FRACTURE NETWORK MODELING FOR SHALE ROCKS, CASE STUDY FROM BALTIC BASIN

Abstract

The discrete fracture network (DFN) approach is attractive for applications in which the geometry and properties of discrete fractures play a significant role in geomechanics, and resource assessment. (Dershowitz & Doe 1988). Comparison of the simulated data to realistic fractures observed on cores increases confidence in the DFN approach. The aim of this study is prediction of fracture properties for the Lower Paleozoic shale rocks.

A DFN model typically combines deterministic and stochastic discrete fractures. The deterministic fractures are those directly imaged through seismic or intersected in wells. Other, usually smaller-scale fractures may not have been detected through seismic, yet may be very important for reservoir performance. These fractures are generated stochastically (Parney et al. 2000).

The input data include seismic survey data, well logs with FMI interpretation and was calibrated with measurements and observations on the cores to help ensure accuracy in the estimates of fractures properties. This study was performed using Petrel software from Schlumberger. Typical workflows for modelling of oil and gas reservoirs were applied (e.g. Zakrevsky 2011). The result was 3D fracture distribution model consisted with four zones. In each zone two generations of fractures were created based on well log data. Several seismic attributes were considered as a fracture density drivers for the spatial modeling. Finally the ant tracking structural attribute was chosen as the best indicator of faults and fractures in seismic cube. For the improve the quality of the DFN model, should define the local stress distributions within each tectonic blocks.