Hydrocarbon Prospectivity in the undrilled area of AIMA Field in the Niger Delta Basin, Nigeria.

  • Alexander Ogbamikhumi Department of Geology, faculty of Physical Sciences, University of Benin, Nigeria
  • John Elvis Ighodalo Department of Physics, faculty of Physical Sciences, University of Benin, Nigeria
Keywords: Rock Physics, Neural Network, Reservoir Properties, Cross-plot Analysis, Seismic Inversion

Abstract

Field development is a very costly endeavor that requires drilling several wells in an attempt to understanding potential prospects. To help reduce the associated cost, this study integrates well and seismic based rock physics analysis with artificial neural network to evaluation identified prospects in the field.

 Results of structural and amplitude maps of three major reservoir levels revealed structural highs typical of roll over anticlines with amplitude expression that conforms to structure at the exploited zone where production is currently ongoing. Across the bounding fault to the prospective zones, only the D_2 reservoir possessed the desired amplitude expression, typical of hydrocarbon presence. To validate the observed amplitude expression at the prospective zone, well and seismic based rock physics analyses were performed. Results from the analysis presented Poisson ratio, Lambda-Rho and Lambda/Mu-Rho ratio as good fluid indicator while Mu-Rho was the preferred lithology indicator.

 These rock physics attributes were employed to validate the observed prospective direct hydrocarbon indicator  expressions on seismic. Reservoir properties maps generated for porosity and water saturation prediction using Probability Neural Network gave values of 20-30% and 25-35% for water saturation and porosity respectively, indicating  the presence of good quality hydrocarbon bearing reservoir at the prospective zone.

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References

Abdel-Fattah, M.I., Pigott, J.D., El-Sadek, M.S., 2020. Integrated seismic attributes and stochastic inversion for reservoir characterization: Insights from Wadi field (NE Abu-Gharadig Basin, Egypt). J. African Earth Sci. 161, 103661.

Adeoti, L., Adesanya, O.Y., Oyedele, K.F., Afinotan, I.P., Adekanle, A., n.d. Geosciences Journal GJ Lithology and fluid prediction from simultaneous seismic inversion over Sandfish field, Niger Delta, Nigeria.

Adojoh, O., Marret, F., Duller, R., Osterloff, P., n.d. Tropical palaeovegetation dynamics, environmental and climate change impact from the low latitude coastal offshore margin, Niger Delta, Gulf of Guinea.

Asquith, G., Krygowski, D., Gibson, C., 2004. Basic well log analysis.

Castagna, J.P., Swan, H.W., 1997. Principles of AVO crossplotting. Lead. Edge (Tulsa, OK) 16, 337.

Chatterjee, S., Burreson, M., Six, B., Michel, J.M., 2016. Seismically derived porosity prediction for field development-An onshore Abu Dhabi jurassic carbonate reservoir case study, in: Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference 2016. Society of Petroleum Engineers.

Stacher, P., 1995, undefined, n.d. Present understanding of the Niger Delta hydrocarbon habitat. pascal-francis.inist.fr.

Edwards, J., Santogrossi, P., 1990. Divergent/passive margin basins.

Leite, E., Geosciences, A.V.-C.&, 2011, undefined, n.d. 3D porosity prediction from seismic inversion and neural networks. Elsevier.

Li, E.Y., 1994. Artificial neural networks and their business applications. Inf. Manag. 27, 303–313.

Mokhtari, M., Jalalifar, H., Alinejad-Rokny, H., Afshary, P.P., 2011. Prediction of permeability from reservoir main properties using neural network. Sci. Res. Essays 6, 6626–6635.

Ogbamikhumi, A., Imasuen, O.I., Omoregbe, O.I., 2408. INVERSION FEASIBILITY STUDY FOR RESERVOIR CHARACTERIZATION OF OSI FIELD ONSHORE NIGER DELTA BASIN, FUW Trends in Science & Technology Journal, www.ftstjournal.com e-ISSN.

Omudu, L., Physics, J.E.-N.J. of, 2005, undefined, n.d. Cross-plotting of rock properties for fluid discrimination using well data in offshore Niger Delta.

recorder, B.G.-C., 2001, undefined, 2001. AVO and Lamé constants for rock parameterization and fluid detection, 74.3.176.63.

Short, K., bulletin, A.S.-A., 1967. Outline of geology of Niger Delta.

Toshev, M., 2017. Study Of The Capabilities Of Avo-Methods For The Detection Of Hydrocarbon Accumulations, Journal Of Mining And Geological Sciences.

Zoeppritz, K., 1919. earthquake waves vii. News from Soc. Sci.

Published
2021-03-24
Section
Research Articles
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