Fracture mapping of bedrock and knowledge about how fractures influence rock strength and stability is of great importance in a constructional context. These factors largely dictate where one can build and not build in rock, and to what extent reinforcements and safety measurements are needed. In a city like Stockholm where infrastructure has been forced to expand due to a rapidly growing population, this type of knowledge plays a central role to ensure continued development. Fracture mapping is traditionally executed by a geologist who manually measures fracture orientations with a compass. However, this method bears obvious risks as the geologist must physically approach a possibly unstable rock face to carry out manual measurements of fractures and structures. In some cases, the geologist is not even allowed to approach the rock face for safety reasons. The aspect of time should not be neglected either since the process of manual measurements is often time consuming. This has resulted in newer and safer technological methods being developed and tested. In 2015, The Geological Survey of Sweden (SGU) acquired photogrammetrical equipment and 3D-modelling software ShapeMetriX to ease the fracture mapping process, obtain data of higher quality and increase personnel safety in the field. In this report, the photogrammetrical system is quality tested by comparing its results with manual field measurements. The control was carried out on three different rock faces in two locations; Torsgatan, a central street in Stockholm, and Kungens kurva, a construction site southwest of central Stockholm. The study shows that the results of ShapeMetriX correspond well to the manual field measurements and that the method has several advantages as well as disadvantages compared to conventional mapping methods. / Sprickkartering av berggrund och kunskap om hur bergets hållfasthet och stabilitet påverkas av sprickor är viktigt i konstruktionssammanhang. Dessa faktorer dikterar till stor del var man kan och inte kan bygga i berg samt till vilken grad förstärkningar och säkerhetsåtgärder behövs. I en stad lik Stockholm vars infrastruktur tvingas anpassa sig efter en kraftigt växande befolkning sätts dessa kunskaper i en ännu mer central roll för att kunna säkerställa stadens fortsatta utveckling. Sprickkartering utförs traditionellt av en geolog som med hjälp av en kompass manuellt mäter sprickors orientering. Detta medför dock uppenbara risker då denna fysiskt måste befinna sig nära bergskärningen för att kunna utföra mätningar av sprickor och strukturer. I vissa fall kan geologen, av säkerhetsskäl, inte alls närma sig den berörda ytan vilket omöjliggör en detaljerad kartering. Tidsaspekten av det hela bör inte heller bortses då manuella fältmätningar ofta är tidskrävande. Detta har resulterat i att nyare och säkrare teknologiska metoder för kartering och klassificering av berg både utvecklas och prövas. Sveriges geologiska undersökning (SGU) förvärvade 2015 fotogrammetrisk karteringsutrustning och 3D-modelleringsprogrammet ShapeMetriX för att effektivisera sprickkarteringsarbetet, erhålla data med högre kvalitét och öka säkerheten för personal I fält. I denna rapport utvärderas nämnda stereofotogrammetriska karteringsmetod med tillhörande analysmjukvara genom en jämförelse av dess resultat med manuella fältmätningar. Kontrollen utfördes på tre berghällar; en belägen på Torsgatan, en central gågata strax nordväst om centrala Stockholm och de andra vid Kungens kurva, en byggarbetsplats i närheten av Skärholmen i södra Stockholm. Resultat av studien visar att ShapeMetriX mätningar väl stämmer överens med manuella fältmätningar och även att metoden har en

Rock type is a key concept in modern reservoir characterization that straddles multiple scales and bridges multiple disciplines. Reservoir rock classification (or simply rock typing) has been recognized as one of the most effective description tools to facilitate large-scale reservoir modeling and simulation. This dissertation aims to integrate core data and well logs to enhance reservoir description by classifying reservoir rocks in a geologically and petrophysically consistent manner. The main objective is to develop scientific approaches for utilizing multi-physics rock data at different time and length scales to describe reservoir rock-fluid systems. Emphasis is placed on transferring physical understanding of rock types from limited ground-truthing core data to abundant well logs using fast log simulations in a multi-layered earth model. Bimodal log-normal pore-size distribution functions derived from mercury injection capillary pressure (MICP) data are first introduced to characterize complex pore systems in carbonate and tight-gas sandstone reservoirs. Six pore-system attributes are interpreted and integrated to define petrophysical orthogonality or dissimilarity between two pore systems of bimodal log-normal distributions. A simple three-dimensional (3D) cubic pore network model constrained by nuclear magnetic resonance (NMR) and MICP data is developed to quantify fluid distributions and phase connectivity for predicting saturation-dependent relative permeability during two-phase drainage. There is rich petrophysical information in spatial fluid distributions resulting from vertical fluid flow on a geologic time scale and radial mud-filtrate invasion on a drilling time scale. Log attributes elicited by such fluid distributions are captured to quantify dynamic reservoir petrophysical properties and define reservoir flow capacity. A new rock classification workflow that reconciles reservoir saturation-height behavior and mud-filtrate for more accurate dynamic reservoir modeling is developed and verified in both clastic and carbonate fields. Rock types vary and mix at the sub-foot scale in heterogeneous reservoirs due to depositional control or diagenetic overprints. Conventional well logs are limited in their ability to probe the details of each individual bed or rock type as seen from outcrops or cores. A bottom-up Bayesian rock typing method is developed to efficiently test multiple working hypotheses against well logs to quantify uncertainty of rock types and their associated petrophysical properties in thinly bedded reservoirs. Concomitantly, a top-down reservoir description workflow is implemented to characterize intermixed or hybrid rock classes from flow-unit scale (or seismic scale) down to the pore scale based on a multi-scale orthogonal rock class decomposition approach. Correlations between petrophysical rock types and geological facies in reservoirs originating from deltaic and turbidite depositional systems are investigated in detail. Emphasis is placed on the cause-and-effect relationship between pore geometry and rock geological attributes such as grain size and bed thickness. Well log responses to those geological attributes and associated pore geometries are subjected to numerical log simulations. Sensitivity of various physical logs to petrophysical orthogonality between rock classes is investigated to identify the most diagnostic log attributes for log-based rock typing. Field cases of different reservoir types from various geological settings are used to verify the application of petrophysical rock classification to assist reservoir characterization, including facies interpretation, permeability prediction, saturation-height analysis, dynamic petrophysical modeling, uncertainty quantification, petrophysical upscaling, and production forecasting. / text

Lithological core logging is a subjective and time consuming endeavour which could possibly be automated, the question is if and to what extent this automation would affect the resulting core logs. This study presents a case from the Zinkgruvan Zn-Pb-Ag mine, Bergslagen, Sweden; in which Classification and Regression Trees and K-means Clustering on the Self Organising Map were applied to X-Ray Flourescence lithogeochemistry data derived from automated core scan technology. These two methods are assessed through comparison to manual core logging. It is found that the X-Ray Fluorescence data are not sufficiently accurate or precise for the purpose of automated full lithological classification since not all elements are successfully quantified. Furthermore, not all lithologies are possible to distinquish with lithogeochemsitry alone furter hindering the success of automated lithological classification. This study concludes that; 1) K-means on the Self Organising Map is the most successful approach, however; this may be influenced by the method of domain validation, 2) the choice of ground truth for learning is important for both supervised learning and the assessment of machine learning accuracy and 3) geology, data resolution and choice of elements are important parameters for machine learning. Both the supervised method of Classification and Regression Trees and the unsupervised method of K-means clustering applied to Self Organising Maps show potential to assist core logging procedures.