A Neural Network Model and an Updated Correlation for Estimation of Dead Crude Oil Viscosity

Naseri, Ali; Yousefi, Seyed Hamidreza; Sanaei, Alireza; Gharesheikhlou, A. A.

doi:10.5419/bjpg2012-0003

Cited by 27 publications

(10 citation statements)

References 10 publications

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“…The analysis based on samples from 162 wells was collected from nine different oilfields of Upper Assam Basin of Oil India Limited. The viscosity measurement of the crude oil samples was performed using a rotational viscometer and the data covered a viscosity from 2 to 120 cp and API gravity in [27]. The new proposed correlation was developed by the aid of non-linear multivariable regression and optimization based on extensive data set that covers all Iranian oil reservoirs.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Oil Viscosity Based Various Empirical Correlations for Azerbaijan Crude Oils

İmamverdiyev¹,

Hajirahimova²

2019

IJITCS

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In the oil industry, the evaluation of oil viscosity is one of the important issues. Generally, the viscosity of crude oil depends on pressure and temperature. In this study, the prediction issue of oil viscosity has been viewed applying empirical correlations as Beggs-Robinson, Labedi, modified Kartoatmodjo, Elsharkawy and Alikhan, Al-Khafaji. Original field data reports have been obtained from Guneshli oil field of Azerbaijan sector of Caspian Basin. The correlation models used in the evaluation of viscosity of Azerbaijan oil have been implemented in the Python software environment. The obtained values on empirical correlations have been compared to experimental data obtained from Guneshli oil field. Statistical analysis in terms of percent absolute deviation (% AD) and the percent absolute average deviation (% AAD), mean absolute error (% MAE), correlation coefficient (% 2 R ), root mean square error (% RMSE) are used to subject the evaluation of the viscosity correlations. According to statistical analysis, it has been known that the Beggs-Robinson model has shown the lowest value on AAD (10.5614%), MAE (12.4427 %), RMSE (20.0853 %). The Labedi model has presented the worst result on every four criterions. Even though the Elsharkawy-Alikhan model has presented the highest result (99.9272%) on correlation coefficient, in the evaluation of viscosity of Azerbaijan crude oil, the Beggs-Robinson model can be considered more acceptable.

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Section: Related Workmentioning

confidence: 99%

Evaluation of Oil Viscosity Based Various Empirical Correlations for Azerbaijan Crude Oils

İmamverdiyev¹,

Hajirahimova²

2019

IJITCS

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“…The neural networks developed had a correlation coefficient of 0.99 and performed better than existing empirical correlations [3], [4]. Similar studies with neural networks to predict viscosity of Iranian oils [5]- [7] and Omani oils [8] have shown to perform better than existing correlations. Besides this the entire viscosity curves have also been shown to be accurately predicted for Canadian oilfields using neural networks and SVM [9] thereby establishing the reliable use of data mining techniques to predict crude oil viscosity.…”

Section: Literature Reviewmentioning

confidence: 99%

Bagging Ensemble Model for Prediction of Dead Oil Viscosity

Gulyani¹,

Kumar²,

Fathima³

2017

IJCEA

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Abstract-Dead oil viscosity is a critical design factor for oilfields and refineries. From available literature, crude oil viscosity is found to be a strong function of pressure, temperature, bubble-point pressure, gas-oil ratio, gas gravity, and oil gravity. Oil viscosity is generally determined from laboratory experiments and empirically derived correlations.Reliable measurements of dead oil viscosity are difficult to obtain due to lack of lab equipment or liquid samples. Based on API oil gravity & temperature, various correlations have been used to predict dead oil viscosity. In addition to correlations, recently data mining techniques like Artificial Neural Networks (ANN) and Support Vector Machines (SVM) have been used to predict petroleum viscosity. The aim of this paper is to introduce the ensemble model of bagging as an important data mining technique to predict dead oil viscosity. The ensemble model predicted the viscosity accurately with a correlation coefficient of 0.99, an accuracy that is comparable to that of ANN as found in literature. It was also observed that bagging lowered the relative error of the base classifier (ANN) from 10% to about 8%, thereby stabilizing the ANN while retaining its accuracy.

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“…It is also used in correlating the reservoir fluid properties without physically relating to the independent variables to lower the cost of field sampling and expensive laboratory experimental procedures. Naseri et al (2012) published ANN-based correlation to predict the viscosity of crude oil at different operation conditions and isothermal subsurface temperatures and it gave a promising result in estimating the viscosity of the large set of Iranian crudes. Nada et al (2012) conducted a survey by collecting 104 real data sets to validate propagation of neural network to derive correlation that predicts bubble point pressure for Iraqi fields with average error percentage about 6.5%.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Predicating Water-flooding Performance into Stratified Reservoirs Using a data driven proxy model

Omar¹,

Chen²,

Khalifa³

2017

J. Petroleum Gas Eng.

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Fundamentally, all mathematical models employed in analysis of water-flooding performance implied assumptions to exclude one or more forces to cope with the reservoir heterogeneity. In the beginning of the survey, a series of sensitivity investigations were undertaken to examine the parameters that affect the water-flooding performance in stratified reservoirs. The factors were designed to measure the impact of each force that contributed in water-flooding process. The forces are: viscous force, the force of gravity and capillary forces. Additionally, the cross flow phenomena which result from the viscosity and gravity segregation are investigated. The parameters that affected performance to a high degree were sampled randomly to create a knowledge domain with specific inputs and target outputs. In this case, it was the final oil recovery factor by reservoir simulator tool. This domain is used as input (supplied solved problems) to the proxy model (artificial neural network) for adjusting the magnitude of the connections between the neurons during training process to generate a model that can predict the performance of the water-flooding in such reservoirs within a limited range with very minor percentage of error. This model can anticipate the performance of the water-flooding process in heterogeneous reservoir when supplied with 12 key parameters (mobility ratio, density of fluids, dipping angle, permeability ordering, heterogeneity degree, injection rate, reservoir thickness, porosity, and permeability in 3D and reservoir depth). The average absolute percentage of error is about 4.6% particularly and error standard deviation about 8.7% with correlation coefficient between result collected from simulation and ANN is about 99.1%, when the system parameters are within the range of data that was used during the training.

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A Neural Network Model and an Updated Correlation for Estimation of Dead Crude Oil Viscosity

Cited by 27 publications

References 10 publications

Evaluation of Oil Viscosity Based Various Empirical Correlations for Azerbaijan Crude Oils

Evaluation of Oil Viscosity Based Various Empirical Correlations for Azerbaijan Crude Oils

Bagging Ensemble Model for Prediction of Dead Oil Viscosity

Predicating Water-flooding Performance into Stratified Reservoirs Using a data driven proxy model

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