Modeling of Horizontal Extent of Pay zone Layer on the Basis of Petrophysical Parameters Variations Using Indicator Kriging Method in one of the Southwest Iranian Oil Fields
Subject Areas : Petrophysics
Farnaz Saberi
1
,
Farhad Mohammad Torab
2
,
Kioumars Taheri
3
1 - M.Sc. in Petroleum Engineering Exploration, Department of Mining and Metallurgical Engineering, Yazd University, Yazd, Iran
2 - Associate Professor, Department of Mining and Metallurgical Engineering, Yazd University, Yazd, Iran
3 - M.Sc. in Petroleum Engineering Exploration, Department of Mining and Metallurgical Engineering, Yazd University, Yazd, Iran
Keywords: Petrophysical ParametersGeostatisticsIndicator KrigingProduction ZoneSouthwest Oil basin,
Abstract :
Determining the position of the production zone is one of the best ways to reduce drilling costs as well as quick access to the reservoir and optimal production of hydrocarbon resources. The purpose of this study, is to estimate the porosity, water saturation and thickness of an oil field in Iran’s southwestern Basin , and ultimately to reach the production zone. Therefore, according to the data obtained from 76 wells of this field, variation of reservoir petrophysical parameters were modeled with variogram operation and using Geostatistical methods. By using ordinary Kriging method, the values of the parameters were estimated in the whole field. Subsequently, by using the indicator kriging method, the field boundaries were separated in order to obtain the exact area of the oil zone and the volume of oil in place and finally by considering the 80% probability level, The definite boundary of the presence of the production zone was determined in the block model and In this zone the volume of reservoir’s hydrocarbon was estimated about 147/5 million cubic foot.
1. Taheri, K. and A.H. Morshedy, Three-dimensional Modeling of Mud Loss Zones Using the Improved Gustafson-Kessel Fuzzy Clustering Algorithm (Case Study: One of the South-western Oil Fields). Journal of Petroleum Research, 2017. 27(96-5): p. 82-97.
2.
Verly, G., Grade Control Classification of Ore and Waste: A Critical Review of Estimation and Simulation Based Procedures. Mathematical Geology, 2005. 37(5): p. 451-475. 3. Taheri, K. and F. Mohammad Torab, Applying Indicator Kriging in Modeling of Regions with Critical Drilling Fluid Loss in Asmari Reservoir in an Oil Field in Southwestern Iran. Journal of Petroleum Research, 2017. 27(96-4): p. 91-104
. 4. Abraham, A.B., A.B. Evans, and B.E. Thompson, Analysis of spatial distribution pattern of reservoir petrophysical properties for horizontal well performance evaluation-a case study of reservoir X. The Open Petroleum Engineering Journal, 2019. 12(1)
. 5. Essien, U.E., A.O. Akankpo, and O.E. Agbasi, Evaluation of Reservoir’s Petrophysical Parameters, Niger Delta, Nigeria. International Journal of Advanced Geosciences, 2017. 5(1): p. 19-25.
6. Alli, M.M., E.A. Nowatzki, and D.E. Myers, Probabilistic analysis of collapsing soil by indicator kriging. Mathematical Geology, 1990. 22(1): p. 15-38
. 7. Taheri, K., et al., Correction Investigating the Design of Casing Pipes Using Drilling Data Analysis in Bangestan Wells, One of the Oil Fields in the Southwest of Iran. Journal of Petroleum Geomechanics, 2018. 2(1): p. 41-54
. 8. Chabock, R., M. Riahi, and M. Memariani, Determination of the Petrophysical parameters using geostatistical method in one of the hydrocarbon reservoirs in South West of Iran. J Sci Eng Res, 2017. 4(12): p. 44-55
. 9. Nezhad, H.K. and H. Tabatabaei, Simulation of Petrophysical Parameters of Asmari Reservoir Using SGS Method in Mansuri Oil Field, Southwest of Iran. Open Journal of Geology, 2017. Vol.07No.08: p. 12
. 10. Khan, N. and K. Rehman, Petrophysical evaluation and fluid substitution modeling for reservoir depiction of Jurassic Datta Formation in the Chanda oil field, Khyber Pakhtunkhwa, northwest Pakistan. Journal of Petroleum Exploration and Production Technology, 2019. 9(1): p. 159-176
. 11. Kiakojury, M., S.J.S. Zakariaei, and M.A. Riahi, Investigation of Petrophysical Parameters of Kangan Reservoir Formation in One of the Iran South Hydrocarbon Fields. Open Journal of Yangtze Oil and Gas, 2018. 3(01): p. 36
. 12. Kamali, M.R., A. Omidvar, and E. Kazemzadeh, 3D geostatistical modeling and uncertainty analysis in a carbonate reservoir, SW Iran. Journal of Geological Research, 2013. 2013
. 13. Kasmaee, S. and F. Torab, Risk reduction in Sechahun iron ore deposit by geological boundary modification using multiple indicator Kriging. Journal of central south university, 2014. 21(5): p. 2011-2017
. 14. Khan, K.D., O. Leuangthong, and C.V. Deutsch, Solved problems in geostatistics. 2011: John Wiley & Sons. 15
. Marinoni, O., Improving geological models using a combined ordinary–indicator kriging approach. Engineering Geology, 2003. 69(1): p. 37-45. 16. Nabikhani, N., et al., The Evaluation of Reservoir Quality of Sarvak Formation in One of Oil Fields of the Persian Gulf. Journal of Petroleum Science and Technology, 2012. 2(1): p. 3-15
. 17.
Sha, F., et al., Petrophysical characterization and fractal analysis of carbonate reservoirs of the eastern margin of the pre-Caspian Basin. Energies, 2018. 12(1): p. 78. 18. Yeten, B. and F. Gümrah, The Use of Fractal Geostatistics and Artificial Neural Networks for Carbonate Reservoir Characterization. Transport in Porous Media, 2000. 41(2): p. 173-195
. 19. Zhao, P., et al., Methods for estimating petrophysical parameters from well logs in tight oil reservoirs: a case study. Journal of Geophysics and Engineering, 2016. 13(1): p. 78-85
. 20. Zekri, A.R.Y., S.A. Shedid, and R.A. Almehaideb, Experimental investigations of variations in petrophysical rock properties due to carbon dioxide flooding in oil heterogeneous low permeability carbonate reservoirs. Journal of Petroleum Exploration and Production Technology, 2013. 3(4): p. 265-277
.
