In multistage hydraulic fracturing treatments, the combination of extreme large-scale pumping (high rate and volume) and the high heterogeneity of the formation (because of large contact area) normally results in complex fracture growth that cannot be simply modeled with conventional fracture models. Lack of understanding of the fracturing mechanism makes it difficult to design and optimize hydraulic fracturing treatments. Many monitoring, testing and diagnosis technologies have been applied in the field to describe hydraulic fracture development. Strain rate measured by distributed acoustic sensor (DAS) is one of the tools for fracture monitoring in complex completion scenarios. DAS measures far-field strain rate that can be of assistance for fracture characterization, cross-well fracture interference identification, and well stimulation efficiency evaluation. Many field applications have shown DAS responses on observation wells or surrounding producers when a well in the vicinity is fractured. Modeling and interpreting DAS strain rate responses can help quantitatively map fracture propagation.

In this work, a methodology is developed to generate the simulated strain-rate responds to assumed fracture systems. The physical domain contains a treated well that the generate strain variation in the domain because of fracturing, and an observation well that has fiber-optic sensor installed along it to measure the strain rate responses to the fracture propagation. Instead of using a complex fracture model to forward simulate fracture propagation, this work starts from a simple 2D fracture propagation model to provide hypothetical fracture geometries in a relatively reasonable and acceptable range for both single fracture case and multiple fracture case. Displacement discontinuity method (DDM) is formulated to simulate rock deformation and strain rate responds on fiber-optic sensors. At each time step, fracture propagation is first allowed, then stress, displacement and strain field are estimated as the fracture approaches to the observation well. Afterward, the strain rate is calculated as fracture growth to generate patterns as fracture approaching. Extended simulation is conducted to monitor fracture propagation and strain rate responses. The patterns of strain rate responses can be used to recognize fracture development.

Examples of strain rate responses for different fracturing conditions are presented in this paper. The relationship of injection rate distribution and strain rate responses is investigated to show the potential of using DAS measurements to diagnose multistage hydraulic fracturing treatments.

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