Electrical Submersible Pump Research Articles

Comprehensive analysis and identification of energy performance and unsteady two-phase flow patterns based on experiments and comparison between two distinct multiphase flow models

ABSTRACT In the petroleum sector, electrical submersible pumps (ESP) face numerous challenges when handling multiphase flow of gas and liquid. The primary challenge is from the build-up of the gas-bubbles within the impellers of ESPs, leading from moderate to severe deterioration in pump efficiency. Consequently, a comprehensive investigation is conducted, involving a combination of experimental and numerical analyses, to thoroughly explore the effects of gas entrainment on the performance and intricate internal flow mechanisms of a five-stage mixed-flow ESP. All these analysis was carried out under both design (Qd ) and off-design conditions (0.8Qd , and 1.2Qd ). For unsteady numerical calculations, two widely used multiphase-models: The volume of fluid (VOF) and The Euler-Euler model are employed and compared to investigate the versatility/validity of these models in predicting multiphase performance of ESP. The comparison between tests, Eulerian, and VOF models demonstrated that the Eulerian model aligns well with the experimental results. and shows higher-adaptability pattern with the test results. This reveals its best capability/versatility in predicting two-phase performance and internal-flow results for ESP. Moreover, it can more accurately capture the phase slippage inside ESP under two-phase flow. Additionally, this work provides a vision to the broad-applicability of Eulerian-model for complicated machinery such as ESP.

ESP vibration prediction based on pump operating conditions in laboratory using machine learning

Electric Submersible Pump (ESP) is an effective artificial lift method of pumping medium to high production fluids. However, it is prone to failure because most of the system is located downhole. When mechanical defects start to develop, vibration signal changes. By examining the variation in vibration, component failures can be located and predicted. The objective of this study is to creates a machine learning model to predict ESP vibration using experimental data. The dataset that was utilized to create the model has more than a million data points collected from laboratory. Based on pump performance, the experimental data are divided into three groups: normal, advised and failing pump conditions. The model features are operating time, pump speed, oil flow rate, water flow rate, pump intake pressure, motor temperature, free gas percentage and liquid/mixture density and viscosity. Among the algorithms used to develop the model, random forests regression produces results with acceptable accuracy while not requiring too much computing power. Because of that, random forests regression was used for further optimization and simulation. The results of the model show that pump speed and operating time have the biggest effects of all the features. Due to the limited range of experimental data, in some cases, it is either unknown or unclear how the features affect ESP vibration, particularly in advised and failing pump conditions. The model's vibration predictions closely match the results of the experiments. This model can be used to forecast pump vibration under a particular operating condition by incorporating into the ESPSim program. The predicted vibration magnitude can be compared to the actual value to determine whether an ESP is in failing condition. With this information, the amount of downtime when the pump fails could be minimized. The extreme downhole conditions can also cause sensor cables to break and become incapable of transmitting data to the surface. Operators can use predicted ESP vibration to get an idea of the status of the pump.

Design and performance study of gas–liquid separation–mixing device for electric submersible pump in high-gas-content oil wells

In the process of oil production, wells containing gas can impact the efficiency of electric submersible pump (ESP), potentially causing gas lock. This issue can lead to the loss of lifting capacity in ESP, affecting the normal production of oil wells. To address this problem, the concept of gas separation before mixing transportation has been proposed, and a gas–liquid separation–mixing device has been designed. Experimental tests on the gas–liquid two-phase flow under various working conditions were conducted. A numerical model of the physical process was developed and validated with the experimental results. The results indicate that when the inlet flow rate exceeds 8.75 m3/h, the gas phase can be effectively accumulated in the center of the main pipeline after flowing through the guide vanes, thereby achieving efficient gas–liquid separation. Centrifugal number, which is defined as the ratio of axial flux of centrifugal force to axial flux of gravity, was proposed for evaluating the flow characteristics. When the centrifugal number exceeds 6.5, a high-quality gas core is formed in the pipe. At high inlet gas content, the volume fraction of gas in the main pipe initially decreases to 2% as the flow rate increases to 15 m3/h. However, at a flow rate of 30 m3/h, the volume fraction gradually rises to 30%, which results in a significant amount of gas being forced into the main pipe. The results are beneficial for expanding the use of ESP and improving the lifting efficiency in the development of oil field with high gas content.

Development and simulation of the gas-downhole water sink-assisted gravity drainage (GDWS-AGD) process to reduce carbon footprint and improve clean oil production by injecting CO2 and petroleum-associated gas

Implementing continuous gas injection into oil reservoirs, surrounded by strong water drives, enhances oil production. Nevertheless, water coning/cresting in these reservoirs leads to high water cuts, substantial oil production reductions, and major water-handling issues for surface facilities. The Gas-Downhole Water-Sink-Assisted Gravity Drainage (GDWS-AGD) process can enhance oil recovery (EOR) in such reservoirs and mitigate the influx of aquifer water into the oil zone. This study compares the performance of GDWS-AGD and GAGD processes based on reservoir-simulation results for two heterogeneous reservoirs: the PUNQ-S3 reservoir and the giant South Rumaila (SR) oil field (Iraq).The PUNQ-S3 simulation exclusively employs carbon dioxide (CO2) as the injected gas. In contrast, the SR simulation evaluates the CO2 injection in comparison to petroleum-associated gas (PAG). The GDWS-AGD process performance was assessed based on oil recovery factor, water cut, gas-to-oil ratio (GOR) in oil producers, cumulative volumes of gas injected, and oil/gas saturations. The PUNQ-S3 simulation employs six wells: two vertical CO2 injectors with perforations at the top of the reservoir to form a gas-cap zone. Additionally, there are two horizontal oil-production wells and two horizontal water-production wells positioned below the oil-water contact (OWC). The GDWS-AGD process was assessed in the SR simulation, using CO2 and PAG, in several configurations to compare it with the basic GAGD. In the SR design, twenty-two vertical gas-injection wells, ten horizontal oil-production wells, and six horizontal water-production wells were placed in the gas-, oil-, and water-bearing zones, respectively. A vertical well was dual-completed with two lateral sections originating from separate kick-off points above and underneath the OWC with 2–3/8-inch tubing for oil and water production, respectively. Hydraulic packers in the vertical wellbore separate oil and water production tubing in the dual-completion configuration, and electric-submersible pumps are then employed for water production.The downhole-water-sink configuration effectively reduces oil-reservoir pressure and minimizes water cut/cresting tendencies. CO2 remains immiscible due to reduced gas-injection pressure and decreased solubility. The CO2-based simulation of the SR field yielded oil-recovery factors of 71.67%, 75.25%, and 76.162% for the GAGD, basic, and advanced GDWS-AGD setups, respectively. The oil-recovery factor of GDWS-AGD was further increased to 82.64% by implementing PAG injection, resulting in a decrease in water cut to 5% in all oil producers. Although PAG gas injection outperforms CO2 in oil recovery, water cut, and gas injectivity in the SR field simulation, the GDWS-AGD approach with CO2 remains a feasible option. This technology has the capability to effectively capture and store significant amounts of CO2 in the flooded reservoir for many years, thereby mitigating carbon emissions associated with fossil fuel usage.

Performance improvement of a rotating gas separator for electrical submersible pump considering entropy generation method

The utilization of Electrical Submersible Pumps (ESPs) stands as one of the foremost methods in artificially extracting oil, primarily within the oil industry. Under typical conditions, the pump encounters a two-phase flow of oil and gas. Should the gas phase within this mixture surpass an acceptable threshold, it can lead to a significant decline in performance and inflict detrimental damage upon the pump. To address this issue, the incorporation of a gas separator unit, one of the pivotal components within ESPs, is imperative at the pump's inlet section. Enhancing the gas separator unit holds paramount importance for ensuring pump safety and optimizing performance, particularly in wells with elevated gas contents.The conventional approach to calculating hydraulic losses in turbomachinery, relying on pressure drop calculations, often falls short in pinpointing the precise locations of losses. This paper delves into enhancing gas separator performance by augmenting gas separation efficiency through the application of the entropy generation method and leveraging the second law of thermodynamics. To achieve this, pivotal entropy-generating geometrical parameters of the gas separator were identified as the design variables necessary for forming the Taguchi sample space. Numerical simulations were conducted utilizing steady three-dimensional Reynolds-Averaged Navier-Stokes (RANS) equations for incompressible fluid flow, coupled with a turbulence model. Results indicate that the gas separation efficiency (GSE) of the initial model surged from 45.6% to 88% in the improved sample, albeit with a concurrent rise in entropy generation rate (EGR) from 209 W/K to 243.1 W/K. To establish a more comprehensive criterion for analysis, the ratio of GSE to EGR was employed as an objective function. This ratio quantifies the efficiency of gas separation per unit of energy loss, and it escalated from 0.218 in the primary sample to 0.362 in the improved sample, representing a 66% increase. This signifies that despite simultaneous increases in EGR and GSE, the rate of energy loss per unit of gas separation has diminished. Furthermore, the heightened EGR in the improved sample can be attributed to an increase in total volume, leading to an expanded contact surface area and consequently an elevation in total entropy. This investigation underscores the advantages of the entropy generation method, particularly in delineating the precise location and magnitude of energy dissipation within the rotating gas separator.

Research on application of electric submersible pumps to increase oil recovery field Y, lot 05-1a, the Nam Con Son basin

The application method of Electric Submersible Pump increases the oil recovery factor Y at 05-1A block, the Nam Con Son basin, which is affected by many factors such as energy regime, fluid regime, fractures, etc. All these factors are based on the simulation model from Prosper simulation software. The process starts from the appropriate PVT data, exploitation data, selecting necessary parameters, designing wells, and predicting exploitation to find the best case for the Electric Submersible Pump exploitation plan. The results showed that if oil is only exploited by natural energy, it will not be effective, so it is possible to equip a Electric submersible pump system with 2 exploitation wells for the most efficient exploitation. The Electric Submersible Pump is applied and effective in the case of wells with high WTC and low GOR.

A novel fault diagnosis method under limited samples based on an extreme learning machine and meta-learning

BackgroundTraditional fault diagnosis methods typically rely on an adequate number of fault samples. Yet, in industrial processes, the availability of faulty samples is often limited, accompanied by high sample collection cost. In this case, traditional methods prove ineffective for accurate diagnosis. MethodsTo solve this issue with limited samples, a novel fault diagnosis method, incorporating Extreme Learning Machine (ELM) and Meta-learning, is proposed. This method comprises two stages: Meta-learning optimization and top-model classification. In the first stage, the Model-Agnostic Meta-Learning framework is adopted to extract gain valuable model parameters from the available faulty data, yielding in the optimization of the initial weight and bias of the network model to obtain the reconstructed ELM. This enhancement significantly bolsters the ELM's parameters optimization capability, especially in scenarios with limited fault samples. Consequently, in the second stage, the reconstructed ELM model is deployed for effective fault diagnosis. Significant FindingsThe proposed method has proven successful in the real-time diagnosis of electric submersible pump, specifically addressing the challenging issue of ineffective fault diagnosis due to the limited fault samples in condition monitoring. The results showcase a 30 % classification improvement compared to ELM and an 8 % enhancement over PSO-ELM, FOS-ELM, LE-ELM, and Siamese Nets.

A critical analysis and improvements of empirical models for predicting the performance of Electrical Submersible Pumps under viscous flow

One of the main challenges associated with Electrical Submersible Pumps (ESPs) system is the correct sizing of the pump. High viscosity fluids, emulsions, or compressible fluids significantly impact the pump’s ability to transfer energy to the fluid. Obtaining ESP characteristic curves in conditions resembling real applications is complex but essential for the correct design, operation, optimization, and safeguarding of ESP systems. The objective of this study is to critical assess the traditional methods for predicting the ESP performance under viscous flow. This study also improves these methods driven by ESP performance data. The results show that the original models have limitations because their databases are primarily based on data from conventional centrifugal pumps. Consequently, using these methods can result in significant errors in the sizing of the production system. The accuracy of the models improved significantly after modifying the methods. Reductions in errors were observed in all correction factors, and particularly in the prediction of global performance. The improvements were effective in mitigating the effects of increased deviations with the reduction in Reynolds number observed when applying the original models to predict flow rate and efficiency.

Experimental and Numerical Study of Hydraulic Characteristics and Pressurization Deterioration Mechanism of a Three-Stage Mixed-Flow Electrical Submersible Pump Under Gas-Liquid Condition

Abstract Electrical submersible pump (ESP) is extensively utilized in industrial sectors such as petroleum, chemical, and nuclear energy. However, ESPs experience pressurization deterioration due to the high gas volume fraction (GVF), resulting in the pressurization failure. In this paper, a three-stage mixed-flow ESP with closed impeller structure is detailed analysis. The interstage hydraulic characteristics and pressurization deterioration mechanism of the mixed-flow ESP are investigated at various rotational speeds and inlet conditions by combining experimental and simulation. The population balance model (PBM) and renormalization group (RNG) k − ε model are employed. As the liquid flowrate increases, the ESP experiences a “three-stage” downward trend in pressurization. It is discovered that the first booster stage has a lower inflow velocity and flow separation degree compared to the subsequent booster stages, resulting in a greater liquid-phase pressurization capacity. The gas–liquid pressurization exhibits a wave-shaped downward trend due to significant deterioration in stage-wise pressurization when the liquid flowrate is low. Once the inlet gas volume fraction (IGVF) reaches the first critical GVF, the gas aggregates on the impeller's suction surface are removed at the impeller outlet, creating an annular air mass, which creates a chaotic vortex absorbing the fluids' kinetic energy.

Comparative analysis of approaches necessary for designing a robotic module for industrial parts defect detection

Over the past two decades, the most frequently implemented method of oil extraction in the Russian Federation has been a mechanized method using an electric submersible pump (ESP) unit. Due to the remoteness of the fields from the base of repair enterprises, the cost of transporting spent but maintainable ESPs significantly exceeds their purchase price, which over time leads to the formation of a significant amount of decommissioned oil production equipment. To solve this problem, it is proposed to develop a mobile robotic module for sorting, defecting and storing pump parts, thanks to its use, minor repairs of equipment will be possible directly at production sites. The article deals with the problems of complex flaw detection of metal and non-metallic parts with the possibility of applying the developed methodology for a wide range of industrial products, at the same time, the main attention is paid to work with ESP parts. Based on the decomposition of the problem, the most problematic operations were identified: classification of parts, surface control (identification of defects), dimensional control. The results of a brief comparative analysis for each of the above subtasks are based on a review of the scientific literature over the past 30 years, with the largest number of sources reviewed in the last 5 years. As a result optimal methods for solving the task were derived — a machine learning technique for classifying surface defects, the use of a coordinate measuring machine with a manipulator for dimensional control. A new approach is also proposed to solve the main problem of machine learning methods (lack of training samples) in the form of using synthetic photorealistic images for classification with transfer of defect features from semantically close and publicly available training samples.

Production Optimization of an Oil Well by Restraining Water Breakthrough

This study investigates the well named X (for confidential reasons) of the field called Y which initially was productive with the natural energy of the reservoir of the oil in the absence of water. After a few years of production, water began to overflow excessively in the well. The goal of this paper is to maximize the oil production in an oil well X by reducing water ingress. The Pressure Volume Temperature (PVT) data, completion data, and reservoir data are analyzed via PIPESIM and Excel software by using the nodal analysis method to get the well performance and decline curve for predictions. Two scenarios are considered: firstly, to install an electric submersible pump (ESP) to activate the X well and secondly to make a new perforation. The ESP is installed at 11300 ft where the water production flow rate is 5586.264 STB/d and the oil production flow rate is 1396.566 STB/d. The new perforation is installed at 12038 ft where the water production flow rate is 277.1693 STB/d and the oil production flow rate is 5543.387 STB/d. To have the optimal parameters, the sensitivity analysis is applied to the flowline diameter and the wellhead pressure. The optimal parameter values obtained are 308.6128 STB/d for the water production flow rate and 5863.643 STB/d for the oil production flow rate. The new perforation is appropriate because this scenario allows water reduction, oil production maximization, profitability of 98086854 $, and a return on investment in 5 months during 16 years of production.

Experimental data-driven model development for ESP failure diagnosis based on the principal component analysis

The reliable diagnosis of electrical submersible pump (ESP) failure is a vital process for establishing of the optimal production strategies and achieving minimum development costs. Although traditional ammeter charts and nodal analysis are commonly used for ESP failure diagnosis, the techniques have limitations, as it requires manpower and is difficult to diagnose the failure in real-time. Therefore, in this study, ESP failure diagnosis was performed using the principal component analysis (PCA). First, 11 types of 9,955 pieces of data were acquired from a newly constructed ESP experimental system for 300 days. During the experimental period, ESP failure occurred twice with a significant drop in performance: first on day 112 and second on day 271. The PCA model was constructed with the 8,928 pieces of normal status data and tested with the 1,027 pieces of normal and failure status data. Three principal components were extracted from the measured data to identify the patterns of the normal and failure status. Based on the logistic regression method to analyze the efficiency of the PCA model, it was found out that the developed PCA model showed an accuracy of 93.3%. Therefore, the PCA model was found to be reliable and effective for the ESP failure diagnosis and performance analysis.

Maximizing Oil Recovery in a Marginal Oilfield: Niger Delta Case Study

Marginal field development and production are often abandoned by operators because of finding reliable and available equipment and services to enable the field to be developed. These challenges make it difficult to economically produce such fields. This research demonstrates the use of industry-based simulators (PIPESIM, ECLIPSE and PETREL) to design well completion model, Electrical Submersible Pump (ESP). model, simulate, and evaluate the performance of ESP on a typical marginal oilfield. The main objective of this study is to effectively optimize oil production from marginal fields in the Niger Delta using Electrical Submersible Pump (ESP). ECLIPSE software was used for the reservoir description. PIPESIM was used to design the artificial lift system (ESP) for five oil wells and PETREL was used to integrate the whole system for effective production optimization. The performance of ESP wells was simulated and compared with the naturally flowing wells. The results obtained from the production forecast showed that the ESP wells gave superior oil production when compared to natural flowing wells. From the simulation results, it was observed that the cumulative oil recovery without ESP was 33,684,736 stb while that recovered with ESP was 87,751,136 stb (about 261% oil increment). The findings of this study will enable petroleum engineers to design ESP systems and well completion that would effectively optimize oil production from marginal fields in the Niger Delta. Furthermore, the findings of the study will offer new and exciting ways to process and transform abandon oilfields into productive marginal oilfields.

Research on the Process of Laser Cladding Ni60 Coating on High-Nickel Cast Iron Surfaces

In order to achieve high-performance coatings on the surface of electric submersible pump impellers, it is crucial to optimize the laser cladding process parameters. Using Ansys 2021 R1 commercial software, a numerical simulation of laser cladding Ni60 powder on high nickel cast iron was conducted. The simulation utilized a 3D Gaussian heat source, parametric language, and life–death unit technology to replicate the characteristics of synchronous powder delivery laser cladding. The study focused on analyzing the temperature field cloud map and molten pool size under different laser power and scanning speeds, narrowing down the process parameter window, selecting optimized laser power and scanning speed, and assessing the changes in surface morphology, melting height and width, dilution rate, microhardness, and microstructure of the laser cladding coating. Results indicate that the coating width and thickness increase with higher laser power and lower scanning speeds. The microstructure consists primarily of dendritic, block, short rod, and long strip formations, and exhibits a tightly distributed and uniform grain structure. Furthermore, the microhardness of the coating shows a negative correlation with laser power and scanning speed. The optimal process parameters were determined to be a laser power of 1100 W and a scanning speed of 6 mm/s. A comparison with the simulation confirmed the effectiveness of the simulation in effectively guiding actual production.

Identification of the energy-efficient operation mode of the electric submersible pump installation in the periodic mode

ABSTRACT Reducing the power consumption of oil production wells is one of the important directions of oil industry development in the context of transition of many fields to the late stages of operation. At the same time, there is often a need to limit hydrocarbon production, which is often accomplished by shutting down wells completely. In this paper, the authors propose an approach to improve the energy efficiency of the electric centrifugal pump unit through the use of periodic operation mode, which allows to reduce the specific power consumption of the electric centrifugal pump unit. Also, this mode can be considered as a way to regulate the well flow rate. According to the results of calculations it was obtained that the reduction of specific power consumption of the unit when operating at nominal frequency can reach 5% relative to the continuous mode of operation. An assessment was made of the effect of reducing the frequency of the supply voltage in a periodic mode on the parameters of the operation of the installation and it was found that the well flow rate increased by 1.43% with a reduction in specific power consumption by 1.64%.

A Digital Twin Development Framework for an Electrical Submersible Pump (ESP)

Premature failure of a sub-system can be critical for an industrial Cyber-Physical System (CPS). A digital twin (DT) assisted predictive maintenance procedure can reduce the risk of costly unplanned maintenance. This study presents a generalized DT development framework for an Electrical Submersible Pump (ESP) that can assist in predictive maintenance. The framework is applied on a single-phase ESP as a proof of concept. The maximum winding temperature of the selected ESP is simulated using a multiphysics simulation tool with transient electromagnetic and transient heat transfer solvers. The simulation parameters were refined using data captured through an ESP free-run experiment. Simulating the total energy loss in the ESP stator and rotor and the transfer of heat from the outer fluid domain facilitates a relationship between the measurable external temperature and the maximum temperature in the stator winding. Following a design of experiments (DOE) approach, a series of simulations were run to establish a statistical model for the winding temperature in terms of the fluid temperature, the time duration a particular temperature was persistent, and the initial maximum stator winding temperature. As the instantaneous maximum stator winding temperature is related to the remaining useful lifetime, it was shown using a case study that the proposed framework can prognosticate the ESP failure, assisting effective decision-making for predictive maintenance of a CPS.

Technology Focus: Artificial Lift (March 2024)

The industry is now in its second year beyond the COVID-19 pandemic, and the workforce has changed in many ways, but issues facing production performance remain. Major conferences saw 116 submissions from 28 countries, with 64% regarding electrical submersible pumps (ESPs), and 18% and 8% devoted to gas lift and rod lift, respectively. Many of the papers dealt with case history successes. That is all well and good, but it doesn’t necessarily drive reader interest if it can’t be applied to their asset. The top three papers were selected on the guidelines of clarity of the abstract in addressing the scope, methods/procedures/process, results/observations/conclusions, and novel or additive information. The papers described here focus on novel unconventional horizontal downhole pump card signatures, carbon reduction through alternative energy to pump wells, and predicting downhole-discharge pressure of ESPs. Paper SPE 214723 presents a discussion of the design, evaluation, and field testing of a new type of mechanical gas separator with what the authors term a “hydrohelical” design. The separator, in Permian Basin tests, almost doubled the flow capacity of conventional gas separators with a high level of efficiency. The authors draw particular attention to the improvement of ESP operations under multiphase conditions. Carbon reduction is a hot topic, and paper OTC 32671 does not disappoint as it looks at the use of a 10-MW floating solar photovoltaic power system to meet the energy needed for 10 ESP wells on an oil platform in the Brazil equatorial region. Collaboration with the National Renewable Energy Laboratory and its System Advisor Model was used with extensive meteorological and wind data to examine the sustainability of green energy use for the petroleum industry and offer valuable guidance for future renewable energy projects in regions with similar characteristics. Finally, paper SPE 216598 comes from Sudan and Saudia Arabia and focuses on digital solutions for the challenge of knowing the downhole-discharge pressure of an ESP system. Most ESP systems do not have downhole gauges, so an artificial neural network (ANN) approach is used to predict the ESP discharge pressure for performance optimization. Forty wells and more than 12,000 data points were reduced to 16 inputs plus one hidden layer of seven neurons to provide a high degree of accuracy in prediction of discharge pressure. The authors state that the ability to accurately predict discharge pressure can lead to the early detection of possible anomalies, which can prevent costly failures and reduce downtime. Artificial intelligence technology may improve the accuracy of ANNs, but, for now, ANNs are worth a look to obtain much-needed ESP discharge pressure. Other notable papers dealt with gas separation, ESP reliability, and the increasing challenges of low rates, low pressures, and high gas fractions. Recommended additional reading at OnePetro: www.onepetro.org. SPE 214913 Solved: ESP Shutdowns and Restart Issues—Novel Pump Protection and Active 3D Dispersion System Improve ESP Performance in Multiple Wells by A. Bhattacharjee, SLB, et al. SPE 215474 A New Artificial Lift Approach in Telisa Low-Productivity Reservoir: Utilized Very Low-Rate ESP by Agus Aryanto, Medco E&P Indonesia, et al. SPE 215170 A Comprehensive Study of a Novel Submersible Pump by Hattan Banjar, Saudi Aramco, et al. SPE 214722 Case Study: Predicting Electrical Submersible Pump Failures Using Artificial Intelligence and Physics-Based Hybrid Models by Shejuti Silvia, Baker Hughes, et al. SPE 214733 Gas-Flow-Management Technology Designed To Decrease Downtime and Improve ESP Efficiency by S. Fulwider, ConocoPhillips, et al.

Hydrohelical ESP Gas Separator Increases Flow Range, Improves Reliability

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 214723, “New High-Performance ESP Gas Separator,” by Barry Nicholson, SPE, and Scott R. Harryman, SPE, Oxy, and Donn J. Brown, SPE, Halliburton, et al. The paper has not been peer reviewed. _ The complete paper presents research, design, and field testing of a new type of mechanical gas separator for electrical submersible pump (ESP) systems that increases operating flow range and separation efficiency while decreasing erosion problems and improving reliability. The hydrohelical separator system has been proved effective in different types of wells. Background Several methods and techniques exist for avoiding gas in ESPs, including inverted shrouds, passive separators, mechanical gas separators, and tandem gas separators. Most share the limitations of limited flow capabilities and reduced performance at higher total flow rates. The many variables associated with complex two-phase flow behavior, internal and external pressure variations, separation methods, velocity and viscosity of fluids, effect of the pump bolted above, inherent erosion issues, and single vs. tandem designs add to the challenge of basic mechanical gas-separator operation. In 2016, a program was initiated that featured investment in both experienced personnel and advanced testing technology to unravel the aforementioned challenges and improve understanding of the operation of downhole mechanical gas separation. The team investigated innovative methods of testing and created a transparent testing system that allowed visual observation of individual internal flow regimes as well as component performance. Using a combination of high-speed photography, computational fluid dynamics validation, and specialized instrumentation, every component of a mechanical separator system was enhanced for higher flow capabilities, improved separation efficiency, and higher reliability for a variety of downhole conditions. The result was the development of a new type of mechanical gas separator for ESP use. Development Most gas separators ingest multiphase fluid into a separation chamber, separate the gas and liquid phases, eject the separated gas phase into the well annulus, and provide sufficient liquid phase fluid to the pump to enable efficient pumping. Existing designs vary, but they share separation methods that result in decreased efficiency at higher flow rates and low maximum flow rates compared with the pumps they feed. The hydrohelical gas separator is the first downhole, dynamic gas-separator design improvement in more than 30 years designed to address these deficiencies (Fig. 1). The design was aimed at the following main objectives: - Increase total fluid flow rate through the separator - Maximize efficiency of every internal and external component of the system - Reduce erosion characteristics common in traditional gas-separator designs - Improve reliability The design of the hydrohelical gas separator is based on a stationary helical vortex inducer and pump stages that enable movement of large quantities of fluid while remaining immune to gas locking. The stationary helical vortex inducer design enables increased efficiency and flow rate through the separator by reducing turbulence in remixing regions within the helical inducer and separation chamber.

Artificial Lift: 25 Years of Change Tracked in the Pages of JPT

Nodding donkeys. Thirsty birds. Rocking horses. All are playful nicknames for the oil and gas icon known as a pumpjack. To the uninformed, the pumpjack is a thing-a-ma-jig that has something to do with oil, probably “fracking” because that’s what drilling rigs do, right? But as an industry-educated and well-informed reader of JPT, you know this is inaccurate. By whatever name you call it, you know that the pumpjack is the visible manifestation of an invisible physics equation, a mechanism buried deep underground that lifts reservoir fluids to the surface. You also know it is one type of artificial lift available in a stable of systems with equally curious and technical names like progressive cavity, plunger, jet, gas lift, and electrical submersible pump (ESP). While there is no global repository of artificial lift statistics, industry observers estimate that 90 to 95% of the world’s producing wells use artificial lift, as noted in a 2013 JPT story on the challenges and opportunities for artificial lift. Artificial lift, as applied in oil and gas production, has existed for over 100 years. A 2014 JPT story laid out a brief history of the practice, sharing how the walking beam principle dates back to at least 476 CE when used in Egypt, and evidence of the use of sucker rods to lift fluids has been excavated from the homes of wealthier families in the early days of the Roman Empire. Even as we leap forward to the modern-day application of artificial lift systems, many techniques and technologies resemble their original designs. How these systems are applied has changed, especially in the past decade-plus with the emergence of shale. As highlighted in a JPT story on the past 25 years of technology advances, the industry underwent a robust era of technology development in its rush to crack the shale development riddle. Since 1999, the industry also delivered a 36% increase in global supplies, as noted in this JPT story celebrating this industry’s biggest innovations. 1999 represents an interesting turning point in the industry’s technological journey. Also, it is the last time JPT editors took the opportunity to celebrate the publication as it turned 50 years old that year. What follows are a few highlights from JPT’s coverage of advances and changes made in artificial lift applications and practices over the past quarter century.

Artificial Neural Networks Predict Discharge Pressures of ESPs

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216598, “Application of Artificial Neural Networks in Predicting Discharge Pressures of Electrical Submersible Pumps for Performance Optimization and Failure Prevention,” by M.A. Mahmoud, M. AbuObida, and O. Mohammed, University of Khartoum, et al. The paper has not been peer reviewed. _ This paper presents a novel approach using artificial neural networks (ANNs) to predict the discharge pressure of electrical submersible pumps (ESPs). A data set of more than 12,000 data points collected from 40 different wells was used to train and test various ANN models with different input parameters. The ability to predict discharge pressure accurately can lead to the early detection of possible anomalies. Methodology Data Collection. The data-collection process for ESP sensor data involved collecting data from 40 wells operational during 2019–2023 with starting dates from 2019 through 2022. The data included key parameters provided in the complete paper. Data Cleaning and Integration. The data-cleaning process was performed to ensure that the accuracy and reliability of the data were maintained. Scatter plots were used to visualize the distribution of each variable in the data set, and outliers were identified and removed or smoothed. Missing values were addressed by fill-in using statistical methods or by removing them from the data set if the percentage of missing values was significant. Inconsistencies in the data were dealt with by removing duplicates, fixing errors, and ensuring that the data was consistent across all sources. The data-integration process involved combining data from different sources to create a single data set for analysis. Data were labeled and formatted to facilitate analysis, including assigning meaningful names to the variables and ensuring that data were in the correct format. Data Analysis and Physical Relationship. The MATLAB program was used to analyze the data statically. A code was made to generate charts and statistical calculations to gain insights into characteristics such as minimum, maximum, mean, variation, standard deviation, skewness, and covariance. Understanding the physical relationship between the various operating parameters and the pump-discharge pressure is crucial. A comprehensive analysis was conducted to examine the correlation between 18 key parameters and the pump-discharge pressure. Model Development and Optimization. The 18 input parameters were used to build an ANN using MATLAB. The neural network was trained using a set of known input/output pairs and tested on a separate set of data. The performance of the neural network was evaluated using various metrics such as mean squared error and correlation coefficient. After the ANN model was built, its performance was evaluated to determine the best configuration of the model. This was achieved by varying the number of input parameters, hidden layers, and neurons in each layer. The best model configuration was then identified that provided the most-accurate predictions and minimized the errors in the model.