Using image warping for time-lapse image domain wavefield tomography

Abstract

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.

Rapid 4D FWI using a local wave solver

“…When performing FWI for multiple surveys, spurious time-lapse changes may be generated when the results for different data sets disagree in more than just the regions of true time-lapse perturbation (i.e., the models come from different local minima of the objective function). Several approaches have been proposed to mitigate this problem including double-difference FWI (DDFWI) (Watanabe et al, 2004;Denli and Huang, 2009), image domain waveform tomography (Yang et al, 2014a), and model-space regularization methods (Zhang and Huang, 2013a;Maharramov and Biondo, 2014b;Asnaashari et al, 2015). Another option is to redatum the data to subsurface locations (Mulder, 2005), allowing the inversion to focus on a smaller domain (Yang et al, 2012).…”

“…Full-waveform inversion for 4D is challenging because of the nonuniqueness of the problem (there are multiple models that fit (Yang et al, 2014a), and model-space regularization methods (Zhang and Huang, 2013a; Maharramov and Biondo, 2014b;Asnaashari et al, 2015). Another option is to redatum the data to subsurface locations (Mulder, 2005), allowing the inversion to focus on a smaller domain (Yang et al, 2012).…”

“…This significantly reduces the computational cost of the algorithm but also restricts the recovered change to a particular subset of the model space. In Yang et al (2014b), it was observed that DDFWI predicted changes throughout the model, leading to the development of a tomography method in the image space (Yang et al, 2014a). That method allowed for the use of narrower-offset data by minimizing the difference between baseline and monitor images rather than data sets.…”

“…Here, we show how this algorithm can be used to increase the efficiency of FWI for the particular case of 4D velocity change estimation. If there are significant changes outside the reservoir region, as can occur due to compaction for example, then our method may not result in significant cost savings as the number and size of the relevant subdomains grow.Full-waveform inversion for 4D is challenging because of the nonuniqueness of the problem (there are multiple models that fit (Yang et al, 2014a), and model-space regularization methods (Zhang and Huang, 2013a; Maharramov and Biondo, 2014b;Asnaashari et al, 2015). Another option is to redatum the data to subsurface locations (Mulder, 2005), allowing the inversion to focus on a smaller domain (Yang et al, 2012).…”

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

“…As an alternative to FWI, we apply an image domain wavefield tomography (IDWT) method (Yang et al, 2013) to time-lapse VSP data. Based on the assumption that the geology has not changed dramatically over time, both the baseline and time-lapse seismic data should be able to image the same area in the subsurface.…”