ZONALNA PROCJENA I INTERPOLACIJA KAO ISTOVREMENI PRISTUPI U SLUČAJU MALOG ULAZNOG B ROJA PODATAKA (PRIMJER POLJA ŠANDROVAC, SJEVERNA HRVATSKA)

The Bjelovar Subdepression area in Northern Croatia was analysed, especially the Šandrovac Field that is located in the northern part of the subdepression. In this example, e-log depth marker Z’, i.e. the Pannonian and Pontian boundary, was used as an input data. The data were statistically analysed for the entire subdepression from 497 data readings from the regular grid with cell size of 1x1 km that covers the existing palaeostructural map. Then is selected 18 well data within the Šandrovac Field where e-log markers are recognised (an example of a small number of data). They are also read directly for given structural map and mapped using one of the declustering methods known as Thiessen polygon method or Kriging. It is concluded when the mapping includes small number of data, and consequently local uncertainties, the subsurface mapping need to be done on both ways and maps compared.Analiziran je prostor Bjelovarske subdepresije, osobito polje Šandrovac koje se nalazi u sjevernom dijelu. U ovom primjeru kao ulazni podatci uporabljene su dubine EK-markera Z\u27, tj. granice panona i ponta. One su statistički analizirane na razini cijele subdepresije iz 497 podataka očitanih iz pravilne mreže s ćelijama veličine 1x1 km kojom je prekrivena postojeća paleostrukturna karta. Nadalje, odabrano je 18 bušotinskih smjestišta unutar polja Šandrovac gdje su karotažom određene dubine markera (primjer malog ulaznog broja podataka). I oni su očitani izravno sa spomenute karte te kartirani jednom od deklasterizacijskih metoda, tj. metodom Thiessenovih poligona ili kriginga. Zaključeno je kada kartiranje uključuje značaje lokalne nesigurnosti te mali broj podataka, opravdano je dubinsko kartiranje na oba prikazana načina te usporedba rješenja

Visualization and design systems for road infrastructure

Transportation infrastructure provides a vital service for the functionality of a city. The efficient design of road networks poses an interesting topic in computer science for digital content developers. For civil engineers, the visualization of analysis results on infrastructure both efficiently and intuitively is crucial. The following contributions are made to these areas: * Street Network Design - A system built around tensor field design is proposed. Necessary background information is discussed and a set of tools are developed. Generated street networks are shown to demonstrate the effectiveness of the system. * Bridge Analysis Visualization - A unique system to visualize the analysis results of bridges is discussed. Methods used to procedural model a bridge and visualize analytical information over the generated model in an intuitive manner are detailed

Branching Boogaloo: Botanical Adventures in Multi-Mediated Morphologies

FormaLeaf is a software interface for exploring leaf morphology using parallel string rewriting grammars called L-systems. Scanned images of dicotyledonous angiosperm leaves removed from plants around Bard’s campus are displayed on the left and analyzed using the computer vision library OpenCV. Morphometrical information and terminological labels are reported in a side-panel. “Slider mode” allows the user to control the structural template and growth parameters of the generated L-system leaf displayed on the right. “Vision mode” shows the input and generated leaves as the computer ‘sees’ them. “Search mode” attempts to automatically produce a formally defined graphical representation of the input by evaluating the visual similarity of a generated pool of candidate leaves. The system seeks to derive a possible internal structural configuration for venation based purely off a visual analysis of external shape. The iterations of the generated L-system leaves when viewed in succession appear as a hypothetical development sequence. FormaLeaf was written in Processing

MAPPING OF RESERVOIR PROPERTIES AND FACIES THROUGH INTEGRATION OF STATIC AND DYNAMIC DATA

Knowledge of the distribution of permeability and porosity in a reservoir is necessary for the prediction of future oil production, estimation of the location of bypassed oil, and optimization of reservoir management. But while the volume of data that can potentially provide information on reservoir architecture and fluid distributions has increased enormously in the past decade, it is not yet possible to make use of all the available data in an integrated fashion. While it is relatively easy to generate plausible reservoir models that honor static data such as core, log, and seismic data, it is far more difficult to generate plausible reservoir models that honor dynamic data such as transient pressures, saturations, and flow rates. As a result, the uncertainty in reservoir properties is higher than it could be and reservoir management can not be optimized. The goal of this project is to develop computationally efficient automatic history matching techniques for generating geologically plausible reservoir models which honor both static and dynamic data. Solution of this problem is necessary for the quantification of uncertainty in future reservoir performance predictions and for the optimization of reservoir management. Facies (defined here as regions of relatively uniform petrophysical properties) are common features of all reservoirs. Because the flow properties of the various facies can vary greatly, knowledge of the location of facies boundaries is of utmost importance for the prediction of reservoir performance and for the optimization of reservoir management. When the boundaries between facies are fairly well known, but flow properties are poorly known, the average properties for all facies can be determined using traditional techniques. Traditional history matching honors dynamic data by adjusting petrophysical properties in large areas, but in the process of adjusting the reservoir model ignores the static data and often results in implausible reservoir models. In general, boundary locations, average permeability and porosity, relative permeability curves, and local flow properties may all need to be adjusted to achieve a plausible reservoir model that honors all data. In this project, we will characterize the distribution of geologic facies as an indicator random field, making use of the tools of geostatistics as well as the tools of inverse and probability theory for data integration

Generating Fractals from Voronoi Diagrams

This paper describes how to generate fractal patterns by recursively creating Voronoi diagrams on a set of points. These patterns resemble such things as leaf veins and roadmaps. By varying the degree of subdivision and the distribution of points, different output patterns can be obtained. August 23, Generating Fractals from Voronoi Diagrams Ken Shirriff Computer Science Division 571 Evans Hall University of California, Berkeley Berkeley, CA 94720 Voronoi diagrams can be used to generate interesting fractal patterns which resemble leaf veins, roadmaps, and cracked pottery glaze. The patterns are generated by recursively creating a Voronoi diagram inside each Voronoi polygon. (A Voronoi diagram consists of nearest-neighbor polygons. Given a set of points, the Voronoi polygon around each point is the region of the plane closer to the selected point than to any other point [PrS85].) To generate the fractal images, we start with a small set of points and draw the Voronoi diagram of thes..