Double-difference waveform inversion: Feasibility and robustness study with pressure data

Abstract

Time-lapse seismic data are widely used to monitor reservoir changes. Qualitative comparisons between baseline and monitor data sets or image volumes provide information about fluid and pressure effects within the reservoir during production. However, to perform real quantitative analysis of such reservoir changes, quantitative estimates of the elastic parameters are required as input parameters to rock-physics-based reservoir models. Full-waveform inversion has been proposed as a potential tool for retrieving subsurface properties, such as P-and S-wave velocities and density by fitting simulated waveforms to seismic data. An extension of this method to time-lapse applications seems straightforward, but, in fact, it requires more tailored processes such as double-difference waveform inversion (DDWI). We used realistic 2D synthetic pressure data examples to compare the performance of DDWI with that of two other inversion schemes: one using the same starting model for both inversions and the other starting the monitor inversion with the final baseline inversion model. The data simulation and inversion were based on acoustic theory. Although P-wave velocity changes were reliably recovered by each inversion method, DDWI was found to deliver the best results when perfectly repeated surveys were used. However, differencing the baseline and monitor data sets, as required by DDWI, could be found to be sensitive to the presence of survey nonrepeatability. To investigate the feasibility of using DDWI in practice, the dependence of DDWI on the quality of the baseline models and its robustness to survey nonrepeatability were studied with numerical tests. Various types of nonrepeatability were considered separately in the synthetic tests, including random noise, acquisition geometry mismatch, source wavelet discrepancy, and overburden velocity changes. A study of the correlation between the levels and types of nonrepeatability and the resulting contamination of the inversion results found that, for pressure data, DDWI was capable of inverting reliably for P-wave velocity changes under realistic survey nonrepeatability conditions.

Time-lapse full-waveform inversion with ocean-bottom-cable data: Application on Valhall field

“…Nonrepeatability issues, such as random noise, source wavelet discrepancy, source position error, and overburden changes, can generate significant data differences that may overwhelm the real timelapse signals. These nonrepeatability effects are discussed and tested in detail in Yang et al (2015), which concludes that DDWI is robust to random noise, and mild nonrepeatabilities. For the LoFS 10 and LoFS 12 surveys, the standard deviation of the source positioning error is less than 5 m. Source wavelets are well-repeated in the frequency range used in FWI, and any water velocity changes do not have a huge impact because it is a shallow water environment.…”

“…Coherent noise should lead to changes throughout the model, if it is constructively interfering and significant enough. Nonrepeatibilities can introduce coherent noise but are less likely to be modeled in the simulation, which is why DDWI is robust to them (Yang et al, 2015). For example, the weak variations in the shallow part of our DDWI result are likely caused by the data differences from nonrepeatibilities.…”

“…It reduces the effects of uncertainties in the baseline model. The mechanism and implementation of the method are well-explained by Zhang and Huang (2013) and Yang et al (2015). From a computational point of view, scheme I seems to be the most expensive method because it is twice as costly as a regular FWI.…”

“…As discussed in Routh et al (2012), most parts of the models are already close and the inversion mainly focuses on the time-lapse difference. However, this is only true when the baseline FWI is so complete that no extra updates would be generated through more iterations (Yang et al, 2015). In practice, we cannot afford an infinite number of iterations, and so the residuals due to the truncation might leak to the monitor inversion and mix with the real time-lapse difference.…”

Simultaneous joint migration inversion for high‐resolution imaging/inversion of time‐lapse seismic datasets

“…(); Raknes and Arntsen (); Raknes, Weibull and Arntsen (); Yang et al . (); Kazei and Alkhalifah (). Ignoring non‐linear propagation effects (i.e.…”