Modelling of Electric Submersible Pump Work on Gas-Liquid Mixture by Machine Learning

2021 ◽  
Author(s):  
Kirill Alexandrovich Goridko ◽  
Arturas Rimo Shabonas ◽  
Rinat Alfredovich Khabibullin ◽  
Vladimir Sergeevich Verbitsky ◽  
Andrey Valeryevich Gladkov
Keyword(s):  
Machine Learning ◽  
Power Consumption ◽  
Flow Rate ◽  
Liquid Mixture ◽  
Gas Content ◽  
Petroleum Reservoir ◽  
Submersible Pump ◽  
Bench Tests ◽  
Wide Range ◽  
Electric Submersible Pump

Abstract Oil wells in Western Siberia usually placed on artificial drilling pads, forming well clusters up to 30 wells. The flow rate of each well in the cluster measured by an automatic measuring unit one by one. Often flow rate measurement requires several hours and flow rate of a single well can be measured once a week or less. This led to situation then events affecting well rate can be invisible between measurements. Identifying such events can be extremely useful in many cases, for example for wells with unstable behavior or transient regimes. The same challenges are also faced at distant green fields during their development, there the flow rates can be measured once a month with a mobile unit. The objective of this paper is to develop a virtual flowmeter model based on indirect high-frequency data of well operation and ESP. In Gubkin University, at the Petroleum Reservoir and Production Engineering Department, bench tests of ESP5-50 (118 radial stages) on gas-liquid mixture in a wide range of volumetric gas content (βin = 0-60%), intake pressure (Pin = 0.6-2.1 MPa) and pump shaft speed (n= 2400-3600 rpm) were performed. Three vibration sensors were installed on the unit: on the ESP, at the ESP discharge, on the pipeline, which simulates the wellhead production tree. During the bench tests were recorded series of pressures at the intake, discharge and along the pump length, series of current and power consumption, as well as vibrations with frequency several times per second. Based on the bench test results, we investigated the possibility of indirect determination of well operation parameters during artificial lift modelling by machine learning. As a result, the approaches to modelling taking into account various sets of parameters (features) have been studied: based on hydraulic parameters – ESP intake and outlet pressure;based on hydraulic and electric parameters – current and power consumption;based on hydraulic, electric and vibrating parameters. The analysis of data series allowed to define the boundaries of stable ESP operation, namely the transition to surging and pump starvation. The novelty of the work is: –machine learning modeling of the gas-liquid mixture pumping process by electric submersible pump;–solving both direct and inverse issues: as virtual liquid flowmeter as, virtual gas content flowmeter at the pump intake.

Numerical modeling of the degassing process of a gas-liquid mixture in hydrocyclone

2019 ◽  
Vol 5 (3) ◽  
pp. 213-229 ◽  
Author(s):  
Artem I. Varavva ◽  
Vladimir E. Vershinin ◽  
Dmitry V. Trapeznikov
Keyword(s):  
Numerical Modeling ◽  
Flow Measurement ◽  
Separation Efficiency ◽  
Liquid Mixture ◽  
Separation Process ◽  
Gas Content ◽  
Phase Flow ◽  
Flow Rates ◽  
Wide Range ◽  
Measurement Units

Centrifugal separators&nbsp;— hydrocyclones&nbsp;— are widely used in many areas of the national economy to separate mixtures of substances of different densities. Hydrocyclones can be used for phase separation in oil, water and gas flow measurement units. The flow from the well is initially a three-phase mixture. The hydrocyclone separates the gas and liquid phases at the inlet of the measuring unit, which are then transferred to separate gas and liquid measurement units. Maintaining the accuracy of the phase flow measurement when using hydrocyclones in the measuring units requires high quality separation over a wide range of flow rates and phase contents. One of the directions of forecasting the characteristics of the separation process is based on the numerical solution of the equations of hydrodynamics of multiphase flows. Modern software of computational hydrodynamics allows to solve problems of such class in three-dimensional statement and thus to estimate efficiency of work of the device and its metrological characteristics.<br> This paper studies the processes of separation of gas-liquid mixture in hydrocyclone at different volume gas content and phase flow rates. The authors present a mathematical model with indication of the main assumptions and formulate the boundary conditions of the problem. Calculations were carried out on the open platform OpenFOAM with the use of interFoam solver. The results of numerical modeling have determined the basic structures of currents in the hydrocyclone. The influence of the initial gas content on the separation efficiency at different flow rates is investigated. The main reasons for the decrease in separation efficiency at low gas content values are revealed. In addition, the influence of the guiding elements on the separation process is considered.

scholarly journals The Application of Permanent Magnet Motor on Electric Submersible Pump in X Well

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Andreas Setiabudi ◽  
Muhammad Taufiq Fathaddin ◽  
Suryo Prakoso
Keyword(s):  
Permanent Magnet ◽  
Production Rate ◽  
Flow Rate ◽  
Asynchronous Motor ◽  
Life Time ◽  
Oil Well ◽  
Submersible Pump ◽  
Production Flow ◽  
Permanent Magnet Motor ◽  
Electric Submersible Pump

<em>In thisresearch the application of permanent magnet motor and asynchronous motor in X Well was evaluated. The permanent magnet motor and asynchronous motor used in this research are PM51 – NFO 150 FLT @50hz and</em> <em>AM51 – NFO 150 FLT @50hz, respectively. Several parameters are compared such energy losses, energy consumption, motor heating, and production rate. Based on the data analysis, there are some advantages by using permanent magnet motor which can help to improve efficiency and consume less energy, therefore can give more profit within the same period of production. These advantages consist of durability for motor, consume less electricity energy to maintain</em> <em>the operation of ESP string, give bigger production rate, and longer expected life time than an asynchronous motor. The implementation of permanent magnet motor is recommended in oil well that has high fluctuation in production flow rate, since the setting flow rate of the motor is adjustable.</em> <em>This advantage can be useful to give longer lifetime and hence to reduce the pump replacement program</em>

Installation of Electric Submersible Pump as Artificial Lift Method in Low Flow Rate Wells, a Case History

2017 ◽  
Author(s):  
J. J. Del Pino ◽  
J. L. Martin ◽  
H. Vargas ◽  
J. S. Maldonado ◽  
E. Rubiano ◽  
...  
Keyword(s):  
Flow Rate ◽  
Case History ◽  
Low Flow ◽  
Submersible Pump ◽  
Artificial Lift ◽  
Electric Submersible Pump ◽  
Low Flow Rate

Making smart wells equipped with electric submersible pump units

2018 ◽  
pp. 130-133
Author(s):  
S.S. Ulianov ◽  
◽  
R.I. Sagyndykov ◽  
D.S. Davydov ◽  
S.A. Nosov ◽  
...  
Keyword(s):  
Submersible Pump ◽  
Smart Wells ◽  
Electric Submersible Pump

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Interlayer Distance ◽  
Computational Screening ◽  
Wide Range

Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Intelligent Techniques Analysis for Glycosylation Site Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Alhassan Alkuhlani ◽  
Walaa Gad ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Cell Interaction ◽  
Glycosylation Site ◽  
Machine Learning Classification ◽  
Site Prediction ◽  
Glycosylation Sites ◽  
Wide Range ◽  
Feature Extraction And Selection ◽  
Computational Intelligent

Background: Glycosylation is one of the most common post-translation modifications (PTMs) in organism cells. It plays important roles in several biological processes including cell-cell interaction, protein folding, antigen’s recognition, and immune response. In addition, glycosylation is associated with many human diseases such as cancer, diabetes and coronaviruses. The experimental techniques for identifying glycosylation sites are time-consuming, extensive laboratory work, and expensive. Therefore, computational intelligence techniques are becoming very important for glycosylation site prediction. Objective: This paper is a theoretical discussion of the technical aspects of the biotechnological (e.g., using artificial intelligence and machine learning) to digital bioinformatics research and intelligent biocomputing. The computational intelligent techniques have shown efficient results for predicting N-linked, O-linked and C-linked glycosylation sites. In the last two decades, many studies have been conducted for glycosylation site prediction using these techniques. In this paper, we analyze and compare a wide range of intelligent techniques of these studies from multiple aspects. The current challenges and difficulties facing the software developers and knowledge engineers for predicting glycosylation sites are also included. Method: The comparison between these different studies is introduced including many criteria such as databases, feature extraction and selection, machine learning classification methods, evaluation measures and the performance results. Results and conclusions: Many challenges and problems are presented. Consequently, more efforts are needed to get more accurate prediction models for the three basic types of glycosylation sites.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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