Hydrate Formation Risk Evaluation for New HPHT Gas Exploitation, Offshore Field, Abu Dhabi

Mehran, Fatemeh; Didanloo, Ali; Awadhi, Farhaad Al; Amer, F.; Hussieny, Hassan Al

doi:10.2118/183416-ms

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…As shown, the LSTM model indicates the lowest AAPRE of 8.422% and the highest correlation coefficient ( R ) of 0.99. On the other hand, the Velarde et al 2 and Mehran et al 24 models display AAPRE (%) of 9.0 and 9.75% and correlation coefficient ( R ) of 0.972 and 0.969, respectively. The Petrosky and Farshad 14 model shows the highest AAPRE of 76.59%.…”

Section: Results and Discussionmentioning

confidence: 93%

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Deep Learning Approach for Robust Prediction of Reservoir Bubble Point Pressure

et al. 2021

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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.

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Section: Results and Discussionmentioning

confidence: 93%

“…The correlation was based on 100 Gulf of Mexico pressure–volume–temperature (PVT) laboratory. Mehran et al 24 and Bolondarzadeh et al 25 proposed a P b correlation using 387 data points and 166 data points, respectively, from Iran fields. Malallah et al 26 used graphical alternating conditional expectation to calculate a P b .…”

Section: Introductionmentioning

confidence: 99%