Design, implementation and application of the Marchenko plane-wave algorithm

The Marchenko algorithm can suppress the disturbing effects of internal multiples that are present in seismic reflection data. To achieve this, a set of coupled equations with four unknowns is solved. These coupled equations are separated into a set of two equations with two unknowns using a time window. The two unknown focusing functions can...

A framework for subsurface monitoring by integrating reservoir simulation with time-lapse seismic surveys

Reservoir simulations for subsurface processes play an important role in successful deployment of geoscience applications such as geothermal energy extraction and geo-storage of fluids. These simulations provide time-lapse dynamics of the coupled poromechanical processes within the reservoir and its over-, under-, and side-burden environments...

Extracting small time-lapse traveltime changes in a reservoir using primaries and internal multiples after Marchenko-based target zone isolation

Geophysical monitoring of subsurface reservoirs relies on detecting small changes in the seismic response between a baseline and monitor study. However, internal multiples, related to the over- and underburden, can obstruct the view of the target response, hence complicating the time-lapse analysis. To retrieve a response that is free from...

Time-lapse applications of the Marchenko method on the Troll field

The data-driven Marchenko method is able to redatum wavefields to arbitrary locations in the subsurface, and can, therefore, be used to isolate zones of specific interest. This creates a new reflection response of the target zone without interference from over- or underburden reflectors. Consequently, the method is well suited to obtain a...

Plane-Wave Marchenko Imaging Method: Field Data Application

Seismic imaging is often used to interpret subsurface formations. However, images obtained by conventional methods are contaminated with internal multiples. The Marchenko method provides the means to obtain multiple-free subsurface images. Due to the high computational cost of the conventional point-source Marchenko imaging method, the less...

Discerning small time-lapse traveltime changes by isolating the seismic response of a reservoir using the Marchenko method

4D seismic studies aim to observe time-lapse changes in the subsurface between a baseline and a monitor study. These changes are generally small, and the seismic response from a deep reservoir can be concealed by reflections from shallow structures. Here, we introduce a novel way of isolating the reservoir response by means of the Marchenko...

Adaptation of the iterative Marchenko scheme for imperfectly sampled data

The Marchenko method retrieves the responses to virtual sources in the Earth's subsurface from reflection data at the surface, accounting for all orders of multiple reflections. The method is based on two integral representations for focusing- A nd Green's functions. In discretized form, these integrals are represented by finite summations...

Discrete representations for Marchenko imaging of imperfectly sampled data

Marchenko imaging is based on integral representations for focusing functions and Green’s functions. In practice, the integrals are replaced by finite summations. This works well for regularly sampled data, but the quality of the results degrades in a case of imperfect sampling. We have developed discrete representations that account for...

Employing internal multiples in time-lapse seismic monitoring, using the Marchenko method

Time-lapse seismic monitoring aims at resolving changes in a producing reservoir from changes in the reflection response. When the changes in the reservoir are very small, the changes in the seismic response can become too small to be reliably detected. In theory, multiple reflections can be used to improve the detectability of traveltime...

Correcting for imperfectly sampled data in the iterative Marchenko scheme

The Marchenko method retrieves the responses to virtual sources in the subsurface, accounting for all orders of multiples. The method is based on two integral representations for focusing and Green’s functions. In discretized form these integrals are represented by finite summations over the acquisition geometry. Consequently, the method...

Data-driven internal multiple elimination applications using imperfectly sampled reflection data

We consider reflection data that have been subsampled by 70% and use Point-Spread-Functions to reconstruct the original data. The subsampled, original and reconstructed reflection data are used to image the medium of interest with the Marchenko method. The image obtained using the subsampled data shows artifacts caused by internal multiples,...

Coda-wave interferometry and the Marchenko method

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Marchenko-based target replacement in laterally varying media

Seismic time-lapse studies are generally concerned with variations in a specific target zone, situated inside an otherwise static medium. In seismic monitoring the entire reflection response at the surface needs to be remodeled for every change in the target zone. Ideally, however, only the response of the target zone is remodeled, which is then...

Representations for the Marchenko Method for imperfectly sampled data

The Marchenko method is based on two integral representations for focusing functions and Green’s functions. In practice the integrals are replaced by finite summations. This works well for regularly sampled data, but the quality of the results degrades in case of imperfect sampling. We reformulate the integral representations into summation...