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Reservoir Characterization and Reservoir Quality Prediction in Deepwater Turbidite Sandstones, Niger Delta


Macaulay, Calum1, Irene Espejo1, Chris Wojcik2, Charles Anowai3 (1) Technology Applications & Research, Houston, TX (2) EPT-Solutions, Houston, TX (3) EPT-Solutions, Rijswijk, Netherlands


Reservoir quality is a key parameter determining the economic viability of deepwater prospects. Assessment of reservoir quality is usually based on analogue core data. However, full diameter conventional core data is sparse in the deepwater Niger Delta area, leaving much uncertainty in reservoir quality prediction in exploration settings. This uncertainty can be reduced by application of a multidisciplinary approach that integrates data from cores, basin modeling, biostratigraphy and detailed seismic observations to understand and pre­dict the quality of turbidite reservoirs away from well control.

Data were generated using thin sections from representative sand petrofacies identified in available cores. Detailed, quantitative petrographic analyses were performed on all sam­ples. Three major sand petrofacies include: moderately to well sorted fine-grained sands (Tc), moderately sorted medium-grained sands (Ta-Tb) and poorly sorted coarse-grained to pebbly sands (S). Burial history models for control wells were constrained in some cases with fluid inclusion temperatures.

Petrographic and thermal history data were used to calibrate a Touchstone reservoir quality model. The model considers the possible impact of facies-dependent textural vari­ability and differences in diagenetic history, recognized in previous studies of the deepwater Niger Delta. Model results establish parameters that can be utilised to make quantitative pre­dictions of porosity, quartz cement abundance, and permeability prior to drilling. The pre­drill assessment of reservoir quality is carried out in a context of basin-wide stratigraphic and thermal history models. Results are thus consistent with the assessment of other risks and uncertainties at undrilled prospects and can be used in quantification of risk modifiers.