Geophysical Prospecting - Volume 73, Issue 4, 2025

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.

In geophysics, Bayesian inversion methods are of significant prominence. Here, we present a novel approach utilizing the Hamiltonian Monte Carlo (HMC) method in gravity inversion for elucidating three‐dimensional (3D) density structures. HMC provides a multi‐dimensional sampling method that demonstrates enhanced optimization efficiency, facilitating the attainment of distant proposals with elevated acceptance probabilities. Its applicability also extends to resolving linear inverse problems. Three synthetic models of cubic bodies, dipping dykes and a combined model were designed for tests. The testes underscore the promising potential of HMC in recovering subsurface density source bodies and giving the uncertainty of the inversion model. Furthermore, an inversion test conducted on the Vinton salt dome yields a reasonable 3D distribution of cap rock, consistent with prior studies in this area. The modelling and field experiments showed that the proposed HMC gravity inversion method had higher accuracy and application potential.

Structural information about the subsurface near the borehole can be obtained from reconstructed conductivity distributions. These distributions may be reconstructed via the inversion of deep‐sensing electromagnetic induction log data. Unfortunately, these complex media often display anisotropy and structural variations in both horizontal and vertical directions, making the three‐dimensional inversion computationally demanding and ill‐posed. To address these challenges, we introduce a sequential inversion strategy of deep‐sensing electromagnetic induction logging data that is measured while drilling. For the inversion at each logging position, we employ a matrix‐free implementation of the adjoint integral equation method and a quasi‐Newton algorithm. To tackle the ill‐posed nature of the problem, we regularize the inverse problem by employing a multi‐dimensional inversion parameter technique that shifts from zero‐ to three‐dimensional parameterization. The model derived from the inversion of the data at multiple positions is incrementally integrated by utilizing the sensitivity data at each logging position. To validate our approach, we tested our method on simulated data using an anisotropic model. These experiments show that this approach produces a good reconstruction of the true conductivity for the whole track while only doing the inversion at a single position at a time.