Reservoir Fluid Effect Recognition and Discrimination in the Niger Delta Using AVO-Derived Elastic Parameters and Poisson’s Ratio

Knowledge of reservoir fluid such as hydrocarbon and brine is very critical in reservoir fluid study and need to be well understood for successful reservoir evaluation. The aim is to isolate the hydrocarbon content for effective hydrocarbon optimization in new and marginal fields. This was achieved through in-depth study of the well logs and Amplitude versus offset (AVO) fluid effect. Zoeppritz equation was used to produce synthetics for AVO modeling while two-terms Aki-Richard approximation equations of Zoeppritz mathematical relation were used to generate various AVO volumes ranging from Intercept (A), gradient (B) and Scaled Poisson’s Ratio Change (aA+bB) volumes. Also, Fatti et al equation was equally utilised to generate various AVO volumes such as Rp, Rs and fluid factor cubes. The findings revealed that the well log analysis delineated the intervals characterised with hydrocarbon with high resistivity, low density, low p-wave etc. The AVO results showed that reservoir zones characterised with hydrocarbon exhibited high AVO responses compare to the encasing shale with brine effect or sandstone saturated with formation water which showed the lithological background AVO effect. The reservoirs were classified as type II and showed clearly on Scaled Poisson’s Ratio Change cube (units with hydrocarbon accumulations are characterised with hydrocarbon effect different from lithological response). Also, the Rp volume showed both lithology and fluid effect combined, Rs cube revealed only the effect due to lithology while fluid factor volume (△F = Rp – 0.58Rs) revealed the fluid content characterising each of the reservoir rocks. All these AVO volumes identified various hydrocarbon-charged units around and away from the drilled well locations thereby recognising critical oil and gas potential zones in the field. Therefore, results of the study indicate that various AVO cubes can be effectively deployed to recognize the various fluid contents of the reservoir for optimum hydrocarbon production using well logs.