Staff Publications (Energy, Geoscience, Infrastructure and Society)

Permanent URI for this collectionhttps://dspace-upgrade.is.ed.ac.uk/handle/10399/2821

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Adaptive algorithms for history matching and uncertainty quantification
(2014-06) Abdollahzadeh, Asaad; Christie, Professor Mike; Corne, Professor David
Numerical reservoir simulation models are the basis for many decisions in regard to predicting, optimising, and improving production performance of oil and gas reservoirs. History matching is required to calibrate models to the dynamic behaviour of the reservoir, due to the existence of uncertainty in model parameters. Finally a set of history matched models are used for reservoir performance prediction and economic and risk assessment of different development scenarios. Various algorithms are employed to search and sample parameter space in history matching and uncertainty quantification problems. The algorithm choice and implementation, as done through a number of control parameters, have a significant impact on effectiveness and efficiency of the algorithm and thus, the quality of results and the speed of the process. This thesis is concerned with investigation, development, and implementation of improved and adaptive algorithms for reservoir history matching and uncertainty quantification problems. A set of evolutionary algorithms are considered and applied to history matching. The shared characteristic of applied algorithms is adaptation by balancing exploration and exploitation of the search space, which can lead to improved convergence and diversity. This includes the use of estimation of distribution algorithms, which implicitly adapt their search mechanism to the characteristics of the problem. Hybridising them with genetic algorithms, multiobjective sorting algorithms, and real-coded, multi-model and multivariate Gaussian-based models can help these algorithms to adapt even more and improve their performance. Finally diversity measures are used to develop an explicit, adaptive algorithm and control the algorithm’s performance, based on the structure of the problem. Uncertainty quantification in a Bayesian framework can be carried out by resampling of the search space using Markov chain Monte-Carlo sampling algorithms. Common critiques of these are low efficiency and their need for control parameter tuning. A Metropolis-Hastings sampling algorithm with an adaptive multivariate Gaussian proposal distribution and a K-nearest neighbour approximation has been developed and applied.
Investigation of the biodiversity and ecology of encrusting epifauna associated with bivalve molluscs in the North-East Atlantic and Qatar
(Energy, Geoscience Infrastructure and Society, 2018-02) Al-Ghanem, Marwa Mohammed
Biogenic reefs provide hard substrates in areas that would otherwise be dominated by sediment. These reefs support a diverse assemblage of suspension feeders such as barnacles, tube worms, and bryozoans. The aim of this study is to investigate the factors influencing biodiversity, abundance, competition, spatial distribution and successional patterns of encrusting epifaunal communities in temperate and tropical regions. These include the Modiolus modiolus reefs of the North- East Atlantic temperate zone and the coral reefs and pearl oyster reefs in Qatar. In both areas, the encrusting epifaunal community was recorded, species identity confirmed with selected SEM imagery. The species abundance and competition data was analysed by multivariate approaches to give an understanding of the community complexity of horse mussel shell epifauna, and related to the microenvironmental and biogeographic context. The present study provides the first formally described taxonomical information on bryozoan fauna collected from Qatar that are exposed to extremes in temperature and salinity. Twenty-five species of bryozoans are reported from across the coastline of Qatar. These species belong to twenty-four genera in twenty-two families of the orders Ctenostomata and Cheilostomata. High precision Nonius X-ray Diffractometer was used to determine the mineral composition of Qatar Bryozoa to baseline our knowledge of Qatar bryozoan calcification and start to consider how they endure extreme ocean climate, specifically the combination of high temperature and salinity.