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Content available remote Modelowanie właściwości elektromagnetycznych materiałów nadprzewodnikowych
PL
W artykule przedstawiono wybrane prace teoretyczno-obliczeniowe zrealizowane przez Autora w Instytucie Elektrotechniki, poświęcone modelowaniu właściwości elektromagnetycznych materiałów nadprzewodnikowych. Przedyskutowano opracowane modele teoretyczne dotyczące transportu prądu przez materiały nadprzewodnikowe zarówno nisko jak i wysokotemperaturowe.
EN
For efficient reservoir management and long-term field development strategies, most geologists and asset managers pay special attention to reservoir chance of success. To minimise this uncertainty, a good understanding of reservoir presence and adequacy is required for better ranking of infill opportunities and optimal well placement. This can be quite challenging due to insufficient data and complexities that are typically associated with areas with compounded tectonostratigraphic framework. For the present paper, data analysis and variography were used firstly to examine possible geological factors that determine directions in which reservoirs show minimum heterogeneity for both discrete and continuous properties; secondly, to determine the maximum range and degree of variability of key reservoir petrophysical properties from the variogram, and thirdly, to highlight possible geological controls on reservoir distribution trends as well as areas with optimal reservoir quality. Discrete properties evaluated were lithology and genetic units, while continuous properties examined were porosity and net-to-gross (NtG). From the variogram analysis, the sandy lithology shows minimum heterogeneity in east-west (E–W) and north-south (N–S) directions, for Upper Shoreface Sands (USF) and Fluvial/Tidal Channel Sands (FCX/TCS), respectively. Porosity and NtG both show the least heterogeneity in the E–W axis for reservoirs belonging to both Upper Shoreface and Fluvial Channel environments with porosity showing a slightly higher range than NtG. The vertical ranges for both continuous properties did not show a clear trend. The Sequential Indicator Simulation (SIS) and Object modelling algorithm were used for modelling the discrete properties, while Sequential Gaussian Simulation (SGS) was used for modelling of the continuous properties. Results from this exercise show that depositional environment, sediment provenance, topographical slope, sub-regional structural trends, shoreline orientation and longshore currents, could have significant impacts on reservoir spatial distribution and property trends. This understanding could be applied in reservoir prediction and for generating stochastic estimates of petrophysical properties for nearby exploration assets of similar depositional environments.
EN
Workflow of 3D static reservoir modelling, with the emphasis on the different types of data integration is discussed in the paper. The example of two-stage integration of geophysical (seismic and well log), petrophysical, sedimentological and reservoir engineering data within a framework of 3D static reservoir models is given. The main goal of the first stage of data integration is to obtain consistent well profiles of reservoir parameters such as: porosity, permeability, facies, water saturation. For that purpose the example of use of the following data types were presented: well logs, lab data, sedimentological interpretation, well tests and mud logging results. The second stage includes construction of structural model, defining the geometry of analyzed geological object, facies/lithological modelling; calibration of facial and petrophysical well profiles with 3D seismic, usually represented by results of seismic inversion, seismic attributes and meta-attributes and finally, the calculation of the spatial distribution of analyzed reservoir properties.
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