Time‐lapse seismic imaging using regularized full‐waveform inversion with a prior model: which strategy?

Abstract

Full‐waveform inversion is an appealing technique for time‐lapse imaging, especially when prior model information is included into the inversion workflow. Once the baseline reconstruction is achieved, several strategies can be used to assess the physical parameter changes, such as parallel difference (two separate inversions of baseline and monitor data sets), sequential difference (inversion of the monitor data set starting from the recovered baseline model) and double‐difference (inversion of the difference data starting from the recovered baseline model) strategies. Using synthetic Marmousi data sets, we investigate which strategy should be adopted to obtain more robust and more accurate time‐lapse velocity changes in noise‐free and noisy environments. This synthetic application demonstrates that the double‐difference strategy provides the more robust time‐lapse result. In addition, we propose a target‐oriented time‐lapse imaging using regularized full‐waveform inversion including a prior model and model weighting, if the prior information exists on the location of expected variations. This scheme applies strong prior model constraints outside of the expected areas of time‐lapse changes and relatively less prior constraints in the time‐lapse target zones. In application of this process to the Marmousi model data set, the local resolution analysis performed with spike tests shows that the target‐oriented inversion prevents the occurrence of artefacts outside the target areas, which could contaminate and compromise the reconstruction of the effective time‐lapse changes, especially when using the sequential difference strategy. In a strongly noisy case, the target‐oriented prior model weighting ensures the same behaviour for both time‐lapse strategies, the double‐difference and the sequential difference strategies and leads to a more robust reconstruction of the weak time‐lapse changes. The double‐difference strategy can deliver more accurate time‐lapse variation since it can focus to invert the difference data. However, the double‐difference strategy requires a preprocessing step on data sets such as time‐lapse binning to have a similar source/receiver location between two surveys, while the sequential difference needs less this requirement. If we have prior information about the area of changes, the target‐oriented sequential difference strategy can be an alternative and can provide the same robust result as the double‐difference strategy.

Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

“…In the associated application paper (Dupuy et al, forthcoming), we use time-lapse FWI results as input data. For time-lapse acoustic inversion, Asnaashari et al (2015) show, on synthetic examples, that there is approximately 5% error in a noise-free case and approximately 10% error in a noisy case. However, this uncertainty increases with depth due to the decrease in illumination.…”

“…Using modern FWI techniques, we assume that a reliable estimation of velocities and quality factors could be obtained with 5% and 12% uncertainty, respectively (Asnaashari et al, 2015). Table 8 sums up the results of the poroelastic inversion for both parametrizations considering the uncertainty in velocity and quality factor input data.…”

Centered Differential Waveform Inversion with Minimum Support Regularization

“…Regularization can boost the resolution of the time-lapse inversion (Maharramov et al, 2016;Asnaashari et al, 2015) by imposing constraints on the difference in the model parameters. We compare the often used Tikhonov regularization (laplacian is used as the weighting matrix) and TV regularization, with a Minimum Support (MS) regularization (Portniaguine and Zhdanov, 1999), not yet used in full-waveform seismic inversion.…”

“…Watanabe et al (2004) apply differential full-waveform inversion to reduce the dependence on the precision of the inversion results for the baseline model (Figure 1(b)), Denli et al (2009) recast the approach for elastic media as Double-difference waveform inversion (DDWI). Raknes and Arntsen (2014) and Asnaashari et al (2015) utilize localized regularization penalty terms to focus time-lapse inversion. Maharramov et al (2016) propose joint full-waveform inversion (Figure 1) with total variation (TV) regularization applied to the difference of simultaneously inverted baseline and monitor velocity models, and Alemie and Sacchi (2016) reparametrize the joint inversion in model space to focusing the inversion on time-lapse changes.…”

Rapid 4D FWI using a local wave solver

“…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).…”