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AAPG GEO 2010 Middle East
Geoscience Conference & Exhibition
Innovative Geoscience Solutions – Meeting Hydrocarbon Demand in Changing Times
March 7-10, 2010 – Manama, Bahrain

Use of Pre-Stack Seismic Data to Guide the 3D Rock-Type Distribution of Arab-D in Maydan Mahzam High-Resolution Geological Model

Ahmed A. Mandani Al-Emadi2; Scott Robinson1; Nizar Jedaan2; Nicolas Desgoutte3; Marie-Stéphanie Blum3; Gaël Lecante3; Bruno Caline1; Christian Fraisse1

(1) Total, Paris, France.

(2) Qatar Petroleum, Doha, Qatar.

(3) Beicip-Franlab, Rueil-Malmaison, France.

Maydan Mahzam is a carbonate field located offshore Qatar with principal oil production from the Arab-D reservoir. In order to optimize the management of this mature field, Qatar Petroleum undertook a comprehensive reservoir modeling exercise using an up-to-date integrated multi-disciplinary approach, combining reservoir, geological, geophysical and production data. This paper illustrates how seismic data has been used to quantitatively constrain the reservoir model between wells.

The main objective of the seismic reservoir characterization part of the project is to retrieve a robust distribution of reservoir properties between wells from pre-stack seismic data. This will provide a better control of uncertainties in petrophysical property distribution (namely porosity and dominant lithology probability) away from the wells.

As a first step, a comprehensive geophysical well database has been generated in parallel to the rock-typing phase of the project in order to maximize the consistency between Reservoir Rock-type definition and upscaled reservoir properties which can be predicted or discriminated from seismic data. In practice, this also means that elastic parameters (compressional and shear velocities and impedances) have been linked with the Rock-Type definition schemes.

Pre-stack inversion has then been performed to produce optimized P and S impedance volumes. The combination of inversion results with the previous petro-elastic analysis allows defining the training database to be used for dominant lithology discrimination and porosity prediction. Supervised discriminant analysis has been used to discriminate dominant dolomite from dominant limestone (i.e. at seismic scale) and associated probability of occurrence. As a second stage, lithology-dependant Porosity vs. Impedance relationship has been calibrated at wells.

As a result, volumes of probability of occurrence of dolomite and porosity volumes have been used to derive an average Dolomite occurrence probability map for Arab D interval DI and average PIGE (to be put in full) maps for Arab D intervals DII, DIIIA and DIIIB. These average maps have been generated using surfaces of the high-resolution geological model converted back to time, to ensure the topological consistency between the constraints extracted from seismic data and the geological model.