Centered Differential Waveform Inversion with Minimum Support Regularization

Kazei, Vladimir; Alkhalifah, Tariq

doi:10.3997/2214-4609.201701336

Cited by 5 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We would like to illustrate the robustness of the inversion algorithm in the presence of random noise. The robustness of inversion for the baseline model is not addressed here as our focus is the target, yet it can be improved by utilizing all available surveys to retrieve the average model (Kazei and Alkhalifah, 2017). Before inversion, we add a Gaussian white noise with different energy level to the observed baseline and monitor data set.…”

Section: Random Noisementioning

confidence: 99%

Target-oriented time-lapse waveform inversion using redatumed data: Feasibility and robustness

Guo

Alkhalifah

et al. 2020

SEG Technical Program Expanded Abstracts 2020

View full text Add to dashboard Cite

Seismic monitoring of the changes in the subsurface induced by various types of injections into reservoirs is important, yet challenging. Time-lapse waveform inversion can retrieve quantitative estimates of subsurface property changes. Considering that property changes usually occur in a limited region rather than the whole subsurface, we present a target-oriented time-lapse waveform inversion method, which allows for dynamic monitoring of the target of interest. We employ a redatuming technique to produce virtual data at a desired datum level for the target-oriented inversion. Given the redatumed time-lapse data, the property changes can be quantitatively estimated from the data difference for the virtual survey using a doubledifference waveform inversion (DDWI). In the numerical examples, the dependence of the inversion performance on the quality of overburden model and its robustness to nonrepeatable acquisition survey and random noise is investigated. The numerical results demonstrate that the inversion method is capable of recovering the time-lapse changes reasonably well under some challenging circumstances. We will show field data examples at the conference.

show abstract

Section: Random Noisementioning

confidence: 99%

Target-oriented time-lapse waveform inversion using redatumed data: Feasibility and robustness

Guo

Alkhalifah

et al. 2020

SEG Technical Program Expanded Abstracts 2020

View full text Add to dashboard Cite

show abstract

“…Many researchers have addressed these issues and proposed various techniques to improve the repeatability and reduce the 4D noise. Generally, enhancing the repeatability requires improving the acquisition (e.g., system node technology, permanent acquisition (Bakulin et al, 2018), simultaneous 4D pre-stack processing (e.g., Nguyen et al, 2015) and inversion (e.g., Kazei and Alkhalifah, 2017)). Traditionally, 4D processing is implemented by matching the arXiv:2204.00941v1 [physics.geo-ph] 2 Apr 2022 monitor to the baseline data through cross-equalization techniques such as matched-filtering (Rickett and Lumley, 2001;Robinson and Treitel, 2000).…”

Section: Introductionmentioning

confidence: 99%

Time-lapse data matching using a recurrent neural network approach

et al. 2022

View full text Add to dashboard Cite

Time-lapse seismic data acquisition is an essential tool to monitor changes in a reservoir due to fluid injection, such as CO2 injection. By acquiring multiple seismic surveys in the exact same location, we can identify the reservoir changes by analyzing the difference in the data. However, such analysis can be skewed by the near-surface seasonal velocity variations, inaccuracy and repeatability in the acquisition parameters, and other inevitable noise. The common practice (cross-equalization) to address this problem uses the part of the data where changes are not expected to design a matching filter and then apply it to the whole data, including the reservoir area. Like cross-equalization, we train a recurrent neural network on parts of the data excluding the reservoir area and then infer the reservoir-related data. The recurrent neural network can learn the time dependency of the data, unlike the matching filter that processes the data based on the local information obtained in the filter window. We demonstrate the method of matching the data in various examples and compare it with the conventional matching filter. Specifically, we start by demonstrating the ability of the approach in matching two traces and then test the method on a pre-stack 2D synthetic data. Then, we verify the enhancements of the 4D signal by providing RTM images. We measure the repeatability using normalized root mean square and predictability metrics and show that in some cases, our proposed method performed better than the matching filter approach.

show abstract

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

Section: Introductionmentioning

confidence: 99%

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

Verschuur

2020

Geophysical Prospecting

View full text Add to dashboard Cite

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.

show abstract

Centered Differential Waveform Inversion with Minimum Support Regularization

Cited by 5 publications

References 11 publications

Target-oriented time-lapse waveform inversion using redatumed data: Feasibility and robustness

Target-oriented time-lapse waveform inversion using redatumed data: Feasibility and robustness

Time-lapse data matching using a recurrent neural network approach

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

Contact Info

Product

Resources

About