A comprehensive model for the prediction of fluid compositional gradient in two-dimensional porous media View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-02-19

AUTHORS

Mahboobeh Kiani, Shahriar Osfouri, Reza Azin, Seyed Ail Mousavi Dehghani

ABSTRACT

Compositional gradient can be described as changes in the composition of components both vertically and horizontally in a hydrocarbon reservoir. In the present work, two-dimensional compositional gradient in multi-component gas and oil mixtures is modeled. A thermodynamic model is developed based on molecular diffusion coefficients in mass diffusive flux. A combination of Sigmund and Bird correlations is considered to estimate molecular diffusion coefficients for gas mixtures. Implementing this comprehensive developed model on a gas condensate sample shows interesting differences not only in trends of component compositions but also in gradient magnitude. A real set of data from a supergiant gas condensate field is used to validate the model. It is perceived that the developed model reduces relative absolute errors to about 50%. In the next step, a comprehensive study was conducted to understand the cross effects of molecular diffusion and natural convection in gas condensate and volatile oil samples. Gas and oil samples are selected to investigate if natural convection has the same effects in different samples. It is observed that increase in natural convection causes to reduce horizontal and vertical composition gradients. This effect is more significant in gas reservoir, as methane composition varies by more than 5.2 mol% diagonally in gas condensate sample, whereas this value is about 0.45 mol% in volatile oil sample. Lower density and higher bulk velocity in gas sample cause more disturbances in flow streams of gas mixture. Evaluation of the developed model shows that the model is reliable for reservoir studies and management programs. More... »

PAGES

1-14

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13202-019-0624-y

DOI

http://dx.doi.org/10.1007/s13202-019-0624-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112228382


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