Understanding acquisition and processing error in microseismic data: An example from Pouce Coupe Field, Canada

Abstract

A B S T R A C TA challenge in microseismic monitoring is quantification of survey acquisition and processing errors, and how these errors jointly affect estimated locations. Quantifying acquisition and processing errors and uncertainty has multiple benefits, such as more accurate and precise estimation of locations, anisotropy, moment tensor inversion and, potentially, allowing for detection of 4D reservoir changes. Here, we quantify uncertainty due to acquisition, receiver orientation error, and hodogram analysis. Additionally, we illustrate the effects of signal to noise ratio variances upon event detection. We apply processing steps to a downhole microseismic dataset from Pouce Coupe, Alberta, Canada. We use a probabilistic location approach to identify the optimal bottom well location based upon known source locations. Probability density functions are utilized to quantify uncertainty and propagate it through processing, including in source location inversion to describe the three-dimensional event location likelihood. Event locations are calculated and an amplitude stacking approach is used to reduce the error associated with first break picking and the minimization with modelled travel times. Changes in the early processing steps have allowed for understanding of location uncertainty of the mapped microseismic events.

Microseismic waveform and shear‐wave splitting analysis with model data

“…In borehole microseismic surveys, P‐waves are typically weak. Previous work illustrates that the P‐wave eigenvector hodogram approach is highly sensitive to noise, causing vector fidelity issues (Bray and White, 2020). Yuan and Li (2017a) demonstrate how shear‐wave hodogram analysis can decrease the uncertainty associated with only using P‐wave hodogram analysis.…”

“…The analysis utilizes waveforms initially rotated to vertical, east and north and P‐ and S‐wave first‐arrival picks. Additionally, P‐wave hodogram event azimuth results are used to reduce the search domain based upon the previously calculated hodogram uncertainty (Bray and White, 2020). For example, if an event has a P‐wave hodogram azimuth uncertainty of ±10°, the joint qP‐ and qS‐wave algorithm will search the fast qS‐axis over the 20° range for the optimum azimuth.…”

“…The modelled waveforms were put through the same processing steps as the field data (Bray and White, 2020). P‐ and S‐wave arrivals are manually picked, and initial P‐wave hodogram analysis is applied to rotate and maximize P‐wave energy on a single component.…”

“…In order to assess the methodology, we generated synthetic events in both a single homogeneous orthorhombic layer and in a 22‐layer model (based on four primary layers) with receiver position akin to the vertical field data arrays at Pouce Coupe (Bray and White, 2020). The set of 50 synthetic events (Fig.…”

“…The velocities and anisotropic parameters in a 1D model (Fig. 1b) are estimated from available sonic logs within the Pouce Coupe Field study area (Bray and White, 2020), formation micro‐image logs, and previous shear‐wave splitting analysis (Andersen et al ., 2013; Lee et al ., 2014; Andrews and Davis, 2016; Bray and White, 2020). The model density and velocities are based on upscaled logs, and anisotropy parameters are selected given conclusions of these previous studies.…”