Time-lapse full-waveform inversion as a reservoir-monitoring tool — A North Sea case study

Hicks, Erik; Hoeber, H.; Houbiers, Marianne; Lescoffit, Séverine Pannetier; Ratcliffe, Andrew; Vinje, Vetle

doi:10.1190/tle35100850.1

Cited by 49 publications

(19 citation statements)

References 21 publications

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“…() and Hicks et al . () have already done investigations based on this model. They showed that full‐waveform inversion (FWI) is capable of recovering production‐related changes and local changes caused by waste injection with reasonable accuracy (although with lower resolution and amplitude than the true one).…”

Section: Synthetic Data Example Based On a Realistic Time‐lapse Modelmentioning

confidence: 99%

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

Verschuur

2020

Geophysical Prospecting

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A B S T R A C TThe current time-lapse practice is to exactly repeat well-sampled acquisition geometries to mitigate acquisition effects on the time-lapse differences. In order to relax the rigid requirements on acquisition effects, we propose simultaneous joint migration inversion as an effective time-lapse tool for reservoir monitoring, which combines a joint time-lapse data processing strategy with the joint migration inversion method. Joint migration inversion is a full-wavefield inversion method that explains the measured reflection data using a parameterization in terms of reflectivity and propagation velocity. Both the inversion process inside the imaging/inversion scheme and the extra illumination provided by including multiples in joint migration inversion makes the obtained velocity and reflectivity operator largely independent of the utilized acquisition geometry and, thereby, relaxes the strong requirement of non-repeatability during the monitoring. Because simultaneous joint migration inversion inverts for all datasets simultaneously and utilizes various constraints on the estimated reflectivities and velocity, the obtained time-lapse differences have much higher accuracy compared to inverting each dataset separately. It allows the baseline and monitor parameters to communicate with each other dynamically during inversion via a userdefined spatial weighting operator. In order to get more localized time-lapse velocity differences, we further extend the regular simultaneous joint migration inversion to a robust high-resolution simultaneous joint migration inversion process using the timelapse reflectivity difference as an extra constraint for the velocity estimation during inversion. This constraint makes a link between the reflectivity-and the velocity difference by exploiting the relationship between them. We demonstrate the feasibility of the proposed method with a highly realistic synthetic model based on the Grane field offshore Norway and a time-lapse field dataset from the Troll Field.

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Section: Synthetic Data Example Based On a Realistic Time‐lapse Modelmentioning

confidence: 99%

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

Verschuur

2020

Geophysical Prospecting

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“…In differential or double-difference waveform inversion the baseline model is inverted first and then the monitor model is constructed based on double-difference misfit functional (Denli et al, 2009), which allows us to reduce artifacts due to imperfect baseline model inversion. Oghenekohwo et al 2015and Hicks et al (2016) proposed to exploit common information between the baseline and monitor data and models in joint inversion and common model inversion respectively. Common information makes inversion for reference model more robust.…”

Section: Cdwi Inversion Strategymentioning

confidence: 99%

Time-lapse waveform inversion regularized by spectral constraints and Sobolev space norm

Kazei

Alkhalifah

2018

SEG Technical Program Expanded Abstracts 2018

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Imperfect illumination from surface seismic data due to lack of aperture and frequency content leads to ambiguity and resolution loss in seismic images and in full-waveform inversion (FWI) results. The resolution of time-lapse velocity updates can, however, be improved enforcing the sparsity of the parameter changes. Edge-preserving regularizations and constraints are typically used to promote sparsity. However, different choice of regularization parameters leads to different inversion results and optimal parameters are hard to identify, especially for real data. In particular it is not straightforward to balance the inversion between sparsity constraint and data fit. Fortunately, it is possible to estimate local spatial wavenumbers in a velocity model that are best illuminated by the data. We propose to constrain part of the model difference spectrum that is well illuminated by the data and optimize the rest of the spectrum to enhance sparsity. We approximate correctly retrieved model wavenumbers by simply picking large enough values in the inverted spectrum and constrain that part of model spectrum. Then we adjust the rest of the wavenumbers to reduce the value of a Sobolev space norm (SSN). SSN reduction promotes sparsity of time-lapse updates, while spectral constraints ensure that the part of the modeled spectrum retrieved from the data is completely retained. Application to synthetic noisy data for a perturbation of the Marmousi II model shows that the model resolution can be improved by using our method to extrapolate the model spectrum.

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“…; Hicks et al . ). In most of the strategies, the baseline survey is inverted first, and then the strategies differ in how the second inversion is performed.…”

Section: Introductionmentioning

confidence: 97%

Iterated extended Kalman filter method for time‐lapse seismic full‐waveform inversion

Eikrem

Nævdal

Jakobsen

2018

Geophysical Prospecting

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A B S T R A C TTime-lapse seismic data is useful for identifying fluid movement and pressure and saturation changes in a petroleum reservoir and for monitoring of CO 2 injection. The focus of this paper is estimation of time-lapse changes with uncertainty quantification using full-waveform inversion. The purpose of also estimating the uncertainty in the inverted parameters is to be able to use the inverted seismic data quantitatively for updating reservoir models with ensemble-based methods. We perform Bayesian inversion of seismic waveform data in the frequency domain by combining an iterated extended Kalman filter with an explicit representation of the sensitivity matrix in terms of Green functions (acoustic approximation). Using this method, we test different strategies for inversion of the time-lapse seismic data with uncertainty. We compare the results from a sequential strategy (making a prior from the monitor survey using the inverted baseline survey) with a double difference strategy (inverting the difference between the monitor and baseline data). We apply the methods to a subset of the Marmousi2 P-velocity model. Both strategies performed well and relatively good estimates of the monitor velocities and the time-lapse differences were obtained. For the estimated time-lapse differences, the double difference strategy gave the lowest errors.

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Time-lapse full-waveform inversion as a reservoir-monitoring tool — A North Sea case study

Cited by 49 publications

References 21 publications

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

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

Time-lapse waveform inversion regularized by spectral constraints and Sobolev space norm

Iterated extended Kalman filter method for time‐lapse seismic full‐waveform inversion

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