Reservoir Model History Matching with Particle Swarms: Variants Study
- Lina Mohamed (Heriot Watt University) | Michael A. Christie (Heriot Watt University) | Vasily Demyanov (Heriot Watt University)
- Document ID
- Society of Petroleum Engineers
- SPE Oil and Gas India Conference and Exhibition, 20-22 January, Mumbai, India
- Publication Date
- Document Type
- Conference Paper
- 2010. Society of Petroleum Engineers
- 5.5.8 History Matching, 2.3 Completion Monitoring Systems/Intelligent Wells, 5.1 Reservoir Characterisation, 5.6.9 Production Forecasting, 1.2.3 Rock properties, 5.5 Reservoir Simulation
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History matching optimisation in Bayesian framework is a fairly recent approach to quantify uncertainty in oil industry. Currently some innovative global optimisation approaches such as evolutionary algorithms and swarm intelligence methods have gained popularity for tackling history matching problems.
Particle swarm optimisation (PSO) is a swarm intelligence approach for solving optimisation problems. In this approach particles are moving points in parameter space. The position of a particle is a candidate solution to the optimisation problem. Each particle searches for better positions in parameter space by updating its velocity according to rules originally inspired by behavioural models of the movement of flocks of birds.
Recently the PSO algorithm has shown to be a promising tool for finding acceptable multiple history matched models quickly (Mohamed et al., 2009). However, the algorithm control parameters which balance between exploitation-exploration trade-off are to be studied for history matching problems. In this paper we investigate some basic PSO variants for updating the control parameters using a real-life case study.
It is shown that PSO could be improved by optimising the PSO control parameters. Some variants converge faster to good fitting regions in parameter space leading to a fewer number of reservoir simulation runs though others maintain diversity of the reservoir models better for this reservoir example. This study helps better employing the PSO algorithm for reservoir model history matching and uncertainty quantification.
History matching optimisation in Bayesian framework is a fairly recent approach to quantify uncertainty in oil industry practices. Currently some innovative global optimisation approaches have gained popularity in research among oil companies for tackling history matching problems like evolutionary algorithms, swarm intelligence techniques and others. Stochastic techniques have been used in the petroleum engineering including Genetic algorithms (Carter and Ballester, 2004; Erbas and Christie, 2007; Romero et al., 2000), Population-Based Incremental Learning (Petrovska and Carter, 2006), Hamiltonian Monte Carlo (Mohamed et al., 2009), Evolutionary Strategies (Schulze-Riegert et al., 2001; Schulze-Riegert and Ghedan, 2007), Ant Colony Optimisation (Razavi and Jalali-Farahani, 2008a, 2008b; Hajizadeh et al.,2009a), Differential Evolution (Jahangiri, 2007; Hajizadeh et al., 2009b), and Neighbourhood Algorithm (Christie et al., 2002; Subbey et al., 2004; Rotondi et al., 2006).
Recently, Particle Swarm Optimisation (PSO) has shown promising results in uncertainty quantification problems. Mohamed et al. (2009) have compared PSO with the Neighbourhood Algorithm (NA) and have shown that PSO is a promising tool for finding acceptable multiple reservoir history matched models quickly. Kathrada (2009) tested the method on synthetic history matching problem though the uncertainty was not quantified in his study. Onwunalu and Durlofsky (2009) have tested PSO optimisation for well placement problem and have compared the results with Genetic Algorithm (GA).
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