A New Methodology for Prediction of Bottomhole Flowing Pressure in Vertical Multiphase Flow in Iranian Oil Fields Using Artificial Neural Networks (ANNs)

Mohammadpoor, Mehdi; Shahbazi, Khalil; Torabi, Farshid; Firouz, Ali Reza Qazvini

doi:10.2118/139147-ms

Cited by 20 publications

(14 citation statements)

References 10 publications

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“…It should be noted that some part of the field data was missing, which may affect the comparison results, such as wellhead temperature, which was back-calculated using a geothermal gradient. All the data was found and selected from the production logging file Poettman and Carpenter (1952) 741.0% 4.949 Baxendell and Thomas (1961) 741.0% 4.949 Fancher and Brown (1963) 80.0% 0.578 Hagedorn and Brown (1965) 24.7% 0.156 Gray (1978) 18.8% 0.179 Dukler et al (1969) 133.3% 1.188 Duns and Ros (1963) 146.8% 2.005 Orkiszewski (1967) 67.3% 0.457 Beggs and Brill (1973) 23.8% 0.222 Mukherjee and Brill (1985) 23.2% 0.170 Aziz, Govier and Fogarasi (1972) 86 Results from some papers share the common point that artificial neural network techniques prove to be better tools to deal with multiphase flow problems than traditional approaches, such as correlations and mechanistic modeling (Ternyik et al, 1995, Shippen and Scott, 2002, Osman, 2004, Osman, Ayoub and Aggour, 2005, Ozbayoglu and Ozbayoglu, 2007, Mohammadpoor et al, 2010, Ashena et al, 2010, Al-Shammari, 2011. But still, even with ANN options, the limitations of prediction range remain.…”

Section: Field Data Validationmentioning

confidence: 96%

“…Third, many papers suggest that the prediction performance of ANN model is superior to multiphase correlations or mechanistic models (Al-Shammari, 2011;Ashena et al, 2010;Mohammadpoor et al, 2010;Osman, Ayoub and Aggour, 2005;Ozbayoglu and Ozbayoglu, 2007;Ternyik et al, 1995). The conclusion from these points in that it is acceptable to implement ANN models into multiphase correlation calculation procedures.…”

Section: Artificial Neural Network (Ann) Approachesmentioning

confidence: 99%

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A Combined Bottom-hole Pressure Calculation Procedure Using Multiphase Correlations and Artificial Neural Network Models

2014

SPE Annual Technical Conference and Exhibition

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Artificial neural network (ANN) techniques have been adopted to predict bottom-hole pressures and have proved to have better, or at a minimum equivalent prediction performance than conventional prediction methods such as multiphase correlations and mechanistic modeling. With the applied design, the use of ANN techniques can be more fully investigated to aid in multiphase flow related issues. In this study, different artificial neural network models have been trained to solve two of the major problems of bottom-hole pressure calculationsflow regime recognition and pressure gradient prediction.A support vector machine model was trained for flow regime classification. In order to include inclination angle effects on flow regime transition, the model uses inclination angle and as well as gas and liquid velocity numbers as input variables. Four possible different flow patterns were considered for upward and horizontal multiphase flow, including bubble flow, slug flow, annular mist flow and stratified flow. Some 3-D plots of all the possible flow patterns at all inclination angles (from horizontal to upward vertical) within the studied condition range were generated based on model outputs.Previous back-propagation neural network models in the literature have been modified to fit into piece-wise calculation procedures of multiphase correlations to achieve higher prediction accuracy and broaden the prediction range. The model training requires wellsegment-scale data sets, which contain pressure gradients as the model output variable and the model input variables, including inclination angle, liquid superficial velocity, gas superficial velocity, gas-liquid surface tension, liquid density, specific gravity of free gas, liquid viscosity, gas viscosity, average pressure and average temperature. The training data was collected from literature and as well as some piece-wise calculation results of multiphase correlations. Different back-propagation neural network model structures have been tested to find a suitable neuron number on hidden-layer. Two pressure gradient prediction models iii were trained for slug flow and annular mist flow.Finally, a combined bottom-hole calculation procedure was designed based on multiphase correlations and trained artificial neural network models. The statistical test results using the collected data show that the combined procedure has the best prediction performance than the eleven multiphase correlations studied in this work with the lowest average absolute percent error of 3.1% and standard deviation of 0.034. Some independent field data was used to test the extendability of the combined procedure prediction range. Comparing to the multiphase correlations, the combined procedure gave fairly accurate predictions with an average absolute percent error of 23.0% and a standard deviation of 0.176. To facilitate field application, a multiphase flow bottom-hole pressure calculator with a user graphic interface was developed.

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Section: Field Data Validationmentioning

confidence: 96%

Section: Artificial Neural Network (Ann) Approachesmentioning

confidence: 99%

A Combined Bottom-hole Pressure Calculation Procedure Using Multiphase Correlations and Artificial Neural Network Models

2014

SPE Annual Technical Conference and Exhibition

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“…The recent progress and success of utilizing ANNs to solve various complicated engineering problems has drawn attention to its potential applications in the petroleum industry. ANNs have been successfully utilized in several areas such as permeability predictions, well testing, PVT properties prediction, identification of sandstone lithofacies, improvement of gas well production, prediction and optimization of well performance, and integrated reservoir characterization …”

Section: Soft Computing‐based Approachmentioning

confidence: 99%

A new soft computing‐based approach to predict oil production rate for vapour extraction (VAPEX) process in heavy oil reservoirs

Mohammadpoor

Torabi

2018

Can J Chem Eng

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There are vast resources of heavy oil and bitumen reservoirs in the Western Canadian Basin. For many of them up to 95 % of reserves still remain in place, and by considering the increase in future energy demand these abundant resources can be considered as potential sources for future years. Recently, solvent‐based heavy oil recovery methods such as vapour extraction (VAPEX) have gained attention due to the potential environmental and economic assets over thermal processes. Due to the complexity of the mechanisms associated with the solvent injection process (i.e. diffusion and gravity drainage processes), such models are incapable of accurately predicting the production rate during the VAPEX process. In this study, the artificial neural networks (ANN) technique is utilized to tackle the limitations that analytical methods encounter while predicting the complex relationships, where there is uncertainty, imprecision, and partial truth. Hence, in the first phase of the research a comprehensive experimental study in two large‐scale, visual rectangular VAPEX models was carried out by utilizing various injection solvents. Based on an extensive literature review and experimental results, the drainage height, solvent type, permeability, porosity, and heavy oil viscosity were considered as the inputs of the model to predict the heavy oil production rate as the output of the model. After trying different training scenarios, it was found that the back‐propagation learning algorithm can be successfully used to predict the ultimate recovery factor after implementing the VAPEX method in the heavy oil system of interest.

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“…In addition, many of these correlations are too complicated to be applicable in the field . Since the majority of oil reservoirs are severely depleted and, in the most cases, multiphase flow is encountered during production, developing a very accurate model for future production design is urgently needed …”

Section: Introductionmentioning

confidence: 99%

Machine learning models to predict bottom hole pressure in multi‐phase flow in vertical oil production wells

Ahmadi

Chen

2019

Can J Chem Eng

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The precise estimation of a pressure drop in vertical multiphase flowing oil wells plays a crucial role in designing robust production facilities and evaluating optimum production plans. A significant amount of research has been conducted on determining a pressure drop via calculating the bottom hole pressure (BHP); these different methods as well as numerical, analytical, semi‐analytical, and empirical correlations can be used for doing this task. Unfortunately, most of those correlations are unable to provide reasonable precision when calculating BHP and, consequently, improvements are still required. To predict BHP in vertical wells, several hybrids of meta‐heuristic optimization methods and a bio‐inspired connectionist approach, i.e., artificial neural network (ANN), are employed. The main goal of these optimization algorithms is to optimize the parameters of the ANN models, i.e., weights and biases, to improve their performance. Based on the obtained outputs and various robustness indexes, a hybrid genetic algorithm and particle swarm optimization (HGAPSO) is highly precise and has a maximum of 10 % error compared to measured pressure data. The results of this work reveal the capability of those hybrid connectionist models for predicting the BHP of multi‐phase flow in vertical wells; these results demonstrate the promising use of connectionist methods in commercial production software in the near future.

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A New Methodology for Prediction of Bottomhole Flowing Pressure in Vertical Multiphase Flow in Iranian Oil Fields Using Artificial Neural Networks (ANNs)

Cited by 20 publications

References 10 publications

A Combined Bottom-hole Pressure Calculation Procedure Using Multiphase Correlations and Artificial Neural Network Models

A Combined Bottom-hole Pressure Calculation Procedure Using Multiphase Correlations and Artificial Neural Network Models

A new soft computing‐based approach to predict oil production rate for vapour extraction (VAPEX) process in heavy oil reservoirs

Machine learning models to predict bottom hole pressure in multi‐phase flow in vertical oil production wells

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