Time-lapse Velocity Changes Research Articles

Machine learning-based time-lapse 1D seismic full-waveform inversion with efficient training data generation in a carbon capture and storage monitoring

Machine learning (ML)-based time-lapse and seismic full-waveform inversion has recently gained attention in carbon capture and storage (CCS) research because of its potential to overcome the limitations of theory-based inversion and its excellent imaging performance for CO2 plumes. We propose a ML-based time-lapse 1D inversion method with efficient training data generation. The proposed method exploits time-lapse seismic data to infer time-lapse velocity changes and uses a 1D inversion strategy to enable the UNet3+ model to learn the general characteristics of velocity changes between baseline and monitoring phases. Our proposed method quickly generates various 1D velocity models and rapidly provides numerous near-offset data by using 1D ray-based modeling method. This allows us to overcome a challenge in constructing of 2D velocity models based on limited geological assumptions and forward modeling to generate seismic gathers, which requires massive computational resources. However, because 2D effects such as diffraction caused by the CO2 plume only affect the monitoring data, inaccurate predictions in the 1D inversion occurred near the plume, following the diffraction trajectory. We corrected this effect using a diffraction removal process for 2D target data, thereby improving the prediction accuracy of the ML model. The ML model was trained with a large amount of training data generated using the proposed method and validated on 2D target data with three different CO2 injection plume shapes obtained from a modified Marmousi2 velocity model. As a result, information about the CO2 plume was reasonably delineated, although the magnitude of the time-lapse velocity change near the plume boundary was reduced by small amplitude losses during the diffraction removal process. Our results suggest that the proposed method is a powerful tool for CCS monitoring, which requires forward modeling for each iterative survey.

Data-assimilated time-lapse visco-acoustic full-waveform inversion: Theory and application for injected CO2 plume monitoring

Continuous seismic monitoring for quantifying CO2 plume migration and detection of any potential leakages in the subsurface is essential for the security of long-term anthropogenic carbon dioxide geologic storage. Traditional time-lapse full-waveform inversion (TLFWI) methods aim to map the CO2 distribution by estimating seismic velocity changes, but recent studies find that CO2-induced attenuation is an important complement to seismic velocity for tracking the CO2 plumes and even quantifying the CO2 saturation. We have developed a novel data-assimilated TLFWI method to construct high-resolution time-lapse velocity and attenuation changes from dense time-lapse monitoring data. This method consists of two theoretical developments: visco-acoustic full-waveform inversion (QFWI) and multiparameter hierarchical matrix-powered extended Kalman filter (mHiEKF). The method is capable of (1) posing temporal constraints to retrieve time-lapse information from dense monitoring data by using mHiEKF, (2) accurately recovering high-spatial-resolution velocity and attenuation perturbations using first-order equation system-based QFWI, and (3) providing the model uncertainty by estimating their model standard deviation. With numerical examples, we first find the effectiveness of the new QFWI on estimating accurate velocity and attenuation models simultaneously. Then, a CO2 leakage case and a realistic Frio-II CO2 monitoring case are presented to find the advantages and applicability of our data-assimilated QFWI method for estimating time-lapse changes using dense time-lapse monitoring surveys. By assimilating time-lapse seismic monitoring data over time, our data-assimilated QFWI method can improve the resolution of velocity and attenuation changes and decrease their model uncertainties.

Combining reflection and transmission information in time-lapse velocity inversion: A new hybrid approach

Full-waveform inversion (FWI) uses the information of the full wavefield to deliver high-resolution images of the subsurface. Conventional time-lapse FWI primarily uses the transmitted component (diving waves) of the wavefield to reconstruct the low-wavenumber component of the velocity model. This requires large-offset surveys and low-frequency data. When the target of interest is deep, diving waves cannot reach the target and FWI will be dominated by the reflected component of the wavefield. Consequently, the retrieved model resembles a least-squares migration instead of a velocity model. Image-domain methods, especially image-domain wavefield tomography (IDWT), have been developed to obtain a model of time-lapse velocity changes in deeper targets using reflected waves. The method is able to recover models of deep targets. However, it also tends to obtain smeared time-lapse velocity changes. We have developed a form of time-lapse waveform inversion that we call dual-domain time-lapse waveform inversion (DDWI), whose objective function joins FWI and IDWT, combining information from the diving waves in the data-domain FWI term with information from the reflected waves in the image-domain IDWT term. During the nonlinear inversion, the velocity model is updated using constraints from both terms simultaneously. Similar to sequential time-lapse waveform inversion, we start the time-lapse inversion from a baseline model recovered with FWI. We test DDWI on a variety of synthetic models of increasing complexity and find that it can recover time-lapse velocity changes more accurately than when both methods are used independently or sequentially.

Full waveform inversion of repeating seismic events to estimate time-lapse velocity changes

Full waveform inversion of repeating seismic events to estimate time-lapse velocity changes

Velocity model estimation by full waveform inversion of time-lapse 4D passive seismic array data

SUMMARY In passive-source monitoring, an accurate velocity model is important to precisely estimate microseismic source locations, and to understand changes in reservoir properties. In this study, we employ frequency-domain full waveform inversion in order to obtain a high- resolution velocity model by exploiting full wavefields. We demonstrate the feasibility of the method for a surface geophone array by inverting for time-lapse 4D velocity changes in a realistic subsurface model. Our method successfully estimates the small velocity changes of a few percent within layers of 10s of meters, even for a single passive seismic source event. The analysis of wavepaths and gradients suggests that keys for the successful inversion are the use of full wavefields (both first and scattered arrivals), the vicinity of velocity changes to the source, and the wide-aperture surface array.

Using image warping for time-lapse image domain wavefield tomography

Time-lapse seismic data are widely used for monitoring subsurface changes. A quantitative assessment of how reservoir properties have changed allows for better interpretation of fluid substitution and fluid migration during processes such as oil and gas production and carbon sequestration. Full-waveform inversion (FWI) has been proposed as a way to retrieve quantitative estimates of subsurface properties through seismic waveform fitting. However, for some monitoring systems, the offset range versus depth of interest is not large enough to provide information about the low-wavenumber component of the velocity model. We evaluated an image domain wavefield tomography (IDWT) method using the local warping between baseline and monitor images as the cost function. This cost function is sensitive to volumetric velocity anomalies, and it is capable of handling large velocity changes with very limited acquisition apertures, where traditional FWI fails. We described the theory and workflow of our method. Layered model examples were used to investigate the performance of the algorithm and its robustness to velocity errors and acquisition geometry perturbations. The Marmousi model was used to simulate a realistic situation in which IDWT successfully recovers time-lapse velocity changes.

Time-lapse walkaway vertical seismic profile monitoring for CO2 injection at the SACROC enhanced oil recovery field: A case study

Geologic carbon storage involves large-scale injections of carbon dioxide into underground geologic formations. Changes in reservoir properties resulting from [Formula: see text] injection and migration can be characterized using monitoring methods with time-lapse seismic data. To achieve economical monitoring, vertical seismic profile (VSP) data are often acquired to survey the local injection area. We investigated the capability of walkaway VSP monitoring for [Formula: see text] injection into an enhanced oil recovery field at SACROC, West Texas. VSP data sets were acquired in 2008 and 2009, and [Formula: see text] injection took place after the first data acquisition. Because the receivers were located above the injection zone, only reflection data contain the information from the reservoir. Qualitative comparison between reverse-time migration images at different times revealed vertical shifts of the reflectors’ center, indicating the presence of velocity changes. We examined two methods to quantify the changes in velocity: standard full-waveform inversion (FWI) and image-domain wavefield tomography (IDWT). FWI directly inverts seismic waveforms for velocity models. IDWT inverts for the time-lapse velocity changes by matching the baseline and time-lapse migration images. We found that, for the constrained geometry of VSP surveys, the IDWT result was significantly more consistent with a localized change in velocity as expected from a few months of [Formula: see text] injection. A synthetic example was used to verify the result from the field data. By contrast, FWI failed to provide quantitative information about the volumetric velocity changes because of the survey geometry and data frequency content.