Descripción del proyecto
Una nueva tecnología para mejorar la eficiencia de las prestaciones de simulación de yacimientos
Los yacimientos petrolíferos necesitan técnicas avanzadas para extraer petróleo. Para llevar a cabo extracciones en yacimientos de hidrocarburos se necesitan tres pasos: el primero está relacionado con el yacimiento energético interno, el segundo conlleva la inyección de agua o gas para soportar la presión y el tercero es el proceso para extraer el petróleo no extraído. Las simulaciones de los yacimientos se suelen llevar a cabo para evaluar el rendimiento de los métodos aplicados. Sin embargo, dichas simulaciones requieren mucho tiempo, dado que se necesitan realizar varias ejecuciones para lograr resultados óptimos. El proyecto SPM-RS, financiado con fondos europeos, desarrollará una innovadora estrategia para crear modelos de representación inteligentes a fin de reducir notablemente el tiempo de ejecución de las prestaciones de simulación de yacimientos. El proyecto combinará métodos avanzados que incluyen técnicas basadas en datos, de optimización y estadísticas.
Objetivo
Despite the progress in renewable energies, oil and gas remain the primary source of energy. Recovery from hydrocarbon reservoirs is subjected to three steps: primary, secondary and tertiary. The primary step results from the intrinsic reservoir energy; the second stage usually consists of injection of water or gas to support the pressure; and the third stage is the process of extracting the oil that cannot be recovered during the previous stages, by injecting miscible gas, thermal and chemicals. To assess the performance of implemented methods during the recovery steps, reservoir simulations are usually performed. However, these traditional simulations are known to be time-consuming, and significant number of runs is required to achieve optimal results. This project will use a combination of advanced methods including optimization, statistics and data-driven techniques, to develop a novel strategy for establishing user-friendly smart proxy models which aim at reducing significantly the run-time in reservoir simulation tasks. The project will be performed at four levels: the physical and numerical aspects of the recovery methods, sampling strategies to select runs for the proxy, learning techniques to build the proxy, and their application for optimizing recovery plans. The project has ultimate multidisciplinary aspects, including reservoir engineering, data science and environment (as CO2 storage is included in the project). The project will be carried out by the experienced researcher who worked during his PhD on the application of data-driven techniques for resolving petroleum engineering problems. The experienced researcher will collaborate with supervisors with a strong background in reservoir simulation and optimization. The transfer of knowledge from the project will have a twofold benefit, to the host institution and to the researcher. Expected results have the potential to improve the knowledge about simulation calculability using new robust approaches.
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MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinador
7491 Trondheim
Noruega