The quantification of inter-well stress interference among neighboring wells is critical to the success of well planning and completion design in shale reservoirs. Moreover, shale rocks, characterized by their inherent anisotropic nature, necessitate a fast and yet accurate approach to quantify their mechanical and hydraulic behaviors under in-situ conditions. This research intends to enhance our understanding of induced pressure-stress alterations in anisotropic shale reservoirs and accelerate reservoir depletion estimation.
We have developed a semi-analytical solution to predict the stress change induced by the production of offset wells with multi-stage fractures in shale reservoirs. Our approach is based on Fourier transform with the assumption of homogeneous and transversely isotropic reservoirs. The developed model converts a given pressure field to the Fourier domain using the Fast Fourier Transform and solves the resulting linear system. The obtained solution in the Fourier domain is then transformed back to the real domain to recover the temporal and spatial distribution of the induced stress change.
Our model has been validated by comparing with numerical approaches and clearly demonstrates its sound accuracy. Through case studies, it is found that the anisotropy of shale rocks significantly affects the stress interference among neighboring horizontal wells, which cannot be captured accurately by conventional approaches based on isotropic assumptions. In particular, the anisotropy of Poisson's ratio drastically alters the reorientation effect of the principal stresses. In addition, thanks to the superior algorithm complexity of the Fast Fourier Transform method, our model predicts the stress tensor field of a million-cell blocks in just a few seconds, which greatly outperforms numerical approaches.
This work is arguably the first trial in using analytical approaches to study the hydraulic-mechanical behavior of shale reservoirs, with the consideration of anisotropic rock properties. The developed model demonstrates high efficiency while maintaining satisfactory accuracy. As such, our model is useful to study and quantify stress interference and valuable to evaluate reservoir depletion quickly and yet sufficiently during shale reservoir development.