Joint microseismic moment-tensor inversion and location using P- and S-wave diffraction stacking

Author:

Xu Jincheng1,Zhang Wei1ORCID,Liang Xing2,Rong Jiaojun3,Li Junlun4ORCID

Affiliation:

1. Southern University of Science and Technology, Shenzhen Key Laboratory of Deep Offshore Oil and Gas Exploration Technology, Shenzhen 518055, China and Southern University of Science and Technology, Department of Earth and Space Sciences, Shenzhen 518055, China..

2. PetroChina Zhejiang Oilfield Company, Hangzhou 311100, China..

3. BGP Inc., CNPC, Zhuozhou 072750, China..

4. University of Science and Technology of China, School of Earth and Space Sciences, Hefei 230026, China.(corresponding author).

Abstract

Microseismic location methods based on diffraction stacking, which does not require arrival picking, can yield accurate and reliable source locations for data with a low signal-to-noise ratio. However, due to the complex radiation pattern from a rupturing source, variation in the waveform polarities brings challenges to diffraction stacking-based methods. The current implementations of joint source mechanism inversion and location methods that only use compressional wave (P-wave) amplitudes have limitations in noise resistance and location accuracy. To mitigate those issues, we have developed a new method for joint microseismic moment-tensor inversion and event location using diffraction stacking with P- and S-waves amplitudes, both of which are used to invert for the moment tensor of a microseismic event; then, the inverted moment tensor is used to correct the waveform polarity changes before stacking. In addition, to expedite the large amount of calculations required for moment-tensor inversion at each potential source position and origin time, we develop an optimized grid search scheme and implement the algorithm with GPUs. Our location method does not require manual picking of the first arrivals, and it can automatically detect and locate microseismic events from continuous data. We first validated the method with two synthetic examples, and then we apply it to a surface monitoring data set for hydraulic fracturing at a shale gas well pad in the southern Sichuan Basin, China, where billions of cubic meters of shale gas are being produced annually. The locations of the microseismic events are nicely correlated with the fracturing stages, and the determined source mechanisms are also consistent with the expected fracture growth. Our method is feasible for microseismic surface monitoring with dense nodal arrays and can provide important information for fracture growth and regional stress characterization.

Funder

Center for Computational Science and Engineering of Southern University

Shenzhen Key Laboratory of Deep Offshore Oil and Gas Exploration Technology

Shenzhen Science and Technology Program

National Natural Science Foundation of China

Publisher

Society of Exploration Geophysicists

Subject

Geochemistry and Petrology,Geophysics

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