Time-lapse monitoring is the process of acquiring and analysing multiple seismic surveys, repeatedat the same place at different time periods. This seismic technique, called 4D becauseof the integration time in the construction of images, allows detection and estimation of thesubsurface parameter variations occured through a time evolution. Particularly, in industries,the monitoring can improve our understanding of a producing oil/gas reservoir and CO2 storagesite. Analyzing the time-lapse seismics can help to better manage production programsof reservoirs. In addition, repeated surveys can monitor the evolution of injected fluid frontsand can permit to optimize injection programs which are considered for enhanced oil recovery(EOR) techniques.Several methods have been developed for time-lapse imaging using seismic wave information.In my thesis, I show that full waveform inversion (FWI) can be used for time-lapseimaging, since this method delivers high-resolution quantitative seismic images. It is a promisingtechnique to recover small variations of macro-scale physical properties of the subsurface.In time-lapse applications, several sources of prior information are often available and shouldbe used to increase the image reliability and its resolution. I have introduced this informationthrough a definition of a prior model in a classical FWI approach by also considering a prioruncertainty model. In addition, I have suggested a dynamic weighting to reduce the importanceof these prior models in the final convergence. In realistic synthetic cases, I have shownhow the prior model can reduce the sensitivity of FWI to a less accurate initial model. It istherefore possible to obtain a highly accurate baseline model for 4D imaging.Once the baseline reconstruction is achieved, several strategies can be used to assess thephysical parameter changes. We can make two independent reconstructions of baseline andmonitor models and make subtraction of the two reconstructed models. This strategy is calledparallel difference. The sequential difference strategy inverts the monitor dataset starting fromthe recovered baseline model, and not from the model used initially. Finally, the doubledifferencestrategy inverts the difference data between two datasets which are added to thecalculated baseline data computed in the recovered baseline model. I investigate which strategyshould be adopted to get more robust and more accurate time-lapse velocity changes. Inaddition, I propose a target-oriented time-lapse imaging using regularized FWI including priormodel and model weighting, if the prior information exists on the location of expected variations.It is shown that the target-oriented inversion prevents the occurrence of artifacts outsidethe target areas, which could contaminate and compromise the reconstruction of the effectivetime-lapse changes.A sensitivity study, concerning several frequency decimations for time-lapse imaging, showsthat the frequency-domain FWI requires a large number of frequencies inverting simultaneously.By doing so, the inversion provides a more precise baseline model and more robust time-lapsevariation model with less artifacts. However, the FWI performed in the time domain appearsto be a more interesting approach for time-lapse imaging considering all frequency content.Finally, the regularized time-lapse FWI with prior model is applied to the real field timelapsedatasets provided by TOTAL. The reconstruction of local variations is part of a steaminjection project to improve the recovery of hydrocarbons: it is possible to reconstruct thevelocity variations caused by the injected steam.
展开▼
