AAPG GEO 2010 Middle East
Geoscience Conference & Exhibition
Innovative Geoscience Solutions – Meeting Hydrocarbon Demand in Changing Times
March 7-10, 2010 – Manama, Bahrain
Geology Meets Petrophysics: from Example of a Process-Based Rock Type Methodology for a Khuff Reservoir, North Oman
(1) Study Centre, Petroleum Development Oman, Muscat, Oman.
(2) Shell Technology India, Bangalore, India.
(3) Woodside Energy, Perth, WA, Australia.
(4) Badley Ashton and Associates, Horncastle, United Kingdom.
Carbonate reservoirs are inherently heterogeneous as result of being deposited in laterally variable settings with subsequent overprint of complex and substantial diagenetic processes. The Upper Khuff carbonate reservoir, deposited on a large carbonate/evaporitic ramp (Late Permian - Early Triassic), is a prime example for such reservoir quality variability dictated by its intrinsic carbonate nature.
A Rock Type (RT) “process-based” geological-petrophysical approach has been applied to a Upper Khuff reservoir in North Oman in order to improve the reservoir characterization and the understanding of its heterogeneity, and ultimately to derive representative static and dynamic models. This approach is based on the integration of sedimentological/petrographic core observations and the analysis of petrophysical properties in wells.
A core-based Rock Type scheme was defined based on Porosity/Permeability from CCA, pore type identification and sedimentological observation on thin sections. Each RT was linked to the original depositional facies and the subsequent diagenetic processes (cementation, dissolution, dolomitization, anhydrite precipitation). Pore throat distribution and capillary pressure curves were also used to calibrate the classification scheme. RTs with enhanced properties correspond to two classes: dolomitized oolitic-skeletal Grainstones and Thrombolites (with intercrystalline, interparticle/mouldic and vuggy porosity) and oolitic/peloidal/skeletal Grainstones and Packstones (with mouldic, interparticle and vuggy porosity).
A Neural Network approach was used to implement this RT scheme to all wells, and its results were calibrated to cores. An electro-facies log was obtained by combining Gamma Ray, Porosity and BVSxo logs. This results in a ‘Depositional Facies’ log, comprising: Bioconstructed facies, Grainstones (well and poorly sorted), Packstones to Mudstones and Argillaceous Mudstone. The ‘Lithology’ log was generated from the Gamma Ray, Neutron/Density and PE logs to distinguish amongst Limestone, Dolostone, Anhydrite and Argillaceous Mudstone. The final RT log was generated by merging ‘Depositional Facies’ and ‘Lithology’ logs.
‘Depositional Facies’ and ‘Lithology’ logs were used to build 3D Facies properties (Depositional Facies and Lithology). The latter were directly merged to generate RT models. Porosity and Permeability were modelled conditioned to RTs which resulted in more realistic distributions.