O objetivo desta dissertação é desenvolver estimadores não paramétricos para a função de covariância de dados funcionais agregados, que consistem em combinações lineares de dados funcionais que não podem ser observados separadamente. Estes métodos devem ser capazes de produzir estimativas que separem a covariância típica de cada uma das subpopulações que geram os dados, e que sejam funções não negativas definidas. Sob estas restrições, foi definida uma classe de funções de covariância não estacionarias, à qual resultados da teoria de estimação de covariância de processos estacionários podem ser estendidos. Os métodos desenvolvidos foram ilustrados com a aplicação em dois problemas reais: a estimação do perfil de consumidores de energia elétrica, em função do tempo, e a estimação da transmitância de substâncias puras em espectroscopia de infravermelho, através da inspeção de misturas, em função do espectro da luz.
Mês: fevereiro 2026
A Procedure to Parameterize High Permeability Zones in Naturally Fractured Reservoir
This paper presents a novel-methodology to compensate for the poor characterization of high-permeability structures (excess-K: vugs, karsts and super-K features), and non-fault-related-fractures, in naturally fractured Brazilian Pre-Salt carbonate reservoirs. These heterogeneities are often undetectable in well logs and seismic data, but significantly impact well performance. The methodology aims to enhance the representation of such features within dynamic simulation models, improving reservoir characterization and supporting more reliable data-assimilation and forecasting processes. The methodology involves: (1) upscaling high-fidelity fine-grid models to coarser-grids while preserving dynamic behavior, (2) identifying wells with productivity/injectivity mismatches due to a poor excess-K characterization, (3) applying a data assimilation (DA) process to minimize the mismatch between modeled and measured wells production and injection rates by updating the absolute permeability of the matrix. The novelty of the process is that the permeability field is updated by creating a mask (3D property) built by kriging permeability increments estimated from the well cells with productivity/injectivity issues. Therefore, the DA aims to find the least increments of permeability needed for each well such that when this mask is summed with the matrix permeability field all wells present good productivity/injectivity matching with history data. The methodology was applied to a dual-porosity/dual-permeability (DP/DK) compositional reservoir model. Two distinct well behaviors were observed: (1) wells located within fracture zone (12 of 33) showed good productivity/injectivity alignment with historical data and (2) the remaining 21 wells, located away from fracture zone, exhibited significantly poorer productivity/injectivity. This mismatch was attributed to the absence of excess-K features in the original matrix permeability model (Km-field). The optimization process was applied to these 21 wells. For each well a specific Ki-value was settled, defining input-points for kriging. The resulting kriged permeability correction volume (mask) was summed with the Km-field to generate an updated-permeability model. This process was repeated until all wells presented good productivity/injectivity matching with historical data. The process not only corrected the simulated dynamic responses, but also revealed key spatial permeability patterns that had not been captured in the static model. The results served as feedback to the geologists and enabled iterative improvement of the geological model, supporting a more integrated and realistic characterization. Overall, the results validate the methodology as a robust tool for incorporating unresolved high-permeability features in reservoir simulation and improving the quality of data assimilation. This study introduces an automated, iterative probabilistic data-assimilation framework that directly integrates geostatistical kriging with permeability adjustments for excess-Kstructures. The approach provides bidirectional feedback to geological modeling and allows the generation of realistic ensembles for data assimilation workflows. By combining geo-statistics within an uncertainty reduction scheme, the method addresses key modeling gaps encountered when modelling a Brazilian Pre-Salt carbonate.
A probabilistic approach for selection of well opening schedule in pre-salt reservoirs using WAG-CO2
The development of petroleum reservoirs is essential to maximize the chances of efficient oil and gas production and to meet global energy demands. In previous work, we proposed a simple procedure for fast decision-making considering a deterministic problem if there is no time to perform an optimization process. In this work, we also propose a fast procedure, but focusing on the probabilistic situation, while also estimating the impact of making a bad decision to this problem. Using representative models (RMs) of the UNISIM-II-D-BO benchmark case and a well positioning configuration proposed in the reference approach, with 21 wells, we propose a fast procedure based on the process developed for the deterministic problem. After the application of the fast procedure, we run a robust optimization on the same RMs to have a reference expected monetary value (EMV) for comparisons. At the end, we verified that there is a range of differences between 2.2% and 4.3% between the best value from the robust optimization and values of the proposed approach, depending on the RM considered. We found that a suboptimal choice of well opening schedule could lead to an EMV up to 12% lower than the best value obtained from robust optimization. Therefore, if there is enough time and computational resources, we recommend that decision makers perform the robust optimization. However, we suggest using the proposed faster procedure as an option for cases with time constraints.
7th EPIC Conference
A edição de 2025 da Epic Conference apresentou um formato um pouco diferente em relação aos anos anteriores. A segunda-feira, 1º de dezembro, foi dedicada a reuniões internas das linhas de pesquisa, realizadas em conjunto com membros do International Advisory Board do EPIC — Dr. Mike King e Prof. Robert Goldstein. No segundo dia, 2 de dezembro, o evento (fechado ao público geral e exclusivo para membros do EPIC) contou com apresentações dos responsáveis técnicos pelas quatro linhas de pesquisa: Vinicius Botechia, Vanessa Guersoni, Guilherme Chinelatto e Daniel Rojas. Os responsáveis técnicos apresentaram um panorama geral de cada linha, abordando os temas de pesquisa atuais, os principais resultados alcançados e os próximos passos previstos. O programa incluiu ainda duas sessões de pôsteres, totalizando 45 trabalhos expostos. Nas sessões de pôster, os pesquisadores do EPIC tiveram a oportunidade de apresentar suas pesquisas para seus colegas, promovendo troca de conhecimento e discussão dos resultados. A palestra de abertura foi ministrada pelo Prof. Denis Schiozer, diretor do EPIC, e contou com a participação de Juliana Bueno e Fernanda Hoerlle, coordenadoras do projeto junto à Equinor.
Para mais informações, consulte o Programa da Conferência, disponível em:
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