Artificial Neural Network Model for Predicting Bottomhole Flowing Pressure in Vertical Multiphase Flow

Abstract

An Artificial Neural Network Model for Predicting Bottomhole Flowing Pressure in Vertical Multiphase Flow El-Sayed A. Osman; El-Sayed A. Osman King Fahd University of Petroleum & Minerals Search for other works by this author on: This Site Google Scholar Mohammed Abdalla Ayoub; Mohammed Abdalla Ayoub King Fahd University of Petroleum & Minerals Search for other works by this author on: This Site Google Scholar Mohamed Ahmed Aggour Mohamed Ahmed Aggour Petroleum Institute Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Middle East Oil and Gas Show and Conference, Kingdom of Bahrain, March 2005. Paper Number: SPE-93632-MS https://doi.org/10.2118/93632-MS Published: March 12 2005 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Osman, El-Sayed A., Ayoub, Mohammed Abdalla, and Mohamed Ahmed Aggour. "An Artificial Neural Network Model for Predicting Bottomhole Flowing Pressure in Vertical Multiphase Flow." Paper presented at the SPE Middle East Oil and Gas Show and Conference, Kingdom of Bahrain, March 2005. doi: https://doi.org/10.2118/93632-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Middle East Oil and Gas Show and Conference Search Advanced Search AbstractAccurate prediction of pressure drop in vertical multiphase flow is needed for effective design of tubing and optimum production strategies.Several correlations and mechanistic models have been developed since 1950.In addition to the limitations on the applicability of all existing correlations, they all fails to provide the desired accuracy of pressure drop predictions.The recently developed mechanistic models provided little improvements in pressure drop prediction over the empirical correlations.However, there is still a need to further improve the accuracy of prediction for a more effective and economical design of wells and better optimization of production operations.This paper presents an Artificial Neural Network (ANN) model for prediction of the bottom-hole flowing pressure and consequently the pressure drop in vertical multiphase flow.The model was developed and tested using field data covering a wide range of variables.A total of 206 field data sets collected from Middle East fields; were used to develop the ANN model. These data sets were divided into training, cross validation and testing sets in the ratio of 3:1:1. The testing subset of data, which were not seen by the ANN model during the training phase, was used to test the prediction accuracy of the model and compare its performance against existing correlations and mechanistic models.The results showed that the present model significantly outperforms all existing methods and provides predictions with higher accuracy.This was verified in terms of highest correlation coefficient, lowest average absolute percent error, lowest standard deviation, lowest maximum error, and lowest root mean square error.A trend analysis was also conducted and showed that the present model provides the expected effects of the various physical parameters on pressure drop.IntroductionA reliable and accurate way of predicting pressure drop in vertical multiphase flow is essential for the proper design of well completions and artificial-lift systems and for optimization and accurate forecast of production performance. Because of the complexity of multiphase flow, mostly empirical or semi-empirical correlations have been developed for prediction of pressure drop.Numerous correlations have been developed since the early 1940s. Most of these correlations were developed under laboratory conditions and are, consequently, inaccurate when scaled-up to oil field conditions[1].The most commonly used correlations are those of (Hagedorn and Brown[2]; Duns and Ros[3]; Orkiszewski[4]; Beggs and Brill[5]; Aziz and Govier[6]; Mukherjee and Brill correlation[7]). Numerous studies were done to evaluate and study the applicability of those correlations under different ranges of data[8–15].Most researchers agreed upon the fact that no single correlation was found to be applicable over all ranges of variables with suitable accuracy[1].It was found that correlations are basically statistically derived, global expressions with limited physical considerations, and thus do not render them to a true physical optimization.Mechanistic models are semi-empirical models used to predict multiphase flow characteristics such as liquid hold up, mixture density, and flow patterns. Based on sound theoretical approach, most of these mechanistic models were generated to outperform the existing empirical correlations.The most widely used mechanistic models are those of Hasan and Kabir[16]; Ansari et al.[17].; Chokshi et al.[18]; Gomez et al.[19]. Other studies were conducted to evaluate the validity of such mechanistic models[20–22].Generally, each of these mechanistic models has an outstanding performance in specific flow pattern prediction and that is made the adoption for certain model of specific flow pattern by investigators to compare and yield different, advanced and capable mechanistic models. Keywords: water rate, relative error, accuracy, reservoir surveillance, artificial neural network model, bottomhole pressure, oil rate, production monitoring, pressure drop, empirical correlation Subjects: Well & Reservoir Surveillance and Monitoring, Information Management and Systems, Neural networks This content is only available via PDF. 2005. Society of Petroleum Engineers You can access this article if you purchase or spend a download.