Accurate subsurface characterization is critical for emerging energy-transition projects, yet conventional seismic data often fail to resolve metre-scale heterogeneities that strongly influence reservoir behaviour. This thesis develops an integrated workflow that combines stratigraphic forward modelling with seismic forward modelling to improve the detection of sub-seismic stratigraphic features in wave-dominated shoreface systems. Outcrop analysis shows significant variability in petrophysical and acoustic properties, revealing the limitations of lithology-based seismic approaches. By converting grain-size distributions from stratigraphic simulations into acoustic properties, the workflow produces synthetic seismic data that better represent fine-scale stratigraphy. Angle-dependent seismic analysis shows potential for identifying subtle acoustic variations, though current modelling techniques require refinement. The results demonstrate that linking geological, petrophysical, and geophysical data enhances subsurface resolution and point toward future developments involving more complex models, in-well seismic methods, and machine learning.

Reducing the uncertainty of reservoir characterization requires to better identify the small-scale structures of the subsurface from the available data. Studying the seismic response of meter-scale, stratigraphic heterogeneities typically relies on the generation of reservoir models based on outcrop examples and their forward seismic modelling. To bridge geological information and seismic modelling, these methods allocate values of acoustic properties, such as mass-density and P-wave velocity, according to discretized properties like layer-type lithology or facies units. This strategy matches the current workflow in seismic data inversion in industry, where modelling workflows are based on lithofacies distributions. However, from stratigraphic modelling, we know that meter-scale heterogeneities occur within certain facies and lithologies. Here, we evaluate the difference on the seismic response between allocating acoustic properties in a grain size–based, semi-continuous manner versus discretized manners based on lithology and facies classifications. To do so, we generate a reference geological simulation that we populate with acoustic properties, mass-density and P-wave velocity, using three different strategies: (1) based on grain size distribution; (2) based on facies distribution; and (3) based on lithology. The method we propose includes the generation of realistic geological simulations based on stratigraphic modelling and the transformation of its output into acoustic properties, honouring the intra-lithology and intra-facies, small-scale structures. We, then, generate seismic data by applying a forward seismic modelling workflow. The synthetic data show that the grain size–based simulation allows the identification of small-scale, stratigraphic heterogeneities, such as beds with strong density and velocity contrasts. These stratigraphic structures are smoothened or may completely disappear in the facies and lithology discretized simulations and, therefore, are not (well) represented in the synthetic seismic data. Recognizing meter-scale, stratigraphic heterogeneities is relevant for the characterization of the fluid flow in the reservoir. However, current discrete and lithology-based strategies in seismic inversion are not able to resolve such heterogeneities because real subsurface properties are not discrete properties but continuous, unless there are stratigraphic discontinuities such as erosional surfaces or faults. This research works towards a better understanding of the relationship between changes in these continuous properties and the observed seismic data by introducing greater complexity into the discretized geological simulations. Here, we use synthetic seismic images with the goal of eventually aiding in fine-tuning seismic inversion methodologies applied to real seismic data. One pathway is to foster the development of inversion approaches that can leverage stratigraphic modelling to get stronger geological priors and replace the standard but inadequate multi-Gaussian prior.

Seismic angle gathers and spectral seismic attributes offer complementary insights to improve understanding of complex subsurface characteristics. However, the labor-intensive process of subsurface characterization, data annotation, limited labeled data, and subsurface complexity make it difficult to leverage these insights via supervised learning approaches.

To overcome such challenges and benefit from the strength of spectral seismic attributes, this study introduces a novel hierarchical Self-Organizing Map (SOM) framework to integrate spectral seismic attributes like scalograms and spectrograms (joint time-frequency analyses) extracted from angle gathers.

In our current research, firstly, we trained individual SOMs, as an unsupervised pattern recognition algorithm on reflectivity images, angle-gathers, and the spectral seismic attributes extracted from angle-dependent data. Secondly, we deploy a hierarchical SOM network to combine and analyze all these datasets. Thirdly, we evaluate the hierarchical approach and standalone analyses of clustering quality and information content using the binary boundary maps and the performance metrics. Our findings indicated that, the scalogram-based hierarchical SOM, containing information of different angles, achieves the lowest Quantization Error and Davis-Bouldin Index, indicating optimal feature representation and well-separated clusters. The findings stress the potential of hierarchical networks and joint time-frequency analyses from angle gathers for robust seismic interpretation workflows.

This extended abstract discusses a novel approach to identify geological sub-seismic scale features using angle-gather spectrograms. Traditional seismic methods face challenges in identifying fine scale geological structures, especially in sublayers thinner than about 1/10 of the wavelength. This paper introduces the use of spectrogram analysis on angle gathers, to capture local spectrum information which is not visible and distinguishable in the reflectivity image and angle gathers themselves. The authors apply this technique to simulated seismic data for a range of basic geological scenarios, extracting spectrograms for each incident angle within angle gathers. The analysis explores the impact of density, velocity variations, layer number and layer thickness on spectrogram patterns, providing insights into their effectiveness for computer vision-related research. The study aims to rejuvenate the utilization of spectrograms for revealing hidden geological structures beyond standard seismic resolution.

In the process of seismic subsurface imaging, there is no acceptable forward model reflecting the AVO response in a laterally inhomogeneous medium for reservoir characterization. This means that even when inversion is performed in full waveform, local heterogeneity is typically not fully incorporated while emplying a local 1.5D assumption. Thus, it is impossible to image and classify the subsurface features with these local heterogeneities. Still, the angle-dependent response encodes heterogeneity information that assists overcoming this issue if used properly. To exploit its capabilities, we present a way for identifying reservoir characteristics in the presence of local heterogeneity by linking encoded angle-dependent responses created using angle-dependent Full Wavefield Migration with their originating source - the relevant geological context. To accomplish this purpose, a pipeline technique that integrates the produced angle-dependent responses with a pattern categorization deep-learning tool is proposed. For a basic test on synthetic data, the method successfully identified the produced different stratigraphic architectures and classified them in the training stage. The method is then validated on angle gathers generated from different models with comparable geological circumstances.

Many stratigraphic features occur at a scale that is at the edge or below vertical seismic resolution. Thus, they cannot be directly observed in the seismic data, while still having an important effect on the fluid flow within the system. The better understanding of these sub-seismic scale features or heterogeneities can help decrease subsurface uncertainty. Here we present a novel method that integrates forward stratigraphic modelling, petrophysics, and geophysics to decipher the seismic imprint of heterogeneities in wave-dominated, shallow marine environments. The proposed three-stepped method starts with defining geology-related input parameters for BarSim, a stratigraphic forward modelling software that produces models that include stratigraphic architecture, grain size distribution, and facies distribution. Then, the geological data is translated, cell by cell, into petrophysical data (density, Vp, and Vs) using emphirical relationships. Finally, the forward seismic modelling is performed by combining a finite difference approach strategy and angle-dependent full wavefield migration to retrieve the angle gathers This method also allows the generation of large amounts of field-independent data suitable for machine learning applications.