quantitative rock properties. General steps in interpretation of seismic data preparing for porosity estimations consist of seismic structural interpretation, inversion procedure and attributes analysis. Since there is no direct measurement for the lithological parameters, they are to be computed from other geophysical logs or seismic attributes. This process also requires repeated intervention of the experts for fine tuning the prediction results. Standard regression methods are not suitable for this problem due to the high degree of the unknown nonlinearity. The problem is further complicated because of uncertainties associated with lithological units. In this context, Artificial Neural Network is considered to be useful tools to establish a mapping between lithological and well log properties. In this study, a strategy is presented for defining 3D seismic reservoir porosity model based on advanced method of artificial intelligence (AI) concept. This strategy then would be applied on a complex and heterogeneous oil reservoir which is a relatively symmetrical anticline whose trend is N-S. Required input data was prepared by seismic attribute and the velocity was modeled by vertical seismic profiling data. The general characterization strategy followed by initial inversion model construction for acoustic impedance of total cube for the target formation. Consequently, initial inversion model for effective and total porosity of the target formation was obtained. Acoustic impedance logs were used for neural network training and the genetic algorithm were used for calculation. High correlation values around 86% in cross plots, confirm accuracy of the porosity estimation by the AI method. This model then was used to precise the geological and geometrical properties of the reservoir for well location proposal.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201803052
2018-12-06
2020-08-13
Loading full text...

Full text loading...

References

  1. Aarnes, J.E.
    , (2004) On the use of a mixed multi-scale finite element method for greater flexibility and increased speed or improved accuracy in reservoir simulation, Multiscale Model. Simulation, 2 (3), pp. 421–439
    [Google Scholar]
  2. 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 computing and applications, 21 (3) pp.409–421
    [Google Scholar]
  3. AhmadiM.A., Soleimani, R., Lee, M., Kashiwao, T., and BahadoriA.R.
    , (2015) Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool. Petroleum. 1 (2), pp. 118–132.
    [Google Scholar]
  4. 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, 2013; 2(1), pp. 42–56.
    [Google Scholar]
  5. BisdomK, BertottG, NickHM
    . (2016), The impact of in-situ stress and outcropbased fracture geometry on hydraulic aperture and upscaled permeability in fractured reservoirs. Techtonophysics.; doi: 10.1016/j.tecto.2016.04.006
    https://doi.org/10.1016/j.tecto.2016.04.006 [Google Scholar]
  6. BoroH, RoseroE, BertottiG.
    (2014) Fracture-network analysis of the Latemar Platform (northern Italy): integrating outcrop studies to constrain the hydraulic properties of fractures in reservoir models. Petrol Geosci.; doi: 10.1144/petgeo2013‑007
    https://doi.org/10.1144/petgeo2013-007 [Google Scholar]
  7. Chen, Y., and Durlofsky, L.J.
    , (2006) Adaptive local-global upscaling for general flow scenarios in heterogeneous formations, Transport Porous Media, 62, pp. 157–185.
    [Google Scholar]
  8. Delalat, M., Kharrat, R.
    , (2013) Investigating the effects of heterogeneity, injection rate and water influx on GAGD EOR in naturally fractured reservoirs. Iranian Journal of Oil and Gas Science and Technology, 2(1), pp. 09–21.
    [Google Scholar]
  9. Emami, H.
    , (2008) Foreland propagation folding and structure of the mountain front flexure in the Pusht-e Kuh arc, NW Zagros, Iran. Ph. D. Thesis. Universitat de Barcelona Facultat de Geologia Departament de Geodinàmica y Geofísica.
    [Google Scholar]
  10. JangA, KimJ, ErtekinT, SungW
    . (2016), Fracture propagation model using multiple planar fracture with mixed mode in naturally fractured reservoir. J Petrol Sci Eng. 2016; doi: 10.1016/j.petrol.2016.02.015
    https://doi.org/10.1016/j.petrol.2016.02.015 [Google Scholar]
  11. Koike, K., Liu, C. Sanga, T.
    (2012), Incorporation of fracture directions into 3D geostatistical methods for a rock fracture system. Environ Earth Sci.; doi:10.1007/s12665‑011‑1350‑z
    https://doi.org/10.1007/s12665-011-1350-z [Google Scholar]
  12. LapponiF, CasiniG, SharpI, BlendingerW, FernándezN, RomaireI, HuntD.
    (2011) From outcrop to 3D modelling: a case study of a dolomitized carbonate reservoir, Zagros Mountains, Iran. Petrol Geosci.; doi: 10.1144/1354‑079310‑040.
    https://doi.org/10.1144/1354-079310-040 [Google Scholar]
  13. LeeCC, LeeCH, YehHF, LinHI
    . (2011), Modeling spatial fracture intensity as a control on flow in fractured rock. Environ Earth Sci.; doi: 10.1007/s12665‑010‑0794‑x
    https://doi.org/10.1007/s12665-010-0794-x [Google Scholar]
  14. MalinouskayaI, ThovertJF, MourzenkoVV, AdlerPM, ShekharR, AgarS, RoseroE, TsennM.
    (2014). Fracture analysis in the Amellago outcrop and permeability predictions. Petrol Geosci.2014; doi: 10.1144/petgeo2012‑094
    https://doi.org/10.1144/petgeo2012-094 [Google Scholar]
  15. MaffucciR, BigiS, CorradoS, ChiodiA, Di PaoloL, GiordanoG, InvernizziC.
    (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.; doi: 10.1016/j.tecto.2015.02.016
    https://doi.org/10.1016/j.tecto.2015.02.016 [Google Scholar]
  16. MasihiM, KingPR
    . (2007), A correlated fracture network: modeling and percolation properties, Water Resource Research; doi: 10.1029/2006WR005331
    https://doi.org/10.1029/2006WR005331 [Google Scholar]
  17. 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), pp. 79–82.
    [Google Scholar]
  18. Noorbakhsh, S.S., Rasaei, M.R., Heydarian, A., and Behnaman, H.
    , (2014) Single-phase near-well permeability upscaling and productivity index calculation methods. Iranian Journal of Oil and Gas Science and Technology, 3(4), pp. 55–66.
    [Google Scholar]
  19. Onwunalu, J.E., Durlofsky, L.J.
    , A new well pattern optimization procedure for large scale field development. SPE Journal16(3).
    [Google Scholar]
  20. Shadizadeh, S.R., and ZoveidavianpoorM.A.
    , (2009) Successful experience in optimization of a production well in a southern Iranian oil field. Iranian Journal of Chemical Engineering; 6(2), pp. 37–49.
    [Google Scholar]
  21. Soleimani, M.
    (2017a), Naturally fractured hydrocarbon reservoir simulation by elastic fractures modeling. Petroleum Science, 14, 286–301.
    [Google Scholar]
  22. (2017b), Well performance optimization for gas lift operation in a heterogeneous reservoir by fine zonation and different well type integration. Journal of Natural Gas Science and Engineering, 40, 277–287.
    [Google Scholar]
  23. Zhang, QH.
    , (2015) Finite element generation of arbitrary 3-D fracture networks for flow analysis in complicated discrete fracture networks. J Hydrol. doi: 10.1016/j.jhydrol.2015.08.065
    https://doi.org/10.1016/j.jhydrol.2015.08.065 [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201803052
Loading
/content/papers/10.3997/2214-4609.201803052
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error