Development of Fracture Models for Korolev Field: Characterization to Simulation

Abstract

Abstract Korolev field is a large Devonian-Carboniferous carbonate buildup with a flow system dominated by natural fractures. Currently TCO is looking into potential IOR opportunities at Korolev field, which might help to unlock additional resources beyond the scope of current development plans. Therefore, characterization and modeling of the fracture system is of fundamental importance for a new flow- simulation model to assess and predict IOR performance. The fracture modeling workflow closely integrates matrix and fracture modeling, which facilitates identification of important parameters for fracture distribution early in the modeling process. Fracture prediction is based on correlations with various geological parameters, such as stratigraphy, depositional facies, mechanical properties and geomorphological features, which provides a soft probability trend for distribution of fracture parameters. Fracture network characterization based on analysis of well log and core data only is very limited in scale. Pressure Transient Tests (PTT) and Pulse Tests provide important insights into characteristics of fracture network at the larger scale than the conventional wireline data allows. Therefore, it is important to incorporate dynamic dataset as a fracture characterization constraint during modelling of fracture distribution. Most of the wells at Korolev field have good quality pressure buildup and pulse test data. TCO developed a workflow to incorporate dynamic data into the fracture modeling process for the full- field dual porosity, dual permeability (DPDK) model. The first step in the workflow is to calibrate fracture density distribution to match well productivity indices (PI) observed in the field. The next step involves dynamic simulation of pressure buildup tests and their comparison to the actual measured data. The last step is to validate the geologic model with available pulse test data. Dynamic data integration required multiple iterations and loopbacks to fracture characterization and property distribution. Close collaboration between fracture experts, earth scientists and reservoir engineers along the whole process was essential for successful implementation of dynamic data into fracture characterization and modeling. Calibration with the available dynamic data led to better understanding of spatial distribution of fracture properties and provided important additional constraint for the fracture model construction. Improved fracture model at Korolev is the key factor for more reliable production forecasts and evaluation of future development opportunities.