Electrical Submersible Pump Prognostics and Health Monitoring Using Machine Learning and Natural Language Processing

2021 ◽  
Author(s):  
Rajeev Ranjan Sinha ◽  
Supriya Gupta ◽  
Praprut Songchitruksa ◽  
Saniya Karnik ◽  
Amey Ambade
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Operating Conditions ◽  
Learning Approaches ◽  
Submersible Pump ◽  
Gas Industry ◽  
Electrical Submersible Pump ◽  
Low Efficiency

Abstract Electrical Submersible Pump (ESP) systems efficiently pump high volumes of production fluids from the wellbore to the surface. They are extensively used in the oil and gas industry due to their adaptability, low maintenance, safety and relatively low environmental impact. They require specific operating conditions with respect to the power, fluid level and fluid content. Oilfield operation workflows often require extensive surveillance and monitoring by subject-matter experts (SMEs). Detecting issues like formation of unwanted gas and emulsions in ESPs requires constant analysis of downhole data by SMEs. The lack of adequate and accurate monitoring of the downhole pumps can lead to low efficiency, high lifting costs, and frequent repair and replacements. There are 3 workflows described in the paper which demonstrate that the maintenance costs of the ESPs can be significantly reduced, and production optimized with the augmentation of machine learning approaches typically unused in ESP surveillance and failure analysis.

Fundamentals of Wireless Sensor Networks Using Machine Learning Approaches

2020 ◽  
pp. 233-254
Author(s):  
E. B. Priyanka ◽  
S. Thangavel ◽  
D. Venkatesa Prabu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Real Time ◽  
Oil And Gas ◽  
Predictive Analytics ◽  
Oil And Gas Industry ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Learning Approaches ◽  
Gas Industry

Big data and analytics may be new to some industries, but the oil and gas industry has long dealt with large quantities of data to make technical decisions. Oil producers can capture more detailed data in real-time at lower costs and from previously inaccessible areas, to improve oilfield and plant performance. Stream computing is a new way of analyzing high-frequency data for real-time complex-event-processing and scoring data against a physics-based or empirical model for predictive analytics, without having to store the data. Hadoop Map/Reduce and other NoSQL approaches are a new way of analyzing massive volumes of data used to support the reservoir, production, and facilities engineering. Hence, this chapter enumerates the routing organization of IoT with smart applications aggregating real-time oil pipeline sensor data as big data subjected to machine learning algorithms using the Hadoop platform.

Submersible Pump with Line Shaft Pump in Shallow Water, Optimum Selection for Offshore Application in the Oil & Gas Industry

2021 ◽  
Author(s):  
Shafir Mohammed ◽  
Ibrahim Kobbia
Keyword(s):  
Life Cycle Cost ◽  
Capital Investment ◽  
Oil And Gas ◽  
Cost Effective ◽  
Oil And Gas Industry ◽  
Vertical Line ◽  
Submersible Pump ◽  
Gas Industry ◽  
Electrical Submersible Pump ◽  
Operational Expenditure

Abstract Seawater is an essential fluid used in various process circuits, such as cooling, reinjection into the wells and utilities, etc., in the offshore oil and gas industry. Vertical pumps facilitates with lifting seawater to the platform. This study investigates and compares two pump alternatives that has been widely used in oil and gas industry for seawater lift application: Vertical line-shaft pump and Electrical submersible pump. Existing seawater lift pump operating parameters are used as the basis of this study. The pump that is considered for the study has a flow rate of 3415 US GPM (776 m3/hr.) with a total head of 250 ft. The motor rating is 350 HP. The overall length of the pump is 21 meters. The main methodology used is a Life Cycle Cost Analysis (LCC) where the total cost of ownership of the vertical line-shaft pump and electrical submersible pump were analyzed for a period of 30 years. Furthermore, this research also addresses the operational drawbacks associated with both the pumps. Submersible pumps have higher initial capital investment cost when compared to line-shaft pump of similar capacity and size. The energy consumption cost of submersible pumps are higher mainly owing to lower efficiencies of the motors. The power factor for submersible pumps are lower in relation to line- shaft pumps. One of the main benefits of submersible pumps are their less installation and pump pullout time. Submersible pumps occupies lower space above ground when compared to line-shaft pumps. Additionally, submersible pumps are less noisy and have lower vibration in comparison to line-shaft pumps. This paper aims to provide key information and knowledge for engineers to make prudent decision regarding selection of the most cost effective pump for the seawater lift application with a tangible added cost value to both Capital Expenditures (CAPEX) and Operational Expenditure (OPEX).

scholarly journals Multiphase gas-flow model of an electrical submersible pump

2018 ◽  
Vol 73 ◽  
pp. 29
Author(s):  
Diana Marcela Martinez Ricardo ◽  
German Efrain Castañeda Jiménez ◽  
Janito Vaqueiro Ferreira ◽  
Pablo Siqueira Meirelles
Keyword(s):  
Gas Flow ◽  
Oil And Gas ◽  
Learning Algorithm ◽  
Estimation Error ◽  
Oil And Gas Industry ◽  
Support Vector ◽  
System Response ◽  
Submersible Pump ◽  
Two Phase ◽  
Electrical Submersible Pump

Various artificial lifting systems are used in the oil and gas industry. An example is the Electrical Submersible Pump (ESP). When the gas flow is high, ESPs usually fail prematurely because of a lack of information about the two-phase flow during pumping operations. Here, we develop models to estimate the gas flow in a two-phase mixture being pumped through an ESP. Using these models and experimental system response data, the pump operating point can be controlled. The models are based on nonparametric identification using a support vector machine learning algorithm. The learning machine’s hidden parameters are determined with a genetic algorithm. The results obtained with each model are validated and compared in terms of estimation error. The models are able to successfully identify the gas flow in the liquid-gas mixture transported by an ESP.

Evaluation of Machine-Learning Tools for Predicting Sand Production

2021 ◽  
Author(s):  
Afungchwi Ronald Ngwashi ◽  
David O. Ogbe ◽  
Dickson O. Udebhulu
Keyword(s):  
Machine Learning ◽  
Niger Delta ◽  
Oil And Gas ◽  
Back Propagation ◽  
Oil And Gas Industry ◽  
Learning Tools ◽  
Sand Production ◽  
Data Set ◽  
Test Set ◽  
Gas Industry

Abstract Data analytics has only recently picked the interest of the oil and gas industry as it has made data visualization much simpler, faster, and cost-effective. This is driven by the promising innovative techniques in developing artificial intelligence and machine-learning tools to provide sustainable solutions to ever-increasing problems of the petroleum industry activities. Sand production is one of these real issues faced by the oil and gas industry. Understanding whether a well will produce sand or not is the foundation of every completion job in sandstone formations. The Niger Delta Province is a region characterized by friable and unconsolidated sandstones, therefore it's more prone to sanding. It is economically unattractive in this region to design sand equipment for a well that will not produce sand. This paper is aimed at developing a fast and more accurate machine-learning algorithm to predict sanding in sandstone formations. A two-layered Artificial Neural Network (ANN) with back-propagation algorithm was developed using PYTHON programming language. The algorithm uses 11 geological and reservoir parameters that are associated with the onset of sanding. These parameters include depth, overburden, pore pressure, maximum and minimum horizontal stresses, well azimuth, well inclination, Poisson's ratio, Young's Modulus, friction angle, and shale content. Data typical of the Niger Delta were collected to validate the algorithm. The data was further split into a training set (70%) and a test set (30%). Statistical analyses of the data yielded correlations between the parameters and were plotted for better visualization. The accuracy of the ANN algorithm is found to depend on the number of parameters, number of epochs, and the size of the data set. For a completion engineer, the answer to the question of whether or not a well will require sand production control is binary-either a well will produce sand or it does not. Support vector machines (SVM) are known to be better suited as the machine-learning tools for binary identification. This study also presents a comparative analysis between ANN and SVM models as tools for predicting sand production. Analysis of the Niger Delta data set indicated that SVM outperformed ANN model even when the training data set is sparse. Using the 30% test set, ANN gives an accuracy, precision, recall, and F1 - Score of about 80% while the SVM performance was 100% for the four metrics. It is then concluded that machine learning tools such as ANN with back-propagation and SVM are simple, accurate, and easy-to-use tools for effectively predicting sand production.

Estimation of Bottom Hole Pressure in Electrical Submersible Pump Wells using Machine Learning Technique

2021 ◽  
Author(s):  
Sherif Sanusi ◽  
Adenike Omisore ◽  
Eyituoyo Blankson ◽  
Chinedu Anyanwu ◽  
Obehi Eremiokhale
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Oil And Gas Industry ◽  
Supervised Machine Learning ◽  
Power Cable ◽  
Submersible Pump ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole ◽  
Electrical Submersible Pump

Abstract With the growing importance and application of Machine Learning in various complex operations in the Oil and Gas Industry, this study focuses on the implementation of data analytics for estimating and/or validating bottom-hole pressure (BHP) of Electrical Submersible Pump (ESP) wells. Depending on the placement of the ESP in the wellbore and fluid gravity of the well fluid, there can be little or no difference between BHP and Pump intake Pressure (PIP); hence these two parameters were used interchangeably. The study focuses majorly on validating PIP when there are concerns with downhole gauge readings. It also has application in estimating PIP when the gauge readings are not available, provided the relevant ESP parameters are obtainable. ESP wells generally have gauges that operate on "Comms-on-Power" principle i.e. downhole communication is via the power cable and loss of signal occurs when there is no good electrical integrity along the electrical path of the ESP system. For proper hydrocarbon accounting and statutory requirements, it is important to have downhole pressure readings on a continuous basis, however this cannot be guaranteed throughout the life cycle of the well. Therefore, an alternative method is essential and had to be sought. In this study, the Response Surface Modelling (RSM) was first used to generate a model relating the ESP parameters acquired real-time to the PIP values. The model was fine-tuned with a Supervised Machine Learning algorithm: Artificial Neural Network (ANN). The performance of the algorithms was then validated using the R-Square and Mean Square Error values. The result proves that Machine Learning can be used to estimate PIP in a well without recourse to incurring additional cost of deploying new downhole gauges for acquisition of well and reservoir data.

A Novel Methodology for Optimal Design of Compressor Plants Using Probabilistic Plant Design

2013 ◽  
Vol 135 (11) ◽  
Author(s):  
Rainer Kurz ◽  
J. Michael Thorp ◽  
Erik G. Zentmyer ◽  
Klaus Brun
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Operating Conditions ◽  
Process Conditions ◽  
Plant Design ◽  
Well Performance ◽  
Worst Case ◽  
Gas Industry ◽  
Machine Performance ◽  
Equipment Sizing

Equipment sizing decisions in the oil and gas industry often have to be made based on incomplete data. Often, the exact process conditions are based on numerous assumptions about well performance, market conditions, environmental conditions, and others. Since the ultimate goal is to meet production commitments, the traditional method of addressing this is to use worst case conditions and often adding margins onto these. This will invariably lead to plants that are oversized, in some instances, by large margins. In reality, the operating conditions are very rarely the assumed worst case conditions, however, they are usually more benign most of the time. Plants designed based on worst case conditions, once in operation, will, therefore, usually not operate under optimum conditions, have reduced flexibility, and therefore cause both higher capital and operating expenses. The authors outline a new probabilistic methodology that provides a framework for more intelligent process-machine designs. A standardized framework using a Monte Carlo simulation and risk analysis is presented that more accurately defines process uncertainty and its impact on machine performance. Case studies are presented that highlight the methodology as applied to critical turbomachinery.

Wheel Box Test Aeromechanical Verification of New First Stage Bucket With Integrated Cover Plates for MS5002 GT

2019 ◽  
Author(s):  
Marco Mariottini ◽  
Nicola Pieroni ◽  
Pietro Bertini ◽  
Beniamino Pacifici ◽  
Alessandro Giorgetti
Keyword(s):  
Gas Turbine ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Response Assessment ◽  
Operating Conditions ◽  
Analytical Models ◽  
Gas Industry ◽  
Improved Performance ◽  
Cover Plates

Abstract In the oil and gas industry, manufacturers are continuously engaged in providing machines with improved performance, reliability and availability. First Stage Bucket is one of the most critical gas turbine components, bearing the brunt of very severe operating conditions in terms of high temperature and stresses; aeromechanic behavior is a key characteristic to be checked, to assure the absence of resonances that can lead to damage. Aim of this paper is to introduce a method for aeromechanical verification applied to the new First Stage Bucket for heavy duty MS5002 gas turbine with integrated cover plates. This target is achieved through a significantly cheaper and streamlined test (a rotating test bench facility, formally Wheel Box Test) in place of a full engine test. Scope of Wheel Box Test is the aeromechanical characterization for both Baseline and New bucket, in addition to the validation of the analytical models developed. Wheel Box Test is focused on the acquisition and visualization of dynamic data, simulating different forcing frequencies, and the measurement of natural frequencies, compared with the expected results. Moreover, a Finite Elements Model (FEM) tuning for frequency prediction is performed. Finally, the characterization of different types of dampers in terms of impact on frequencies and damping effect is carried out. Therefore, in line with response assessment and damping levels estimation, the most suitable damper is selected. The proposed approach could be extended for other machine models and for mechanical audits.

scholarly journals Stochastic Simplex Approximate Gradient for Robust Life-Cycle Production Optimization: Applied to Brugge Field

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Bailian Chen ◽  
Jianchun Xu
Keyword(s):  
Life Cycle ◽  
Net Present Value ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Optimization Techniques ◽  
Production Optimization ◽  
Operating Conditions ◽  
Optimization Under Uncertainty ◽  
Theoretical Argument ◽  
Gas Industry

In oil and gas industry, production optimization is a viable technique to maximize the recovery or the net present value (NPV). Robust optimization is one type of production optimization techniques where the geological uncertainty of reservoir is considered. When well operating conditions, e.g., well flow rates settings of inflow control valves and bottom-hole pressures, are the optimization variables, ensemble-based optimization (EnOpt) is the most popular ensemble-based algorithm for the robust life-cycle production optimization. Recently, a superior algorithm, stochastic simplex approximate gradient (StoSAG), was proposed. Fonseca and co-workers (2016, A Stochastic Simplex Approximate Gradient (StoSAG) for Optimization Under Uncertainty, Int. J. Numer. Methods Eng., 109(13), pp. 1756–1776) provided a theoretical argument on the superiority of StoSAG over EnOpt. However, it has not drawn significant attention in the reservoir optimization community. The purpose of this study is to provide a refined theoretical discussion on why StoSAG is generally superior to EnOpt and to provide a reasonable example (Brugge field) where StoSAG generates estimates of optimal well operating conditions that give a life-cycle NPV significantly higher than the NPV obtained from EnOpt.

Machine Learning as Accelerating Tool in Remote Operation Realisation through Monitoring Oil and Gas Equipments and Identifying its Failure Mode

2021 ◽  
Author(s):  
Ahmad Naufal Naufal ◽  
Samy Abdelhamid Samy ◽  
Nenisurya Hashim Nenisurya ◽  
Zaharuddin Muhammad Zaharuddin ◽  
Eddy Damsuri Eddy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Failure Mode ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Support Vector ◽  
Environmental Damage ◽  
Equipment Failure ◽  
Statistical Regression ◽  
Gas Industry ◽  
Plant Equipment

Abstract Equipment failure, unplanned downtime operation, and environmental damage cost represent critical challenges in overall oil and gas business from well reservoir identification and drilling strategy to production and processing. Identifying and managing the risks around assets that could fail and cause redundant and expensive downtime are the core of plant reliability in oil and gas industry. In the current digital era; there is an essential need of innovative data-driven solutions to address these challenges, especially, monitoring and diagnosis of plant equipment operations, recognize equipment failure; avoid unplanned downtime; repair costs and potential environmental damage; maintaining reliable production, and identifying equipment failures. Machine learning-artificial intelligence application is being studied to develop predictive maintenance (PdM) models as innovative analytics solution based on real-data streaming to get to an elevated level of situational intelligence to guide actions and provide early warnings of impending asset failure that previously remained undetected. This paper proposes novel machine learning predictive models based on extreme learning/support vector machines (ELM-SVM) to predict the time to failure (TTF) and when a plant equipment(s) will fail; so maintenance can be planned well ahead of time to minimize disruption. Proper visualization with deep-insights (training and validation) processes of the available mountains of historian and real-time data are carried out. Comparative studies of ELM-SVM techniques versus the most common physical-statistical regression techniques using available rotating equipment-compressors and time-failure mode data. Results are presented and it is promising to show that the new machine learning (ELM-SVM) techniques outperforms physical-statistics techniques with reliable and high accurate predictions; which have a high impact on the future ROI of oil and gas industry.

Sign in / Sign up

Export Citation Format

Share Document