Improved Microseismic Signal Detection Using Weighted Particle Motion Linearity Measures
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Microseismic data provides important information about the subsurface during hydraulic fracturing jobs. However, microseismic signals are not as easy to identify as conventional seismic signals. I developed a new technique to detect microseismic signals by measuring displacement information along with the three-dimensional time coherence analysis of the 3D particles motion (linearity). It was tested on microseismic data from a horizontal well in the Marcellus Shale. The method multiplies the linearity calculation with a function of the amplitude of oscillation that is generated using the envelope. In addition, I developed techniques to detect signals from the new, weighted linearity (or the traditional, unweighted linearity) that help with the goal of more effective signal detection. The results from the new method show that it can detect 16 signals from two microseismic datasets, including barely noticeable (amplitude of 0.0003), weak (amplitude of 0.002), and strong (amplitude of 0.02) signals. These were compared with the results from the traditional, unweighted linearity calculation, where I detect only 9 signals and give results contaminated with noise. This indicates that there is a 40% improvement in signal detection using the new approach. Furthermore, the weighted linearity showed more detail in the signals and less noise compared to unweighted linearity results. With this new approach, I am able to detect signals that unweighted linearity cannot identify, while not compromising the quality of signals detectable by linearity.
Ahmed, Abdullah Abdulaziz A (2018). Improved Microseismic Signal Detection Using Weighted Particle Motion Linearity Measures. Master's thesis, Texas A & M University. Available electronically from