The transition to sustainable energy and the goal of net-zero carbon dioxide (CO2) emissions by 2050 highlight the need to optimize energy use and reduce emissions in the oil and gas industry. Offshore platforms, especially in Brazil’s pre-salt fields, rely on energy-intensive processes that contribute significantly to CO2 emissions. This study integrates a representative pre-salt reservoir with a topside energy model to evaluate energy demand and emissions under different reservoir management strategies. A proxy embedded within the reservoir model dynamically allocates gas between reinjection and fuel consumption, while key separation stages ensure consistency with topside processes. Two production optimizations were compared: full gas recycling and partial recycling, where part of the produced gas supplies platform energy. In the partial recycle case, the final solution achieved a net present value only 0.2% lower than full recycling, while reducing energy demand and CO2 emissions by 9.3%. This approach is more representative of industry practice and becomes increasingly advantageous when costs are assigned to fuel use and emissions. The strategies also led to distinct well placement configurations and management outcomes: in the full recycle case, 70% of injectors operated with water-alternating-gas cycles of 366 days or less, whereas in partial recycling, 80% had cycles of 548 days or more. These results underscore the importance of incorporating topside gas allocation into reservoir optimization and highlight the value of integrating energy and environmental considerations to support more sustainable decision-making.
Autor: Wara Inti Pardo
Advanced Topics in Data Assimilation and Decision-Making
Convidamos a comunidade acadêmica para o workshop ‘Advanced Topics in Data Assimilation and Decision-Making’, que será realizado pelo EPIC de 25 a 29 de maio. O evento, ministrado por Auref Rostamian e Remus Hanea, proporcionará um espaço para a discussão técnica sobre temas estratégicos da área
Local: Sala Tupi (CEPETRO)
Quem poderá participar: Estudantes de pós-graduação, pesquisadores e profissionais que atuam nas seguintes áreas: engenharia de reservatórios, modelagem de subsuperfície e análise de decisão
Programação: O workshop será realizado de segunda a sexta-feira, nos períodos da manhã (09:00–12:00) e da tarde (13:00–15:00).
Programa do Workshop (em inglês)
Devido à capacidade limitada de vagas, o processo de inscrição será realizado em duas etapas. Os candidatos interessados devem preencher o formulário de pré-inscrição até o dia 30 de abril de 2026.
Importante: o envio do formulário não garante a participação. Os candidatos serão selecionados com base nas informações fornecidas e serão notificados por e-mail. Candidatos não selecionados poderão ser incluídos em uma lista de espera em caso de desistências.
Para realizar sua pré-inscrição, acesse o link:
Em caso de dúvidas, contacte-nos em epic@unicamp.br
Covariance scaling: Theory, extension, and applications to ensemble-based history matching
Ensemble-based methods have become the state-of-the-art approaches to reservoir data assimilation (RDA). In practical applications, however, they suffer from issues imposed by the limited ensemble size. Among others, one noticeable problem is significant sampling error in the sample covariance estimator when the ensemble size is substantially small relative to the dimensionality of an RDA problem. Therefore, in practical applications, enhancing the estimation accuracies of sample covariance matrices is crucial for improving the performance of ensemble-based data assimilation. In this article, we propose a novel approach, called the covariance scaling method, to mitigating sampling errors in sample covariance matrices. This approach aims to find the optimal regularization parameter that minimizes the difference between a true covariance and its sample estimate. In contrast to other similar methods in the literature, such as the covariance shrinkage method, covariance scaling can be applied to minimize the errors in approximating a generic covariance matrix, including the cross-covariance matrix, which is of particular interest to ensemble-based methods. In addition, since the optimal regularization parameter of covariance scaling depends on the true, yet unknown covariance matrix, we propose an approximate formula to calculate the regularization parameter based on some sample covariance and cross-covariance matrices, and we further extend this approximate formula to derive an alternative, tuning-free method for adaptive localization. The covariance scaling method was evaluated and compared with other similar techniques in several experiments, showing improved performance in terms of both cross-covariance estimation and ensemble data assimilation.
Model-Based Decision Analysis of Production Strategy for Heavy-Oil Field Development and Management Under Uncertainty: Waterflooding, Polymer Flooding, and Intelligent Wells
The decision-making procedure to develop and manage a production strategy is challenging because it requires a high investment and is performed under uncertainty. Heavy-oil reservoirs present low mobility and a high production of water under waterflooding. However, intelligent wells with ICVs (inflow control valves) and polymer flooding can improve the field’s performance. This work proposes a decision analysis to select the best strategy for the development of a heavy-oil field, evaluating and comparing the feasibility of waterflooding, polymers, and ICVs. We complement the nominal optimization accomplished for the base case in previous works by considering a probabilistic procedure with uncertainties, which includes the following: the generation of uncertain scenarios, the initial risk evaluation, the optimization of production strategies, a risk curve analysis, and the selection of the best strategy. A model-based reservoir simulation is used to perform the procedure, with the Expected Monetary Value (EMV) quantifying the economic returns. The case study is a sandstone heavy-oil reservoir (13° API) that represents a real Brazilian offshore field. Based on the EMV, we selected the polymer flooding strategy for this case study. However, since better water management was achieved with small differences to the polymer strategy, the option of using the ICVs in combination with polymer could be attractive depending on the various objectives of an oil field.