Prediction of asphaltene precipitation in crude oil

Zahedi, Gholamreza; Fazlali, Alireza; Hosseini, S.M.; Pazuki, G.R.; Sheikhattar, Leila

doi:10.1016/j.petrol.2009.06.023

Cited by 59 publications

(31 citation statements)

References 9 publications

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“…Among these techniques, ANNs have been used to identify relationships between permeability, measured logs and core data (Ligtenbert and Wansink, 2003), to predict water saturation from log data (Al-Bulushi et al, 2009), to predict asphaltene precipitation in crude oil (Zahedi et al, 2009) and to predict reservoir volume (Akin et al, 2008). ANNs also have been used combined with other techniques, making part of hybrid models, like described in (Chao et al, 2009) to predict borehole stability.…”

Section: Related Workmentioning

confidence: 99%

Artificial neural networks for predicting petroleum quality

Camargo

Engel

2011

jacr

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Due to limited understanding of many diagenetic processes which contributes to petroleum quality determination, mathematical models become a very useful tool to improve understanding of these processes and to improve reservoir quality predictions prior drilling. Especially for reservoir engineers and petrophysicists the distribution of porosity and permeability are very important in the formation evaluation and definition of recovery strategies and evaluation of reservoir quality. In this context, we have developed an artificial neural network based model to predict macroporosity of sandstones reservoir systems. We have used a score to quantify the importance of each feature in prediction process. This score allows creating progressive enhancement neural models, which are simpler and more accurate than conventional neural network models and multiple regressions. The main contribution of this paper is the building of a reduced model just with the most relevant features to macroporosity prediction. A dataset, containing petrographic and petrophysical characteristics, containing samples of the same formation sandstone reservoir was investigated. Study results show that progressive enhancement neural network is able to predict macroporosity with accuracy near 90%, suggesting that this technique is a valuable tool for reservoir quality prediction.

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Section: Related Workmentioning

confidence: 99%

Artificial neural networks for predicting petroleum quality

Camargo

Engel

2011

jacr

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“…The CI techniques achieved this by establishing nonlinear relations between the log measurements and the core values for prediction. CI techniques have also been reported to outperform the statistical regression tools (Mohaghegh 2000;Goda et al 2003;Osman and Al-Marhoun 2005;Zahedi et al 2009;Al-Marhoun et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead

Anifowose

Labadin

Abdulraheem

2016

J Petrol Explor Prod Technol

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Computational intelligence (CI) techniques have positively impacted the petroleum reservoir characterization and modeling landscape. However, studies have showed that each CI technique has its strengths and weaknesses. Some of the techniques have the ability to handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality. The ''no free lunch'' theorem also gives credence to this problem as it postulates that no technique or method can be applicable to all problems in all situations. A technique that worked well on a problem may not perform well in another problem domain just as a technique that was written off on one problem may be promising with another. There was the need for robust techniques that will make the best use of the strengths to overcome the weaknesses while producing the best results. The machine learning concepts of hybrid intelligent system (HIS) have been proposed to partly overcome this problem. In this review paper, the impact of HIS on the petroleum reservoir characterization process is enumerated, analyzed, and extensively discussed. It was concluded that HIS has huge potentials in the improvement of petroleum reservoir property predictions resulting in improved exploration, more efficient exploitation, increased production, and more effective management of energy resources. Lastly, a number of yet-to-be-explored hybrid possibilities were recommended.

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“…Intelligence systems have been recently used as mathematical predictive tools to quantitatively formulate the amount of asphaltene precipitation using titration data Naimi et al 2014;Ahmadi 2011Ahmadi , 2012Asoodeh et al 2014a, b;Abedini et al 2010;Zahedi et al 2009;Chamkalani et al 2013;Gholami et al 2013;Gholami et al 2014a, b;Fatahi et al 2014). Although these predictive models are valuable, the quest for greater accuracy has been always remained an issue.…”

Section: Introductionmentioning

confidence: 99%

Improving the estimation accuracy of titration-based asphaltene precipitation through power-law committee machine (PLCM) model with alternating conditional expectation (ACE) and support vector regression (SVR) elements

Gholami

Mohammadzadeh²,

Kord

et al. 2015

J Petrol Explor Prod Technol

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Asphaltene precipitation/deposition have been longstanding issues in petroleum industry which lead to decline in oil production and economical efficiency. Owing to severe undesirable issues associated with this phenomenon, it is crucial to develop a reliable, accurate, and robust approach for quantitative estimation of asphaltene precipitation. In the first section of this paper, amount of asphaltene precipitation from stock tank oil through titration process was estimated using two predictive methods of Support Vector Regression (SVR) as well as Alternating Conditional Expectation (ACE). A novel predictive method, the so-called Power-Law Committee Machine (PLCM) with constituents of SVR and ACE, was then employed for estimation of the amount of asphaltene precipitation. PLCM model assigns weight factors to each individual sub-model of SVR and ACE to specify the contribution of each particular model in the overall prediction of asphaltene precipitation. Optimal values of these weight factors were extracted by means of Genetic Algorithm (GA) since it was already inserted as the combiner in the structure of the PLCM model. To validate this predictive tool, experimental data collected from open source literature were compared against the model predictions. It was observed that PLCM model can estimate the amount of asphaltene precipitation with very high accuracy and it had more satisfactory prediction performance compared to the other models of SVR and ACE.

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Prediction of asphaltene precipitation in crude oil

Cited by 59 publications

References 9 publications

Artificial neural networks for predicting petroleum quality

Artificial neural networks for predicting petroleum quality

Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead

Improving the estimation accuracy of titration-based asphaltene precipitation through power-law committee machine (PLCM) model with alternating conditional expectation (ACE) and support vector regression (SVR) elements

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