Empirical PVT Correlations For Colombian Crude Oils

Frashad, F.; LeBlanc, J.L.; Garber, James D.; Osorio, Juan Gonzalo Morales

doi:10.2118/36105-ms

Cited by 43 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Farshad, et al [9]; Al-Shammasi [10]; Dokla and Osman [3]; Labedi [11] correlations was prepared by linear or nonlinear multidimensional regression or graphic methods, often do not have high productivity and enough accuracy. Due to regional changes in crude oil compositions and properties, none of the correlations can be applied as an exact universal correlation.…”

Section: Empirical Correlations For Forecasting Of Pvt Properties Of Crude Oilmentioning

confidence: 99%

Current Approaches in Prediction of PVT Properties of Reservoir Oils

Hajirahimova

Aybeniz

2018

Review of Information Engineering and Applications

View full text Add to dashboard Cite

Bubble point pressure Bubble point oil formation factor Crude oil.PVT (pressure-volume-temperature) properties of reservoir fluids in the oil and gas industry constitute an integral part of the required data for a thorough study of the reservoir, optimally compilation of oil production and operation schemes. In the absence of PVT data that measured in laboratory conditions, empirical correlation is used to evaluate these properties. These correlations cannot be applied universally due to the differences of crude oil composition, the working condition of geographical and oil environment. In the article widespread correlations and models was investigated in the field of prediction of PVT properties of reservoir oil from different regions. Their accuracy and productivity was thoroughly analyzed too.

show abstract

Section: Empirical Correlations For Forecasting Of Pvt Properties Of Crude Oilmentioning

confidence: 99%

Current Approaches in Prediction of PVT Properties of Reservoir Oils

Hajirahimova

Aybeniz

2018

Review of Information Engineering and Applications

View full text Add to dashboard Cite

show abstract

“…Firstly, an ANN learns the behavior of a database population by selftuning its parameters in such a way that the trained ANN matches the employed data accurately. Secondly, if the data used are sufficiently descriptive 38 , the ANN provides a rapid and confident prediction as soon as a new case, which has not been "seen" by the model during the training phase, is applied. Possibly, the most important aspect of ANNs is their ability to discover patterns in data that are so obscure as to be imperceptible to normal observation and standard statistical methods.…”

Section: Advantages Of Artificial Neural Networkmentioning

confidence: 99%

Using Artificial Neural Networks to Develop New PVT Correlations for Saudi Crude Oils

Al-Marhoun

Osman

2002

All Days

View full text Add to dashboard Cite

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractReservoir fluid properties data are very important in reservoir engineering computations such as material balance calculations, well testing, reserve estimates, and numerical reservoir simulations. Ideally, those data should be obtained experimentally. On some occasions, these data are not either available or reliable; then, empirically derived correlations are used to predict PVT properties. However, the success of such correlations in prediction depends mainly on the range of data at which they were originally developed. These correlations were developed using linear, non-linear, multiple regression or graphical techniques.Recently, researchers utilized artificial neural networks (ANN) to develop more accurate PVT correlations. ANNs are biologically inspired non-algorithmic, non-digital, massively parallel distributive and adaptive information processing systems. They resemble the brain in acquiring knowledge through learning process, and storing knowledge in interneuron connection strengths.The present study presents new models developed to predict the bubble point pressure and, the formation volume factor at the bubble point pressure.The models were developed using 283 data sets collected from Saudi reservoirs. These data were divided into three groups: the first was used to train the ANN models, the second was used to crossvalidate the relationships established during the training process and, the last was used to test the models to evaluate their accuracy and trend stability. Trend tests were performed to ensure that the developed model would follow the physical laws. Results show that the developed models outperform the published correlations in terms of absolute average percent relative error, and standard deviation.

show abstract

“…De Ghetto et al 17 applied multiple, linear, and nonlinear regressions using the statistical analysis system (SAS) program to develop a bubble point pressure correlation utilizing 3700 data points from the Mediterranean Basin, Africa, Persian Gulf, and the North Sea. Frashad et al 18 applied multiple nonlinear regression techniques to develop a P b correlation utilizing 43 data points from Colombia. Almehaideb 19 proposed a P b correlation utilizing 62 data points from the United Arab Emirates (UAE).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Robust Prediction of Reservoir Bubble Point Pressure

et al. 2021

View full text Add to dashboard Cite

The bubble point pressure ( P b ) is a crucial pressure–volume–temperature (PVT) property and a primary input needed for performing many petroleum engineering calculations, such as reservoir simulation. The industrial practice of determining P b is by direct measurement from PVT tests or prediction using empirical correlations. The main problems encountered with the published empirical correlations are their lack of accuracy and the noncomprehensive data set used to develop the model. In addition, most of the published correlations have not proven the relationships between the inputs and outputs as part of the validation process (i.e., no trend analysis was conducted). Nowadays, deep learning techniques such as long short-term memory (LSTM) networks have begun to replace the empirical correlations as they generate high accuracy. This study, therefore, presents a robust LSTM-based model for predicting P b using a global data set of 760 collected data points from different fields worldwide to build the model. The developed model was then validated by applying trend analysis to ensure that the model follows the correct relationships between the inputs and outputs and performing statistical analysis after comparing the most published correlations. The robustness and accuracy of the model have been verified by performing various statistical analyses and using additional data that was not part of the data set used to develop the model. The trend analysis results have proven that the proposed LSTM-based model follows the correct relationships, indicating the model’s reliability. Furthermore, the statistical analysis results have shown that the lowest average absolute percent relative error (AAPRE) is 8.422% and the highest correlation coefficient is 0.99. These values are much better than those given by the most accurate models in the literature.

show abstract

Empirical PVT Correlations For Colombian Crude Oils

Cited by 43 publications

References 0 publications

Current Approaches in Prediction of PVT Properties of Reservoir Oils

Current Approaches in Prediction of PVT Properties of Reservoir Oils

Using Artificial Neural Networks to Develop New PVT Correlations for Saudi Crude Oils

Deep Learning Approach for Robust Prediction of Reservoir Bubble Point Pressure

Contact Info

Product

Resources

About