Reservoir Model History Matching With Particle Swarms

Mohamed, Lina Mahgoub Yahya; Christie, Michael; Demyanov, Vasily

doi:10.2118/129152-ms

Cited by 39 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, we are no longer looking for a single set of parameters which yield a good match; instead we are now searching for many realizations of the reservoir simulation model with different geological and petrophysical properties that can reproduce the history of a reservoir. Other stochastic algorithms that have been examined in the reservoir engineering community are neighborhood algorithm (Subbey and Christie, 2003), genetic algorithms (Erbas and Christie, 2007), scatter search (Sousa, 2007), tabu search (Yang et al, 2007), particle swarm optimization (Mohamed et al, 2010) and ant colony optimization (Hajizadeh et al, 2010). Stochastic modeling is often seen as a good choice to be used to generate multiple realizations (Bush and Carter, 1996).…”

Section: Introductionmentioning

confidence: 99%

Automated History Matching Using Combination of Adaptive Neuro Fuzzy System (ANFIS) and Differential Evolution Algorithm

Rammay

Abdulraheem

2014

All Days

View full text Add to dashboard Cite

History Matching is one of the most important steps to calibrate the Reservoir Simulation models. There are hundreds of thousands of grid blocks in reservoir simulation model, therefore manual History matching is not feasible for these cases. Automatic history matching uses mathematical algorithm and techniques to adjust reservoir engineering data rather than direct engineering judgment. History matching problem is highly non-unique and in order to assess the Non-Uniqueness of the problem, Multiple Models History Matched Models are required.The History Matching with Multiple Models requires a very large number of reservoir simulations which can yield slow Field developments and large computational effort, which are very costly to make effective decision in terms of time and money. Often due to the lack of availability of multiple history matched models, there would be a lack in proper assessment of non-uniqueness, and it costs the wrong decision in terms of dry well or low productivity well. To minimize this problem some techniques such as Spline, Neural Networks, Kriging and Experimental Design, have been presented in the literature to be used as proxies to reservoir simulator. But all proxies can deliver only one history matched model, which can be highly non unique.In this paper combination of Adaptive Neuro-Fuzzy System (ANFIS) and Differential Evolution algorithm is proposed, which can minimize the above problems, by obtaining various history matched models with minimum number of simulation runs and by this way non unique problem can be minimized and accurately assessed in less time. Inputs for ANFIS model are the reservoir simulation model parameters like porosity and permeabilities of three layers and output would be differences between observed and simulated field pressure and oil production rates, water production rates of wells, which are merged together to form an objective function of error or misfit. Differential Evolution obtains Global optimum results through ANFIS, so it cuts down the expensive simulation time. The results showed the large potential of application of the technique introduced.

show abstract

Section: Introductionmentioning

confidence: 99%

Automated History Matching Using Combination of Adaptive Neuro Fuzzy System (ANFIS) and Differential Evolution Algorithm

Rammay

Abdulraheem

2014

All Days

View full text Add to dashboard Cite

show abstract

“…It provides many different equi-probable geological realizations and provides the opportunity to balance exploration and exploitation while searching for optimal solutions. Other stochastic algorithms that have been examined in the reservoir engineering community are neighborhood algorithm (Subbey and Christie, 2003), genetic algorithms (Erbas and Christie, 2007), scatter search (Sousa, 2007), tabu search (Yang et al, 2007), particle swarm optimization (Mohamed et al, 2010) and ant colony optimization (Hajizadeh et al, 2010). Stochastic population-based systems are composed of multiple intelligent individuals that utilize the interactions among members to improve the quality of the solutions.…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution for Assisted History Matching Process: SAGD Case Study

Nghiem

Mirzabozorg²,

Yang

et al. 2013

All Days

View full text Add to dashboard Cite

SAGD (Steam Assisted Gravity Drainage) is an efficient and proven technology to recover vast reserves of Alberta's oil sands. Because of its thermal and compositional effects, numerical simulation of the SAGD process requires extensive computational run time, especially in a history matching framework. Therefore, it is beneficial to use an optimization technique that yields faster convergence and better match-quality solutions.This paper presents a new population-based optimization technique, called differential evolution, in the assisted history matching process. Differential evolution belongs to the class of evolutionary algorithms in the continuous parameter space that has been used successfully in a large range of engineering optimization problems outside the oil industry. Differential evolution converges faster than many other global optimization methods. It requires fewer control variables, is robust and easy to use, and lends itself very well to parallel computing. We applied the differential evolution technique to a SAGD case study to history match saturation and temperature profiles as well as cumulative oil and water production and cumulative SOR. The results show that it is an excellent optimization technique for obtaining multiple good history matched models, which allow the assessment of uncertainty for the forecast stage. The match-quality of the history matched models obtained with differential evolution has been compared to the results of the particle swarm optimization method that is widely used in history matching. The comparison shows that differential evolution offers much better match-quality solutions with much lower number of simulation runs.

show abstract

“…Various optimisation methods have been used in history matching, such as simulated annealing (Ouenes et al, 1993), genetic algorithm (GA) (Romero and Carter, 2001), particle swarm (Mohamed et al, 2010), ant colony (Hajizadeh et al, 2011) and several classical optimisation methods-for example: Gauss-Newton, Levenberg-Marquardt and Limited-memory, Broyden-Fletcher-Goldfarb-Shanno (LBFGS) (He et al, 1997, Zhang et al, 2005. Each method has its own capabilities and weaknesses, and the method selection should be based on conditions-especially the shape of landscape.…”

Section: Introductionmentioning

confidence: 99%

Reservoir characterisation using artificial bee colony optimisation

et al. 2012

View full text Add to dashboard Cite

To obtain an accurate estimation of reservoir performance, the reservoir should be properly characterised. One of the main stages of reservoir characterisation is the calibration of rock property distributions with flow performance observation, which is known as history matching. The history matching procedure consists of three distinct steps: parameterisation, regularisation and optimisation. In this study, a Bayesian framework and a pilot-point approach for regularisation and parameterisation are used. The major focus of this paper is optimisation, which plays a crucial role in the reliability and quality of history matching.Several optimisation methods have been studied for history matching, including genetic algorithm (GA), ant colony, particle swarm (PS), Gauss-Newton, Levenberg-Marquardt and Limited-memory, Broyden-Fletcher-Goldfarb-Shanno. One of the most recent optimisation algorithms used in different fields is artificial bee colony (ABC). In this study, the application of ABC in history matching is investigated for the first time. ABC is derived from the intelligent foraging behaviour of honey bees. A colony of honey bees is comprised of employed bees, onlookers and scouts. Employed bees look for food sources based on their knowledge, onlookers make decisions for foraging using employed bees' observations, and scouts search for food randomly.To investigate the application of ABC in history matching, its results for two different synthetic cases are compared with the outcomes of three different optimisation methods: realvalued GA, simulated annealing (SA), and pre-conditioned steepest descent. In the first case, history matching using ABC afforded a better result than GA and SA. ABC reached a lower fitness value in a reasonable number of evaluations, which indicates the performance and execution-time capability of the method. ABC did not appear as efficient as PSD in the first case. In the second case, SA and PDS did not perform acceptably. GA achieved a better result in comparison to SA and PSD, but its results were not as superior as ABC's.ABC is not concerned with the shape of the landscape, that is, whether it is smooth or rugged. Since there is no precise information about the landscape shape of the history matching function, it can be concluded that by using ABC, there is a high chance of providing high-quality history matching and reservoir characterisation.

show abstract

Reservoir Model History Matching With Particle Swarms

Cited by 39 publications

References 24 publications

Automated History Matching Using Combination of Adaptive Neuro Fuzzy System (ANFIS) and Differential Evolution Algorithm

Automated History Matching Using Combination of Adaptive Neuro Fuzzy System (ANFIS) and Differential Evolution Algorithm

Differential Evolution for Assisted History Matching Process: SAGD Case Study

Reservoir characterisation using artificial bee colony optimisation

Contact Info

Product

Resources

About