Affiliation:
1. PetroChina Changqing Oil Company
2. SLB
Abstract
Abstract
The challenges of lacustrine shale oil reservoirs include not only heterogeneity of reservoir quality, but also the effect of natural fractures on propagation of hydraulic fractures and associated fracture hits. The intercalated sandstone and shale in a gravity deposit setting result in significant variation of the reservoir quality both vertically and horizontally. The subsurface conditions become even more complicated when the reservoirs are naturally fractured. This study aims to characterize the natural fractures with seismic data and diagnose hydraulic fractures with microseismic data, fracturing data, and simulation data.
An ant-tracking algorithm was used with variance of the seismic data as the input to delineate natural fractures and to build the natural fracture model. A geological model and a geomechanical model were built integrating all available data, including logs, core data, and seismic data. The quality check was performed by analyzing various data and comparing these data with the three geoscience models. The data included drilling data, such as the mud loss; microseismic data and fracturing data, such as the observed neighboring wellhead pressure and pressure decline G function analysis; and, eventually, production data. A distributed fracture network (DFN) model was built with the ant-tracking results. Fracture stability analysis was performed based on the natural fracture model and geomechanical model.
The fracturing design was customized and optimized with the models mentioned above and understanding obtained from the analyses. High fracturing efficiency and positive production performance impact are benefits of the workflow.
The hydraulic fracture propagation is diagnosed mainly by microseismic data and fracturing data. Additionally, interaction between nature fractures and hydraulic fractures can be identified by fracturing simulations using an unconventional fracture model simulator for a complex fracture network with the inputs of the DFN and the geomechanical model derived from the 3D geological models.
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