| dc.description.abstract | The presence of natural fractures is one of the major factors for hydrocarbon production and a proper method to incorporate their characterization into the reservoir model are of critical importance for shale reservoirs. This study utilizes discrete fracture network (DFN) modeling as the powerful tool to investigate the spatial distribution of subsurface fracture systems and its dynamic impact on shale reservoirs for optimal field development.
We first present a summary of important field data acquisition programs and an overview of natural fracture characteristics commonly observed in the field, and then propose a workflow to develop DFNs by in-house software. Three types of DFN models are established to meet different research demands, including an outcrop-based model, a conceptual model and a stochastic model.
From each of these models, the outcrop model extracts natural fractures (NF) from the outcrop map to directly represent the distribution of fracture length, fracture spacing and fracture strike for modeling, as it is widely believed that fracture patterns exposed on the surface provide analogous information for subsurface fracture networks. The second synthetic model consists of a horizontal well and multiple hydraulic fractures (HF). The model is developed to explore the possibility of more advanced and complex completion designs rather than traditional uniform stimulation treatment. The third stochastic model adopts the fractal theory that believes the correlation between fracture characteristics and sampling domain size is consistent over different fractal dimensions, to populate fracture distribution over the modeling domain. The Hydraulic Fracturing Test Site (HFTS) in Midland Basin is demonstrated as the field case, along with a detailed history matching process.
Once the DFN model is developed, our software will export the fracture model and the calculated connection list to a commercial simulator to perform numerical reservoir simulation, enabling further research of the sensitivity of NF characteristics, reservoir production evaluation and stimulation treatment optimization. | en |
