Integration of Geological and Dynamic data for Constructing 3D Geological Models: An IOR Study from the Ahwaz Field, SW Iran

Mohit Khanna¹, Arne Linjordet¹, Torgrim Jacobsen², Tor Røsaasen¹, Sigurd Haugen¹, Mohammad Sharafoddin³, Ahmad Miryaan^4
1 Statoil ASA, 4035, Stavanger, Norway ([email protected])
² Statoil ASA, Arkitekt Ebbellsvei 10, Rotvoll, N-7005 Trondheim, Norway
³ RIPI, Tehran
⁴ NISOC, Ahwaz

An IOR study has been performed on the Asmari formation of the Ahwaz Field. As a part of this study a 3D geocellular model was constructed based on reservoir characterization from 338 wells and a regional interpretation of the depositional systems of the Asmari Formation The model was used as a basis to history match 40 years of production history and to predict the outcome of different drainage strategies. This paper describes the construction of the 3D geological model.

The Ahwaz Field spans 420 km² in the Zagros area of SW Iran with 400m thick Asmari Formation as the main oil-producing reservoir unit. It consists of interbedded sandstone, shale and carbonate intervals of Oligocene to Miocene age. 3D geomodeling was performed on a simulation grid design with more than one million cells to avoid upscaling, thereby saving time, cost and avoid mathematical inaccuracies. The main input to the model was core data, petrophysical log interpretation and dynamic data. In the current model a multistage modeling technique was used to use all the wells in the Ahwaz Field. Another unique technique that has been implemented is the use of production data to constrain the distribution of sands and carbonates. The production data used comprises of PLT, PI, and perforations.

Preprocessing included the generation of a bias log from all the production dataset. A 3D parameter was generated from this log by interpolation to co-condition the carbonates in object modeling. This bias log was also used during the upscaling of the well data at grid resolution. A 3D shale volume parameter generated from more than 300 wells by interpolating, was used as a co-conditional parameter for locating shales in the 3D geomodel.

A general marked point process algorithm was used to model the lithofacies picked from wireline log interpretation. Core descriptions provided the input for the definition of lithofacies.

Petrophysical modeling was performed on the results of the object modeling to simulate effective porosity and log derived permeability. Water saturation was modeled using a J-function for different lithofacies.

The resulting simulation model matched the vertical static pressure and major saturation distribution for 228 wells quickly during initialization.