TY - GEN
T1 - Evaluation of below bubble point viscosity correlations & construction of a new neural network model
AU - Ayoub, M. A.
AU - Raja, D. M.
AU - Al-Marhoun, M. A.
PY - 2007
Y1 - 2007
N2 - This paper, precisely, evaluates two famous below bubble point viscosity correlations and tries to create a new Neural Network model for estimating this property. The new created model outperforms the two investigated correlations namely Khan Model (1987) and Labedi Model (1992). The new technique (Artificial neural network) found to be successful in developing a model for predicting viscosity below bubble point with an outstanding correlation coefficient of 99.3%. A limited number of data points have been collected from Pakistani fields in order to construct, test, and validate the model. Viscosity from 99 sets of differential liberation data covering a wide range of pressure, temperature, and oil density were used to validate the correlations and to develop the new model. A series of statistical and graphical analysis were conducted also to show the superiority of the model that has been formulated through an Artificial Neural Network technique. A thorough literature review is also made to check the applicability of the existing correlations and their drawbacks.
AB - This paper, precisely, evaluates two famous below bubble point viscosity correlations and tries to create a new Neural Network model for estimating this property. The new created model outperforms the two investigated correlations namely Khan Model (1987) and Labedi Model (1992). The new technique (Artificial neural network) found to be successful in developing a model for predicting viscosity below bubble point with an outstanding correlation coefficient of 99.3%. A limited number of data points have been collected from Pakistani fields in order to construct, test, and validate the model. Viscosity from 99 sets of differential liberation data covering a wide range of pressure, temperature, and oil density were used to validate the correlations and to develop the new model. A series of statistical and graphical analysis were conducted also to show the superiority of the model that has been formulated through an Artificial Neural Network technique. A thorough literature review is also made to check the applicability of the existing correlations and their drawbacks.
UR - https://www.scopus.com/pages/publications/52049087140
UR - https://www.scopus.com/pages/publications/52049087140#tab=citedBy
U2 - 10.2118/108439-ms
DO - 10.2118/108439-ms
M3 - Conference contribution
AN - SCOPUS:52049087140
SN - 9781604238594
T3 - SPE - Asia Pacific Oil and Gas Conference
SP - 196
EP - 205
BT - Society of Petroleum Engineers - SPE Asia Pacific Oil and Gas Conference and Exhibition 2007 "Resources, Professionalism, Technology
PB - Society of Petroleum Engineers (SPE)
T2 - Society of Petroleum Engineers - SPE Asia Pacific Oil and Gas Conference and Exhibition 2007 "Resources, Professionalism, Technology: Time to Deliver"
Y2 - 30 October 2007 through 1 November 2007
ER -