Detection of Microseismic Events in Near-Wellbore Space by MLE Method for DAS Data

Distributed Acoustic Sensing (DAS) is a technique of using a fiber optic cable as a linear array of virtual seismic sensors [1]. DAS is becoming an increasingly popular method for monitoring seismic activity in near-wellbore space. This technology allows high-resolution location along the entire length of the well without tripping operations, which is important for optimizing the development of hydrocarbon fields. One of the main advantages of DAS is its high spatial resolution, which allows data from virtual seismic activity sensors to be acquired along the entire length of the fiber optic cable, thus providing a high level of detail for event localization. However, despite its advantages, DAS has a marked decrease in sensitivity compared to standard passive acoustic logging methods. This problem is especially relevant when working with data characterized by low values of the signal-to-noise ratio, which makes it difficult to reliably detect and locate microseismic events. Therefore, there is a need to develop a location algorithm that can efficiently process data under low signal-to-noise ratio conditions. In this paper, we present a new event location algorithm based on the MLE maximum likelihood estimation method for DAS data processing. To evaluate the efficiency of the developed algorithm, a comparative analysis with the classical Semblance method, which is widely used in seismic exploration, was performed. The results of our studies show that the developed MLE algorithm demonstrates better performance when working with DAS data, especially in low SNR conditions.