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Application of multicomponent seismic and distributed acoustic sensing data to unconventional reservoir development
Liu, Youfang
Liu, Youfang
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2023
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Abstract
The potential of multicomponent seismic data and microseismic distributed acoustic sensing data for unconventional reservoir characterization and development is investigated. The unconventional reservoirs studied, including the Eagle Ford shale and the Wolfcamp shale, are relatively thin (between 30 and 200 meters) compared to their depths (about 3000 meters), which presents unique challenges for seismic imaging and characterization. These reservoirs are hydraulically fractured for hydrocarbon extraction and also may contain many natural fractures.
The effectiveness of using P-waves and PS-waves (or converted waves) for detecting the azimuthal anisotropy signature caused by natural fractures is first investigated. This investigation is conducted by analyzing P-wave and PS-wave synthetics generated from the SEAM II Barrett model which includes both Woodford and Eagle ford shale. From reflectivity modeling, P-wave synthetics are analyzed using Amplitude Variation versus Azimuth (AVAZ) and Velocity Variation versus Azimuth (VVAZ), while splitting estimation and compensation (SEAC) analysis is conducted on the PS-wave synthetics. The synthetics generated by reflectivity modeling are then compared to synthetics from finite element modeling. This comparison indicates a higher sensitivity of the PS-wave to azimuthal anisotropy in the reservoirs compared to the P-wave data, whose responses are highly masked by mild velocity variation and orthorhombic media in the overburden.
Full waveform inversion (FWI) is subsequently applied to the synthetic data from the SEAM II Barrett model and the field surface seismic data (both P-wave and SH-wave) from the Midland Basin. In both synthetic and field data applications, I apply time-domain acoustic P-wave and SH-wave FWI to estimate the velocities from surface to the deep reservoirs. The typical primary focus of SH-wave FWI is near surface geotechnical applications, where depth is on the order of tens of meters. In this thesis, I extend the implementation of SH-wave FWI to deep reservoirs (at the depths over 2000 meters).
In the synthetic study, P-, PS- and SH-wave gathers migrated using the inverted velocities are flat, indicating FWI is able to provide accurate low-wavenumber P- and S-wave velocities from P-wave and SH-wave data. The flat PS-wave migrated gathers also indicate the inverted P- and S-wave velocities can help PS-wave velocity analysis, which is often difficult due to the combined influence of P- and S-wave. In the field data study, the estimated S-wave velocities show lateral velocity variations due to the change of deposition environment in the overburden. Accurately capturing these velocity variations are important for successful depth imaging. The P- and SH-wave migrated stacks reveal that Spraberry formation might have inconsistent depths as compared to the Wolfcamp formation, which makes geosteering more challenging.
Finally, a novel machine learning workflow is implemented to automatically classify microseismic events originating from and outside the Eagle Ford shale. This workflow takes advantage of guided waves which are only generated by microseismic events occurring within the low-velocity Eagle Ford and are recorded by distributed acoustic sensing (DAS) in a nearby horizontal well. A convolutional neural network (CNN) is utilized to recognize the dispersive energy pattern of the guided waves, and then used to separate Eagle Ford and non-Eagle Ford microseismic events. The vertical extent of the Eagle Ford microseismic events can then be used to obtain a more accurate estimation of the stimulated reservoir volume (SRV). This workflow provides a fast classification of microseismic events while maintaining similar accuracy compared to human inspection.
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