Distributed Acoustic Sensing (DAS) has recently gained importance in monitoring hydraulic fracturing treatments in the oil and gas industry. DAS data contain critical information about the fracture geometry as linearly relatable induced strain variations during the stimulation. The Low-frequency components of the DAS (LF-DAS) data, are known for their complexity as they exhibit various characteristic signals - caused by several mechanisms - that complicate their interpretation. LF-DAS data from horizontal monitoring wells (HMWs) has been used to detect fracture hits and characterize fracture geometry. However, the LF-DAS data from vertical monitoring wells (VMWs) have not been studied extensively as a means to infer fracture geometry. The major limitation of VMWs is the number of monitored stages, but the data contain more information about fracture height compared with LF-DAS measurements from HMWs. Hence, it is necessary to have a physical rock deformation model to simulate the strain-rate responses in offset VMWs during fracture propagation in order to understand and interpret the various patterns that are observed in the field datasets.
The objective of this study is to simulate strain-rate signals in VMWs during hydraulic fracturing and to analyze the measurements to obtain information on the fracture geometry, especially the fracture height. The fracture boundary can be directly related to the strain-rate signals. In this study, we propose a workflow to determine fracture height at different fiber-to-fracture (dff) distances for fracture heights ranging from 20 m to 200 m. We conduct a detailed sensitivity analysis to understand the impacts of the dff, the perforation location, the fracture passing time, and the well inclinations on the measured strain-rate signals. The analysis helps interpret the various patterns observed in field data and the underlying mechanisms. Interpretation of field data from the Hydraulic Fracture Testing Site II (HFTS II) using results from our forward physical model provides valuable information on the fracture characteristics that can be captured by the physical model. The results of this study are expected to provide better interpretations of LF-DAS signals from VMWs.