Virtual multiphase flow metering using diverse neural network ensemble and adaptive simulated annealing. (1st March 2018)

• Record Type: Journal Article
• Authors: AL-Qutami, Tareq Aziz; Ibrahim, Rosdiazli; Ismail, Idris; Ishak, Mohd Azmin
• Is Part Of: Expert systems with applications. Volume 93(2018)
• Page Start: 72
• Page End: 85
• Publication Date: 2018-03-01
• Subjects: Neural network -- Ensemble method -- Simulated annealing -- Multiphase flow -- Virtual flow meter -- Soft sensor; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.10.014
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Development of data-driven virtual flow meter (VFM) using diverse neural network ensembles. Adaptive simulated annealing is used for pruning and combining strategy selection. VFM can provide real-time monitoring for fields with common metering infrastructure. Achieved 4.7% and 2.5% mean absolute errors for gas and liquid flow rate estimations. The proposed method outperforms standard stacking and bagging techniques. Abstract: Real-time production monitoring in oil and gas industry has become very significant particularly as fields become economically marginal and reservoirs deplete. Virtual flow meters (VFMs) are intelligent systems that infer multiphase flow rates from ancillary measurements and are attractive and cost-effective solutions to meet monitoring demands, reduce operational costs, and improve oil recovery efficiency. Current VFMs are very challenging to develop and very expensive to maintain, most of which were developed for wells with dedicated physical meters where there exists an abundance of well test data. This study proposes a VFM system based on ensemble learning for fields with common metering infrastructure where data generated is very limited. The proposed method generates diverse neural network (NN) learners by manipulating training data, NN architecture and learning trajectory. Adaptive simulated annealing optimization is proposed to select the best subset of learners and the optimal combining strategy. The proposed method was evaluated using actual well test data and managed to achieve a remarkable performance with average errors of 4.7% and 2.4% for liquid and gas flow rates respectively. The accuracy of the developed VFM was also analyzed using cumulative deviation plot where the predictions are within a maximum deviation of ± 15%. Furthermore, the proposed ensemble method was compared to standard bagging and stacking and remarkable improvements have been observed in both accuracy and ensemble size. The proposed VFM is expected to be easier to develop and maintain than model-driven VFMs since only well test samples are required to tune the model. It is hoped that the developed VFM can augment and backup physical meters, improve data reconciliation, and assist in reservoir management and flow assurance ultimately leading to a more efficient oil recovery and less operating and maintenance costs.