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Collaborating Authors
Results
On Population Diversity Measures of the Evolutionary Algorithms used in History Matching
Abdollahzadeh, Asaad (Heriot-Watt University) | Reynolds, Alan (Heriot-Watt University) | Christie, Mike (Heriot-Watt University) | Corne, David (Heriot-Watt University) | Williams, Glyn (BP) | Davies, Brian (BP)
Abstract In history matching, the aim is to generate multiple good-enough history-matched models with a limited number of simulations which will be used to efficiently predict reservoir performance. History matching is the process of the conditioning reservoir model to the observation data; is mathematically ill-posed, inverse problem and has no unique solution and several good solutions may occur. Numerous evolutionary algorithms are applied to history matching which operate differently in terms of population diversity in the search space throughout the evolution. Even different flavours of an algorithm behave differently and different values of an algorithm's control parameters result in different levels of diversity. These behaviours vary from explorative to exploitative. The need to measure population diversity arises from two bases. On the one hand maintaining population diversity in evolutionary algorithms is essential to detect and sample good history-matched ensemble models in parameter search space. On the other hand, since the objective function evaluations in history matching are computationally expensive, algorithms with fewer total number of reservoir simulations in result of a better convergence are much more favourable. Maintaining population's diversity is crucial for sampling algorithm to avoid premature convergence toward local optima and achieve a better match quality. In this paper, we introduce and use two measures of the population diversity in both genotypic and phenotypic space to monitor and compare performance of the algorithms. These measures include an entropy-based diversity from the genotypic measures and a moment of inertia based diversity from the phenotypic measures. The approach has been illustrated on a synthetic reservoir simulation model, PUNQ-S3, as well as on a real North Sea model with multiple wells. We demonstrate that introduced population diversity measures provide efficient criteria for tuning the control parameters of the population-based evolutionary algorithms as well as performance comparison of the different algorithms used in history matching.
- North America > United States (1.00)
- Europe > United Kingdom > North Sea (0.25)
- Europe > Norway > North Sea (0.25)
- (2 more...)
Abstract To make prudent decisions regarding the exploitation and management of hydrocarbon reservoirs, we need to carry out history matching, a process for conditioning the reservoir simulation model to observation data collected over time. History matching is an inverse problem which requires an optimisation technique to match the simulation results to the measurements. Many techniques have been applied to address this optimisation problem effectively and in efficient time since reservoir simulation runs are computationally expensive. Genetic algorithms (GAs) and Estimation of Distribution Algorithms (EDAs) are two popular types of evolutionary algorithms. In GAs, new candidate solutions are obtained by applying crossover and mutation operators to a population of feasible solutions according to the principle of ‘survival of the fittest’ in natural evolution. The Estimation of Distribution Algorithm (EDA) is a modern class of EA in which new candidate solutions are generating by sampling from a probability distribution inferred from the better members of the population. A suitable hybrid of the GA and EDA algorithms can combine beneficial characteristics from each of GA and EDA, while addressing each other’s sources of inefficiency. The main difference between these two EAs is the way they generate new individuals, which results in different exploration/exploitation properties. GAs may sample bad representatives of good search regions and good representatives of bad regions, while the EDA may suffer from fitting a single probability distribution to diverse and distinct regions of good solutions. The hybrid algorithm performs a cooperative search that improves the exploitation and the exploration power of both algorithms. In this paper, we applied GA, EDA, and a new hybrid GA-EDA algorithm to optimisation of three cases, a test function, the IC-Fault synthetic reservoir model, and one real reservoir, Teal South. The results show that each of these algorithms can be used for exploring the parameter search space in history matching problem. Depending on the problem type, GA, EDA, and Hybrid GA-EDA can achieve good quality matches while they perform a global seach in the space.
- Asia (0.93)
- Europe (0.68)
- North America > United States > Texas (0.28)
- North America > United States > Michigan (0.28)
- Research Report > New Finding (0.66)
- Research Report > Experimental Study (0.66)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > West Central Graben > Block 21/25 > Anasuria Cluster > Teal South Field > Skagerrak Formation (0.98)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > West Central Graben > Block 21/25 > Anasuria Cluster > Teal South Field > Heather Formation (0.98)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > West Central Graben > Block 21/25 > Anasuria Cluster > Teal South Field > Fulmar Formation (0.98)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > West Central Graben > Block 21/25 > Anasuria Cluster > Teal South Field > Forties Formation (0.98)