Aumento da eficiência dos métodos següenciais de simulação condicional

O algoritmo de simulação seqüencial estocástica mais amplamente utilizado é o de simulação seqüencial Gaussiana (ssG). Teoricamente, os métodos estocásticos reproduzem tão bem o espaço de incerteza da VA Z(u) quanto maior for o número L de realizações executadas. Entretanto, às vezes, L precisa ser tão alto que o uso dessa técnica pode se tornar proibitivo. Essa Tese apresenta uma estratégia mais eficiente a ser adotada. O algoritmo de simulação seqüencial Gaussiana foi alterado para se obter um aumento em sua eficiência. A substituição do método de Monte Carlo pela técnica de Latin Hypercube Sampling (LHS), fez com que a caracterização do espaço de incerteza da VA Z(u), para uma dada precisão, fosse alcançado mais rapidamente. A técnica proposta também garante que todo o modelo de incerteza teórico seja amostrado, sobretudo em seus trechos extremos.

Análise multivariada de dados espaciais na classificação interpretativa de solos

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

A new approach to soil classification mapping based on the spatial distribution of soil properties

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Geological realism in hydrogeological and geophysical inverse modeling: A review

Scientific curiosity, exploration of georesources and environmental concerns are pushing the geoscientific research community toward subsurface investigations of ever-increasing complexity. This review explores various approaches to formulate and solve inverse problems in ways that effectively integrate geological concepts with geophysical and hydrogeological data. Modern geostatistical simulation algorithms can produce multiple subsurface realizations that are in agreement with conceptual geological models and statistical rock physics can be used to map these realizations into physical properties that are sensed by the geophysical or hydrogeological data. The inverse problem consists of finding one or an ensemble of such subsurface realizations that are in agreement with the data. The most general inversion frameworks are presently often computationally intractable when applied to large-scale problems and it is necessary to better understand the implications of simplifying (1) the conceptual geological model (e.g., using model compression); (2) the physical forward problem (e.g., using proxy models); and (3) the algorithm used to solve the inverse problem (e.g., Markov chain Monte Carlo or local optimization methods) to reach practical and robust solutions given today's computer resources and knowledge. We also highlight the need to not only use geophysical and hydrogeological data for parameter estimation purposes, but also to use them to falsify or corroborate alternative geological scenarios.

Comparison between univariate and bivariate geostatistical models for estimating catch per unit of effort (cpue): A simulation study

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A Geostatistical Study pf Alumina-Content in Ores of An Indian Iron-Ore Deposit

Mining and blending operations in the high grade iron ore deposit under study are performed to optimize recovery with minimal alumina content while maintaining required levels of other chemical component and a proper mix of ore types. In the present work the regionalisation of alumina in the ores has been studied independently and its effects on global and local recoverable tonnage as well as on alternatives of mining operations have been evaluated. The global tonnage recovery curves for blocks (20m x 20m x 12m) obtained by simulation closely approximated the curves obtained theoretically using a change of support under the discretised gaussian model. Variations in block size up to 80m x 20m x 12m did not affect the recovery as the horizontal dimensions of the blocks are small in relation to the range of the variogram. A comparison of the local tonnage recovery curves obtained through multiple conditional simulations made with that obtained by the method of uniform conditioning of block grades on an estimate of panel 100m x 100m x 12m panel grade reveals comparable results only in panels which have been well conditioned and possesing an ensemble simulation mean close to the ordinary kriged value for the panel. Study of simple alternative sequence of mining on the conditionally simulated deposit shows that concentration of mining operations simultaneously on a single bench enhances the fluctuation in alumina values of ore mined.

Towards more precise sugar beet management based on geostatistical analysis of spatial variability within fields

Within-field variation in sugar beet yield and quality was investigated in three commercial sugar beet fields in the east of England to identify the main associated variables and to examine the possibility of predicting yield early in the season with a view to spatially variable management of sugar beet crops. Irregular grid sampling with some purposively-located nested samples was applied. It revealed the spatial variability in each sugar beet field efficiently. In geostatistical analyses, most variograms were isotropic with moderate to strong spatial dependency indicating a significant spatial variation in sugar beet yield and associated growth and environmental variables in all directions within each field. The Kriged maps showed spatial patterns of yield variability within each field and visual association with the maps of other variables. This was confirmed by redundancy analyses and Pearson correlation coefficients. The main variables associated with yield variability were soil type, organic matter, soil moisture, weed density and canopy temperature. Kriged maps of final yield variability were strongly related to that in crop canopy cover, LAI and intercepted solar radiation early in the growing season, and the yield maps of previous crops. Therefore, yield maps of previous crops together with early assessment of sugar beet growth may make an early prediction of within-field variability in sugar beet yield possible. The Broom’s Barn sugar beet model failed to account for the spatial variability in sugar yield, but the simulation was greatly improved when corrected for early canopy development cover and when the simulated yield was adjusted for weeds and plant population. Further research to optimize inputs to maximise sugar yield should target the irrigation and fertilizing of areas within fields with low canopy cover early in the season.

Characterization of geothermal reservoirs’ parameters by inverse problem resolution and geostatistical simulations

BTES (borehole thermal energy storage)systems exchange thermal energy by conduction with the surrounding ground through borehole materials. The spatial variability of the geological properties and the space-time variability of hydrogeological conditions affect the real power rate of heat exchangers and, consequently, the amount of energy extracted from / injected into the ground. For this reason, it is not an easy task to identify the underground thermal properties to use when designing. At the current state of technology, Thermal Response Test (TRT) is the in situ test for the characterization of ground thermal properties with the higher degree of accuracy, but it doesn’t fully solve the problem of characterizing the thermal properties of a shallow geothermal reservoir, simply because it characterizes only the neighborhood of the heat exchanger at hand and only for the test duration. Different analytical and numerical models exist for the characterization of shallow geothermal reservoir, but they are still inadequate and not exhaustive: more sophisticated models must be taken into account and a geostatistical approach is needed to tackle natural variability and estimates uncertainty. The approach adopted for reservoir characterization is the “inverse problem”, typical of oil&gas field analysis. Similarly, we create different realizations of thermal properties by direct sequential simulation and we find the best one fitting real production data (fluid temperature along time). The software used to develop heat production simulation is FEFLOW 5.4 (Finite Element subsurface FLOW system). A geostatistical reservoir model has been set up based on literature thermal properties data and spatial variability hypotheses, and a real TRT has been tested. Then we analyzed and used as well two other codes (SA-Geotherm and FV-Geotherm) which are two implementation of the same numerical model of FEFLOW (Al-Khoury model).