Geova

Summary Before constructing wind turbines in the south of Groix island, overseas measurements of the total magnetic field were conducted to locate the Unexploded Ordnances (UXO) buried since the Second World War. A set of two magnetometers pulled by a boat was used to measure the magnetic field in go-and-return trajectories. The measurements along north and southward trajectories were correlated. However, the magnetometers systematically recorded a relatively higher magnetic field during the northward course. Using both north and southward measurements resulted in a heterogenous dataset, which created azimuthal artifacts on the produced maps. A Gaussian transformation was used to adapt the statistical distribution of the southward measurements to the northward ones and make the dataset homogeneous. Therefore, the azimuthal artifacts were considerably reduced, and potential UXO locations were detected more easily. Exploratory data analysis and mapping steps were done entirely in the Isatis.neo™ geostatistical software.

Recently, hybrid clustering algorithms gained much research attention due to better clustering results and are computationally efficient. Hyperspectral image classification studies should be no exception, including mineral mapping. This study aims to tackle the biggest challenge of mapping the mineralogy of drill core samples, which consumes a lot of time. In this paper, we present the investigation using a hybrid clustering algorithm, cohesion-based self-merging (CSM), for mineral mapping to determine the number and location of minerals that formed the rock. The CSM clustering performance was then compared to its classical counterpart, K-means plus-plus (K-means++). We conducted experiments using hyperspectral images from multiple rock samples to understand how well the clustering algorithm segmented minerals that exist in the rock. The samples in this study contain minerals with identical absorption features in certain locations that increase the complexity. The elbow method and silhouette analysis did not perform well in deciding the optimum cluster size due to slight variance and high dimensionality of the datasets. Thus, iterations to the various numbers of k-clusters and m-subclusters of each rock were performed to get the mineral cluster. Both algorithms were able to distinguish slight variations of absorption features of any mineral. The spectral variation within a single mineral found by our algorithm might be studied further to understand any possible unidentified group of clusters. The spatial consideration of the CSM algorithm induced several misclassified pixels. Hence, the mineral maps produced in this study are not expected to be precisely similar to ground truths.

Summary Static geological models are made of horizons and faults which define their geometry. Both faults and horizons (in depth) are uncertain objects, which result from a time-to-depth conversion procedure involving variables such as time maps, velocity maps, markers at wells that in turn might all be affected by a given level of uncertainty. Even faults location may be uncertain and impact Gross Rock Volumes (GRV) calculated in the geological model. This paper details methods which allow simultaneous calculation of depth maps and the associated uncertainty. Focus is put on how to quantify the impact on the global uncertainty on GRV of each Time-Depth conversion input parameter uncertainty. The relative impact of each individual source of uncertainty is calculated on a real case study and the quantitative effect of combining the different sources is estimated and analyzed.

Summary Faults are objects interpreted from seismic data that are often difficult to characterize. Not only the type of a fault can appear different depending on the domain the modeler is working on (time or depth), but accurate shape mapping often extends below the level of coherent seismic data. This proves especially impactful when we consider that simple errors in the fault-network characterization can render a subsurface model useless in terms of its flow properties and provide erroneous values for volumes of reserves. Nevertheless, in absence of proper technology, depth conversion workflows do not account for fault uncertainties, instead relying on a single deterministic interpretation. This paper presents a new methodology that considers positional fault uncertainty in time-to-depth conversion workflows. By considering different realizations of a fault, the target surface is dynamically adapted to this new position and can be used to update further volumetric computations. The proposed methodology can be fully automated and results in a more complete exploration of the uncertainty space regarding fault interpretation.