Many-objective optimization algorithm applied to history matching

Hutahaean, Junko; Demyanov, Vasily; Christie, Mike

doi:10.1109/ssci.2016.7850215

Cited by 16 publications

(8 citation statements)

References 22 publications

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“…23 The main reason why we use MOEA/D is that the HM problem is a typical multi-objective issue. 6,24 Because we are given data for several oil wells that are correlated with each other, and the change of unknown parameters for one production data would have great impact for others, we can regard the HM problem as the model with unknown parameters, and MOEA/D should be a wise choice for optimizing the parameters in HM. The detailed MOEA/D based HM algorithm will be given in Section 3 with the specific objective function defined in Section 4.…”

Section: History Matching Based On Moea/d Optimizationmentioning

confidence: 99%

“…5 In order to overcome the disadvantages of gradient-based methods, many gradient-free algorithms have been developed, including Evolutionary Multi-objective Optimization (EMO), Simultaneous Perturbation Stochastic Approximation (SPSA), EnKF techniques, and so on. [5][6][7][8][9] Although these techniques have shown outstanding results, there are different drawbacks as well. For instance, EMO methods, which could perform global optimization, suffer from the slow speed of optimization, large numbers of initial reservoir models are required to be simulated for obtaining good estimation results in SPSA, and the ability of EnKF is highly dependent on the quality of the initial ensemble.…”

Section: Introductionmentioning

confidence: 99%

“…For all these techniques, the speed and precision are two main considerations due to the ill-posed inverse problems and non-unique solutions. A fundamental factor influencing the optimization speed and model precision results from the huge number of unknown parameters of HM, which is proportional to the number of grid blocks in a reservoir model and can be of dimension 10 4–10,6 . This high-dimensional optimization issue leads to a massive computation burden and difficulties of optimization for finding optimal parameters.…”

Section: Introductionmentioning

confidence: 99%

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Efficient history matching with dimensionality reduction methods for reservoir simulations

et al. 2017

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Oil reservoir history matching is a well-known inverse problem for predicting production by optimizing enormous unknown parameters with numerical simulation. Typically it can be formulated in a Bayesian framework with geological priors. Instead of gradient-based optimization with the possibility of converging to a local minimum, evolutionary algorithms have been introduced to globally find optimal parameters. Due to the high-dimensional parameters, the optimization could become inefficient; therefore, many dimensionality reduction algorithms have been applied in history matching. However, these methods suffer from the linear assumption or the pre-image problem, which could affect the model optimization. In this paper, based on the evolutionary algorithm termed Multi-objective Evolutionary Algorithm Based on Decomposition, which is capable of simultaneously optimizing the parameters with respect to the data of several oil wells, we propose history matching with dimensionality reduction by explicitly utilizing the nonlinear dimensionality reduction model Auto-Encoder to reduce the number of unknown parameters, which can naturally handle the pre-image problem and then improve model performance in terms of precision and complexity. Experimental results based on PUNQ-S3 data verify the efficiency of the newly proposed methods.

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Section: History Matching Based On Moea/d Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient history matching with dimensionality reduction methods for reservoir simulations

et al. 2017

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“…Even with good historical matching, there potential remains for calculating unreliable predictions because of tedious and unsteady production settings. NRS is touted as the optimal conventional method for predicting oil production by emulating and observing historical oil well production [ 14 ]. The success of achieving good performance using NRS is associated with the accuracy of the geological model and the quality of historical oil production.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting

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Abualigah

et al. 2022

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The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine–cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods.

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“…However, it is troublesome to construct an accurate static model [11,14,15]. Moreover, the parameterization techniques of the static model and the integrating method of objective components have a significant effect on history matching and reservoir prediction [14][15][16]. Although multi-objective optimization can be determined, a perfect history matching model leads to poor prediction [17].…”

Section: Introductionmentioning

confidence: 99%

Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting

et al. 2021

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Oil production forecasting is one of the essential processes for organizations and governments to make necessary economic plans. This paper proposes a novel hybrid intelligence time series model to forecast oil production from two different oil fields in China and Yemen. This model is a modified ANFIS (Adaptive Neuro-Fuzzy Inference System), which is developed by applying a new optimization algorithm called the Aquila Optimizer (AO). The AO is a recently proposed optimization algorithm that was inspired by the behavior of Aquila in nature. The developed model, called AO-ANFIS, was evaluated using real-world datasets provided by local partners. In addition, extensive comparisons to the traditional ANFIS model and several modified ANFIS models using different optimization algorithms. Numeric results and statistics have confirmed the superiority of the AO-ANFIS over traditional ANFIS and several modified models. Additionally, the results reveal that AO is significantly improved ANFIS prediction accuracy. Thus, AO-ANFIS can be considered as an efficient time series tool.

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Many-objective optimization algorithm applied to history matching

Cited by 16 publications

References 22 publications

Efficient history matching with dimensionality reduction methods for reservoir simulations

Efficient history matching with dimensionality reduction methods for reservoir simulations

Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting

Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting

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