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Understanding reservoir heterogeneity using variography and data analysis: an example from coastal swamp deposits, Niger Delta Basin (Nigeria)

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Języki publikacji
EN
Abstrakty
EN
For efficient reservoir management and long-term field development strategies, most geologists and asset managers pay special attention to reservoir chance of success. To minimise this uncertainty, a good understanding of reservoir presence and adequacy is required for better ranking of infill opportunities and optimal well placement. This can be quite challenging due to insufficient data and complexities that are typically associated with areas with compounded tectonostratigraphic framework. For the present paper, data analysis and variography were used firstly to examine possible geological factors that determine directions in which reservoirs show minimum heterogeneity for both discrete and continuous properties; secondly, to determine the maximum range and degree of variability of key reservoir petrophysical properties from the variogram, and thirdly, to highlight possible geological controls on reservoir distribution trends as well as areas with optimal reservoir quality. Discrete properties evaluated were lithology and genetic units, while continuous properties examined were porosity and net-to-gross (NtG). From the variogram analysis, the sandy lithology shows minimum heterogeneity in east-west (E–W) and north-south (N–S) directions, for Upper Shoreface Sands (USF) and Fluvial/Tidal Channel Sands (FCX/TCS), respectively. Porosity and NtG both show the least heterogeneity in the E–W axis for reservoirs belonging to both Upper Shoreface and Fluvial Channel environments with porosity showing a slightly higher range than NtG. The vertical ranges for both continuous properties did not show a clear trend. The Sequential Indicator Simulation (SIS) and Object modelling algorithm were used for modelling the discrete properties, while Sequential Gaussian Simulation (SGS) was used for modelling of the continuous properties. Results from this exercise show that depositional environment, sediment provenance, topographical slope, sub-regional structural trends, shoreline orientation and longshore currents, could have significant impacts on reservoir spatial distribution and property trends. This understanding could be applied in reservoir prediction and for generating stochastic estimates of petrophysical properties for nearby exploration assets of similar depositional environments.
Czasopismo
Rocznik
Strony
207--218
Opis fizyczny
Bibliogr. 21 poz.
Twórcy
  • Mobil Producing Nigeria Unlimited, Lagos, Nigeria
  • Department of Geology, University of Nigeria, Nsukka, Enugu State, Nigeria
  • Shell Petroleum Development Company, Port Harcourt, Nigeria
  • Department of Geology, University of Nigeria, Nsukka, Enugu State, Nigeria
Bibliografia
  • Al-Dhafeeri, A.M. & Nasr-El-Din, H.A., 2007. Characteristics of high permeability zones using core analysis and production logging data. Journal of Petroleum Science and Engineering 55, 13–25.
  • Amaefule, J.O., Altunbay, M., Tiab, D., Kersey, D.G. & Keelan, D.K., 1993. Enhanced Reservoir Description: Using core and log data to identify hydraulic (flow) units and predict permeability in uncored intervals/wells. 68th SPE Annual Technical Conference and Exhibition, Houston, USA. Paper SPE-26436, 16 pp.
  • Cabrera-Garzon, R., Arestad, J.F., Dagdelen, K. & Davis, T.L., 1997. Geostatistical simulation of reservoir porosity distribution from 3-D, 3-C seismic reflection and core data in the Lower Nisku Formarion at Joffre Field, Alberta. [in:] J.A. Kupez, J. Gluyas & S. Bloch (Eds), Reservoir quality prediction in sandstones and carbonates. American Association of Petroleum Geologists Memoir 60, 115–125.
  • Delbari, M., Afrasiab, P. & Loiskandl, W., 2009. Using sequential Gaussian simulation to assess the field-scale spatial uncertainty of soil water content. Catena 79, 163–169.
  • Deutsch, C. & Journel, A.G., 1998. GSLIB: Geostatistical Software Library and User’s Guide. 2nd edition. Oxford University Press, 340 pp.
  • Dimitrakopoulos R., & Luo, X., 2004. Generalized sequential Gaussian simulation on group size ν and screen-effect approximations for large field simulations. Mathematical Geology 36, 567–591.
  • Doust, H., & Omatsola, E., 1990. Niger Delta [In:] J.D. Edwards & P.A. Santogrossi (Eds): Divergent/passive margin basins. American Association of Petroleum Geologists Memoir 48, 239–248.
  • Evamy, B.D., Haremboure, J., Kamerling, P., Knaap, W.A., Molloy, F.A. & Rowlands, P.H., 1978. Hydrocarbon habitat of Tertiary Niger Delta. American Association of Petroleum Geologists Bulletin 62, 277–298.
  • Gomez–Hernandez, J.J. & Journel, A.G., 1993. Joint sequential simulation of multigaussian fields. [In:] A. Soares (Ed.): Geostatistics Troia ‘92, Kluwer Academic Publ., 85–94.
  • Hooper, R.J., Fitzsimmons, R.J., Grant, N. & Vendeville, B.C., 2002. The role of deformation in controlling depositional patterns in the south-central Niger Delta, West Africa. Journal of Structural Geology 24, 847–859.
  • Lawrence, S.R., Munday, S. & Bray, R., 2002. Regional geology and geophysics of the eastern Gulf of Guinea (Niger Delta to Rio Muni). The Leading Edge 21, 1112–1117.
  • Lehner, P., & De Ruiter, P.A.C., 1977. Structural history of Atlantic Margin of Africa. American Association of Petroleum Geologists Bulletin 61, 961–981.
  • Leuangthong, O., McLennan, J.A, & Deutsch, C.V., 2004. Minimum acceptance criteria for geostatistical realizations. Natural Resources Research 13, 131–141.
  • Mitchum, R.M., Sangree, J.B., Vail, P.R. & Wornardt, W.W., 1994. Recognizing sequences and systems tracts from well logs, seismic data and biostratigraphy: Examples from the Late Cenozoic of the Gulf of Mexico. American Association of Petroleum Geologists Memoir 58, 163–197.
  • Obi. I.S. & Mode, A.W., 2011. Geologic controls on reservoir architecture and heterogeneity: Example from braided river deposits in SE Nigeria. Nigerian Association of Petroleum Explorationists Bulletin 23, 72–87.
  • Obi, I.S. & Onuoha, K.M., 2017. Lithology and reservoir facies identification using data integration: An example from the ‘Cissero’ Field, Onshore Niger Delta Basin. [In:] K.M. Onuoha (Ed.): Advances in Petroleum Geosciences Research in Nigeria – Basin Analysis and Reservoir Characterization Studies. Utopia Publishing, Lagos, 33–45.
  • Obi, I.S., Onuoha, K.M., & Obilaja, O.T., 2017. Prediction of reservoir properties using Crossplot Trends: An example from Coastal Swamp Depobelt of the Niger Delta Basin, Nigeria. [In:] K.M. Onuoha (Ed.): Advances in Petroleum Geosciences Research in Nigeria – Basin Analysis and Reservoir Characterization Studies. Utopia Publishing, Lagos, 20–32.
  • Soltani, F., Afzal, P. & Asghari, O., 2013. Sequential Gaussian simulation in the Sungun Cu porphyry deposit and comparing the stationary reproduction with ordinary kriging. Universal Journal of Geoscience 1, 106–113.
  • Stacher, P., 1995. Present understanding of the Niger Delta hydrocarbon habitat. [In:] M.N. Oti & G. Postma, G. (Eds): Geology of Deltas: Balkema, Rotterdam, 257–267.
  • Tanmay, C., 2008. Permeability estimation using flow zone indicator from well log data. 7th International Conference and Exhibition on Petroleum Geophysics, Hyderabad, 7 pp.
  • Uguru, C.I., Onyeagoro, O.U., Lin, J. Okkerman, J. & Sikiru, I.O., 2005. Permeability prediction using Genetic Unit Average of Flow Zone Indicators (FZIs) and Neural Networks. Society of Petroleum Engineers Annual Conference, Abuja, Nigeria, paper 98828, 8 pp.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-68a00d14-e28a-458d-b9c9-d213b61a5566
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