화학공학소재연구정보센터
AAPG Bulletin, Vol.92, No.11, 1457-1478, 2008
Fault facies modeling: Technique and approach for 3-D conditioning and modeling of faulted grids
Faults in nature commonly affect surrounding rock volumes and can as such be described as fault envelopes with a given internal geometry and architecture. Modeling techniques currently employed when modeling faults in petroleum reservoirs are mostly two-dimensional (2-D); hence, a need is present for more accurate and realistic description and quantification of deformational architectures and properties to accurately predict fluid flow in fault zones. Fault facies (FF) modeling is a concept for three-dimensional (3-D) fault zone characterization, facies modeling of fault rocks and fluid flow simulation, which is presented here and demonstrated by the use of a synthetic fault model. FF modeling is performed by first generating a 3-D grid of the fault envelope, which includes the conventional fault plane. Second, a kinematic strain calculation is executed in the FF grid. The strain parameter is used to calculate a fault product distribution factor (FPDF), which describes the fault displacement in the fault envelope. This parameter together with strain distribution is subsequently used to condition the fault model for facies modeling. Finally, FF modeling is executed. To achieve adequate flexibility and realism, pixel-based modeling is combined with object-based modeling methods to populate the FF grid with facies. This synthetic model shows that it is possible to honor structural outcrop observations in fault zones, and FF modeling is able to produce realistic looking fault zone deformation structures in 3-D. It is possible to implement faults with varying width and displacement, although the FF grid itself has a regular fixed width. This is highly advantageous as compared to controlling the fault geometry with the grid itself. We propose that FF modeling can improve fault zone characterization and also capture fluid flow uncertainty in fault zones in a more realistic way than is possible with 2-D methods.