Implementation and application of fracture diagnostic tools : fiber optic sensing and diagnostic fracture injection test (DFIT)

Shale reservoirs have drawn much attention in recent years in the oil and gas industry. Hydraulic fracturing is a key technology to extract the trapped hydrocarbon in the shale reservoirs. The complex hydraulic and natural fracture networks enable large contact area between fracture and low-permeability reservoir to enhance the production. The characterization of complex fracture geometry and evaluation of fracture properties are crucial to the fracturing operation design and fractured reservoir simulation. The main approach to a better understanding of fracture and shale reservoir matrix is fracture diagnosis. There are mainly five fracture diagnostic technologies: Distributed Temperature Sensing (DTS), Distributed Acoustic Sensing (DAS), Diagnostic Fracture Injection Test (DFIT), microseismic, and tracer flow-back test. In this study, we mainly focus on the data interpretation model of DTS and DFIT. The current interpretation of DTS data is mostly limited to the qualitative analysis. To enable the quantitative interpretation of DTS data, an in-house comprehensive model is developed to evaluate the fracture properties and geometry. Our model couples fracture, wellbore, and reservoir domain together to capture the full physical process during the production stage. The effects of reservoir parameters, fracture parameters, and fracture geometries on temperature profiling along the wellbore are analyzed with our model. Our forward model could be potentially used to characterize fracture parameters or fracture geometry with history matching. DFIT is consisted of before closure analysis and after closure analysis. The leak-off coefficient, injection efficiency, reservoir matrix permeability, and initial pore pressure can be obtained from DFIT data analysis. In this study, several models for DFIT data interpretation were integrated. A Marcellus shale gas DFIT data is successfully analyzed with our workflow.