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


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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