Detection of geobodies in 3D seismic using unsupervised machine learning

In this work, we present a novel, automated method for detecting geobodies in 3D seismic reflection data, helping to reduce interpreter bias and speed up seismic interpretation. A seismic geobody refers to a geometrical, structural, or stratigraphic feature, such as a channel, turbidite fan, or igneous intrusion. Geobodies are subtle seismic features, hard to pick, and their detection is challenging to automate due to their complex 3D geomorphology and diversity of shapes. Nevertheless, the detection and delineation of these structures are essential for improving the understanding of the subsurface as well as building a variety of conceptual models. In our approach, we can rapidly interpret large 3D seismic volumes using point cloud-based segmentation to identify geobodies of interest, including complex stratigraphic features like lobes and channels. By converting the 3D seismic cube into a 3D seismic point cloud (sparse cube), we reduce the volume of data to analyse, which in turn speeds up the detection process. First, we build the 3D point clouds by filtering the seismic reflection volume using different seismic attributes, and then each point in the cloud is segmented into different clusters. The clustering is performed using the unsupervised Density-Based Spatial Clustering of Applications with Noise (DBSCAN) which allows the segmentation of all structures present into delineated objects. The clustered objects can then be characterised by features based on their 3D shape and spatial amplitude distribution. Finally, our method allows the selection of a specific geobody and can retrieve geobodies based on their similarity to exploration targets of interest. The method has been applied successfully to two modern 3D seismic datasets (Falkland Basins) and two types of geobodies: fans and sill intrusions. We demonstrate that our method can scan through a large 3D seismic volume and automatically retrieve likely fan and sill geobodies in a very efficient manner. This approach can be used to scan through large volumes of 3D seismic, looking for a wide variety of geobodiesJames Watt Scholarshi

3D reconstruction of poral network at nanoscale from 2D slices

International audienceIn France, claystone geological formations are privileged candidates for deep underground nuclear waste storage, due, in particular, to their low permeability ability. However, in order to better predict the behavior of such media, the characterization of the pore network is essential. Such low permeability materials may have a high overall porosity but the majority of this porosity is constituted by pores smaller than 100 nm and whose distribution is still poorly known. The connectivity and the topology of these pores influence the porous media properties, for instance in regards to gas transport. In order to better understand the pore network structure, imaging techniques have been used to provide 2D and 3D images, such as microtomography or FIB-SEM. The latter allows a nanometric resolution (5 to 20nm) at the expense of the total imaged volume (usually a few μm3). Such a sample size is usually below the REV (Representative elementary volume), especially in regards to transport properties. In this project, we explore the possibility to distance the imaged 2D slices and use a numeric method to reconstitute the 3D volume at the REV scale. To this end, we study a stack of 180 parallel FIB-SEM slices of a synthetic clay of nanometric porosity. We compare two methods, based on multiple-point statistic algorithm (MPS), to reconstruct a coherent 3D configuration of its porous structure from 2D sections with different sampling spaces. The results of the reconstruction based on sparse data are then compared to the initial volume in regards to global characteristics – such as porosity - , morphological parameters – such as pore size distribution and connectivity index - as well as Lattice Boltzmann flow simulations to assess the capacity of the reconstruction method to provide satisfactory results. The first method, using a slice sequential reconstitution, produces results with low sensitivity to conditioning data but induces planar effects. The second method, aggregating values of three orthogonal directions allows a better reconstruction of the pore structure. This method is, however, more sensitive to conditioning data. The permeability values resulting from flow simulations on the second method are similar to those obtained on the total image for a distance between conditioning slices up to 11 cells. Thus, by reducing the number of direct images to acquire, this method opens the possibility of exploring volumes 5 to 10 times larger than those currently analyzed, therefore leading to a better representativity of the porous medium.En France, les formations d'argilites sont des candidates privilégiées pour le stockage géologique de déchets haute-teneur du fait, en particulier, de leur faible perméabilité. Cependant, de façon à mieux prédire le comportement de tels milieux, la caractérisation du réseau poral est essentielle. De tels matériaux à faible perméabilité peuvent avoir une forte porosité totale mais la majorité de celle-ci est constituée de pores de taille inférieure à 100 nm et dont la distribution reste peu connue. La connectivité et la topologie de ces pores influence les propriétés du milieu poreux, par exemple liées au transport de gaz. De manière à mieux comprendre la structure du réseau poral, des techniques d'imagerie sont utilisées pour générer des images 2D et 3D, comme la microtomographie ou le FIB-MEB. Cette dernière méthode permet une résolution nanométrique (5 à 20 nm) au prix d'un faible volume imagé (en général quelques µm 3).Un tel volume est souvent inférieur au VER (volume élémentaire représentatif), en particulier en ce qui concerne les propriétés de transport. Dans ce travail, nous explorons la possibilité d'écarter les images 2D acquises et d'utiliser une méthode de reconstruction numérique pour générer un volume 3D à l'échelle du VER. Dans ce but, nous étudions un groupe de 180 coupes FIB-MEB parallèles d'une argile synthétique à porosité nanométrique. Nous comparons deux méthodes, basées sur un algorithme de simulation multi-point (MPS), pour reconstruire une configuration 3D cohérente de la structure porale d'après des sections 2D espacées de différentes distances. Les résultats de ces reconstructions sont ensuite comparées au volume de référence en ce qui concerne des caractéristiques globales, comme la porosité, des paramètres morphologiques, comme la distribution de tailles de pores ou l'indice de connectivité, ainsi que des simulations LBM (Lattice Boltzmann) d'écoulement pour évaluer la capacité des méthodes de reconstruction de générer des structures satisfaisantes. La première méthode, qui utiliser une reconstruction séquentielle des coupes, génère des résultats avec une faible sensibilité à la distance de conditionnement mais créé des effets de plan. La seconde méthode, qui agrège les valeurs dans les trois directions orthogonales, permet une meilleure reconstruction de la structure porale. Cette méthode, par contre, est plus sensible aux données de conditionnement. Les valeurs de perméabilités obtenues par simulation sur la seconde méthode sont similaires à celles obtenues sur l'image de référence pour une distance entre les coupes conditionnnantes inférieure à 11 cellules. Par conséquent, en réduisant le nombre d'images qu'il est nécessaire d'obtenir, cette méthode ouvre la possibilité d'explorer des volumes 5 à 10 fois plus grand que ceux actuellement imagés, permettant une meilleure représentativité du milieu poreux

Comparison of various 3D pore space reconstruction methods and implications on transport properties of nanoporous rocks

International audienceUnderstanding fluid flow and transport within clay rock is essential for predicting caprock integrity in underground gas storage or as host rock in deep radioactive waste storage. The connectivity and topology of the nanopore space, which drive the transfer mechanisms of these materials, are still poorly known and direct 3D imaging is particularly challenging. In this work, we investigate and compare different stochastic reconstruction approaches based on two-point and multiple-point statistics (MPS) methods and using information from 2D training images for 3D volume rendering at submicron scale. A particular emphasis is given to the maximal critical distance of sampling between two consecutive 2D images which is necessary to obtain a coherent 3D reconstruction of the nanopore structure. We assess how these realizations honour various crucial transport properties of material, namely permeability, effective diffusion and longitudinal dispersion. Morphological features such as pore volume, specific surface, Euler characteristic and tortuosity are used to analyze the results. The methods are employed on a synthetic clay of nanometric porosity for which FIB-SEM images are available. Results indicate that the 3DA-MPS and weighted-3DA-MPS approaches are the most suited for preserving pore space features and transport properties, the choice depending on the level of conditioning data available

Can Agents Model Hydrocarbon Migration for Petroleum System Analysis? A Fast Screening Tool to De-Risk Hydrocarbon Prospects

Understanding subsurface hydrocarbon migration is a crucial task for petroleum geoscientists. Hydrocarbons are released from deeply buried and heated source rocks, such as shales with a high organic content. They then migrate upwards through the overlying lithologies. Some hydrocarbon becomes trapped in suitable geological structures that, over a geological timescale, produce viable hydrocarbon reservoirs. This work investigates how intelligent agent models can mimic these complex natural subsurface processes and account for geological uncertainty. Physics-based approaches are commonly used in petroleum system modelling and flow simulation software to identify migration pathways from source rocks to traps. However, the problem with these simulations is that they are computationally demanding, making them infeasible for extensive uncertainty quantification. In this work, we present a novel dynamic screening tool for secondary hydrocarbon migration that relies on agent-based modelling. It is fast and is therefore suitable for uncertainty quantification, before using petroleum system modelling software for a more accurate evaluation of migration scenarios. We first illustrate how interacting but independent agents can mimic the movement of hydrocarbon molecules using a few simple rules by focusing on the main drivers of migration: buoyancy and capillary forces. Then, using a synthetic case study, we validate the usefulness of the agent modelling approach to quantify the impact of geological parameter uncertainty (e.g., fault transmissibility, source rock location, expulsion rate) on potential hydrocarbon accumulations and migrations pathways, an essential task to enable quick de-risking of a likely prospect