Extracting geomechanical information from borehole microseismic data with machine learning

Abstract

A key practice in the development of unconventional hydrocarbon resources is to monitor hydraulic fracturing operations and analyze in detail the induced microseismicity, for understanding the extent of the volume affected by the fractures. Even though microseismic data are one of the few geophysical measurements that can be used for this purpose they are not always used at their full potential: the common analyses are limited to the interpretation of the locations of the induced seismic events in terms of fractured volume, while the propagation and the dynamic evolution of the microseismic events that are directly related to the source process are not studied. The source moment tensor can be used to describe the deformation mechanism occurring at the seismic source and the corresponding geomechanical parameters, which are relevant aspects for the monitoring of hydraulic fracturing operations. Its value can be estimated through an inversion process using the P-wave and S-wave waveforms from the observations at the seismic receivers. However, the limited aperture of the receiver arrays in borehole acquisition is inadequate to solve the inverse problem. The goal of this thesis project is study the feasibility of estimating the source moment tensor with borehole microseismic data making use of machine learning algorithms. The task is done by implementing a forward model to compute waveforms generated by known values of the source moment tensor in a borehole acquisition geometry (forward problem). Forward modeling was carried out using a modied version of the discrete wavenumber method. Afterwards, this data is used to train a neural network to estimate the moment tensor at the source given the microseismic data (inverse problem). The neural network was designed, adapting an existing architecture used in previous studies to solve similar inverse problems. The implementation was done entirely in the Python programming language, using the Tensorflow library for the machine learning parts. The network was trained with the dataset generated in the forward model until reaching an optimal mean squared error minimum. Finally, the capability of the network to invert microseismic measurements and retrieve acceptable values of the source moment-tensor components was tested using synthetic data, resulting in an average prediction accuracy of 0.997. Our results indicate that for this case, a neural network is able to satisfactory invert for the full moment-tensor components. Additionally, the neural network was also trained to handle the presence of Gaussian noise, achieving an average prediction accuracy of 0.990 for tested synthetic data with 10% Gaussian noise.