Layer-specific imaging and monitoring using seismic interferometry and the Marchenko method

Abstract

Seismic imaging and monitoring with reflected waves, originally used in the oil and gas industry to identify and assess potential hydrocarbon reservoirs and later monitor their exploitation, also have diverse applications in near-surface geophysics, mineral exploration, geothermal energy, and CO2 or H2 storage. Beyond revealing subsurface structures, these techniques enhance our understanding of how the subsurface responds to human activities, such as induced seismicity due to extraction processes. Seismic imaging and monitoring often focus on specific target layers within the subsurface, but challenges from interferences with surrounding layers and small changes within the specific layer(s) can distort the specific signals and lower the accuracy. Our research aims to address these challenges and provide practical solutions for more accurate and reliable seismic imaging and monitoring with reflected waves.
In this thesis, we aim to develop seismic data-driven methods for layer-specific imaging and monitoring, with a primary focus on advancing the technique of ghost-reflection retrieval using seismic interferometry (SI) and showing how the Marchenko method could be used with land seismic data.
SI often involves the cross-correlation of seismic observations at different receiver locations and the consecutive summation over the available sources, allowing the retrieval of new seismic responses from virtual sources located at the receiver positions. When using sources and receivers only at the surface, the virtual-source gathers retrieved by SI contain not only pseudo-physical reflections but also ghost (non-physical) reflections. These ghost reflections result mainly from the cross-correlation (CC) or auto-correlation (AC) of primary reflections from two different depths, representing reflections from inside specific subsurface layer(s), as measured with a virtual ghost source and a virtual ghost receiver positioned directly on top of the specific layer(s). Consequently, the ghost reflections can provide information about the specific layer(s) without the effects of the overburden and underburden layers.
We first explore the use of ghost reflections for layer-specific characterisation of the shallow subsurface using SI by AC, utilising numerically modelled data for a layered subsurface model down to 30 m depth, incorporating a lateral change in velocity, a velocity gradient with depth, a thickness change, and a velocity change in the target layer. Additionally, we present the first application of ghost reflections to shallow subsurface field data. Ghost reflections typically exhibit similar characteristics to other reflection events, appear close to or interfere with other events with only slight temporal differences. This makes their identification a significant challenge. To address this, we eliminate surface-related multiples and demonstrate how specific ghost reflections can be more efficiently retrieved by muting undesired reflections in the dataset before applying SI.
To extend the application of ghost reflections to deep structures, we focus on the feasibility of monitoring pore-pressure changes in the Groningen gas field in the Netherlands. We utilise numerical modelling to simulate scalar reflection data, deploying sources and receivers at the surface. We conduct an ultrasonic transmission laboratory experiment to measure S-wave velocities at different pore pressures. This data is used to create subsurface models, which are then utilized to simulate scalar reflection seismic data for monitoring purposes. We retrieve zero-offset ghost reflections by applying SI by AC to the modelled datasets. We then use a correlation operator to determine time differences between a baseline survey and monitoring surveys. Additionally, we investigate the effects of the sources and receivers' geometry and spacing, as well as the number of virtual sources and receivers, on retrieving ghost reflections with high interpretability and resolution. Besides observing time shifts in the ghost reflections, we also explore the feasibility of using the amplitude of ghost reflections for reservoir monitoring.
Having clear reflections from both the top and bottom of the specific layer(s) is crucial for retrieving ghost reflections, which can be challenging when using land seismic datasets due to the usual presence of strong surface waves. Conventionally, surface waves are suppressed during data processing using frequency-offset, frequency-wavenumber, or bandpass filters. However, these approaches can prove ineffective when the surface waves are scattered and/or overlap with the frequency regions of the reflected body waves that we intend to preserve. To overcome some of these challenges, we show the efficacy of the interferometric surface-wave suppression using a 2D seismic reflection dataset from Scheemda, Groningen province, the Netherlands. Interferometric surface-wave suppression can be used to effectively suppress surface waves by applying SI to first estimate the surface waves and second followed by their adaptive subtraction from the original data. We propose to apply these two steps recursively, i.e., several times, which yields better results than a single application in terms of clearer and more continuous reflections. This technique can function as a standalone technique or as part of a pre-processing flow.
When applying the seismic reflection method for monitoring purposes, specific reflections, e.g., from the top and bottom of the reservoir, are of interest. The reflections from both the top and bottom of the specific layer(s) can also be distorted by other events from the surrounding layers. To eliminate such distortions, the Marchenko-redatuming method was introduced. Several Marchenko-redatuming methods have been applied successfully to marine field data. We demonstrate, for the first time, the application of the Marchenko-based isolation technique to field land seismic data to isolate the target response by removing the overburden and underburden. Land data are intrinsically elastic, known for dominant surface waves and a low signal-to-noise ratio, posing a challenge for the Marchenko method, which requires high-quality reflection data. After we carefully apply several pre-processing steps, including recursive interferometric surface-wave suppression, we apply the Marchenko method twice: first, to remove the overburden effects by choosing a focal depth of 30 m, and then to remove the underburden effects by choosing a focal depth of 270 m. This process generates a new reflection response from the target area, providing clearer subsurface responses. The Marchenko method is particularly beneficial for data-driven techniques such as ghost-reflection retrieval, seismic imaging, and time-lapse studies using land seismic datasets.