# Feasibility of Identifying Shale Sweet Spots by Downhole Microseismic Imaging
## 4 Discussion  
In our synthetic examples, we demonstrate the capability of using two observation wells to effectively map the sweet spot, characterized by a low Poisson's ratio distribution. Both the event locations and velocity solutions are accurately recovered. However, as the distance between events and receivers increases, the effectiveness of recovering event locations and structural models decreases. Furthermore, using just one monitoring well proves insufficient for effectively recovering event locations and velocities. By testing our method with three observation wells arranged in a triangular distribution, we significantly improve the accuracy of both event locations and velocity results. These tests highlight the importance of sufficient ray coverage to balance the trade-off between event locations and structural models. Our findings suggest that with at least two strategically positioned observation wells, our method can effectively reconstruct \(\mathrm{Vp}\) and Poisson's ratio models.  
Our method uses 3D grid search to locate events without requiring prior known locations. Initial velocity models are directly constructed from sonic logs. With high-quality observed data and well-prepared initial models, we successfully derive reliable Poisson's ratio models and map sweet spots. In complex geological settings, the disparity between \(\mathrm{P}\) and \(\mathrm{S}\) rays might lead to biased Poisson's ratio results from the conventional \(\mathrm{Vp}\) and \(\mathrm{Vs}\) models [44]. Traditional techniques, such as calculating the \(\mathrm{Vp}\)/\(\mathrm{Vs}\) ratio [45] or inverting \(\mathrm{S}\)-\(\mathrm{P}\) time ratios [14,22], might inaccurately estimate [PERSON]'s ratio or \(\mathrm{Vp}\)/\(\mathrm{Vs}\) models under such conditions. Our method, not assuming identical \(\mathrm{P}\)- and \(\mathrm{S}\)-wave raypaths, avoids biases from unresolved portions of the \(\mathrm{Vp}\) model. In synthetic tests, our traveltime tomography produces more accurate P-wave velocity and Poisson's ratio solutions than traditional methods. However, in real cases where \(\mathrm{P}\)- and \(\mathrm{S}\)-wave raypaths are similar due to the short distances between events and receivers, our method does not show significant improvements over traditional techniques.  
We observe that the cluster of induced events correlates strongly with the low Poisson's ratio area marked by a black rectangle in Figure 9. This anomaly in Poisson's ratio could indicate changes in the shale rock properties triggered by high-pressure fluid injection during fracturing. The treatment enhances porosity and permeability, allowing hydrocarbons to fill the fractures and subsequently lower the Poisson's ratio. Thus, a low Poisson's ratio can serve as an indicator for predicting hydrocarbon presence or sweet spots. In our field example, the limited data restrict our analysis to only one stimulation stage, impacting the resolution and accuracy of the sweet spot map. However, with more data, we could produce a more precise and detailed map, which would aid in estimating the  
Figure 10: (**a**) Convergence curve of location error of 3D grid search over three loops in the field case. (**b**) Misfit curves of the traveltime tomography of the last loop. The circle represents the data misfit of each loop, and the triangle and square are the traveltime misfits over each iteration of \(\mathrm{P}\) and \(\mathrm{S}\) waves, respectively.  
volume of the reservoir stimulated during hydraulic fracturing, applicable to widespread unconventional hydrocarbon [46] and geothermal [47] exploitations.