Exploring the adoption of a conceptual data analytics framework for subsurface energy production systems: a study of predictive maintenance, multi-phase flow estimation, and production optimization

Als die Technologie weiter fortschreitet und immer stärker in der Öl- und Gasindustrie integriert wird, steht eine enorme Menge an Daten in verschiedenen Wissenschaftsdisziplinen zur Verfügung, die neue Möglichkeiten bieten, informationsreiche und handlungsorientierte Informationen zu gewinnen. Die Konvergenz der digitalen Transformation mit der Physik des Flüssigkeitsflusses durch poröse Medien und Pipeline hat die Entwicklung und Anwendung von maschinellem Lernen (ML) vorangetrieben, um weiteren Mehrwert aus diesen Daten zu gewinnen. Als Folge hat sich die digitale Transformation und ihre zugehörigen maschinellen Lernanwendungen zu einem neuen Forschungsgebiet entwickelt. Die Transformation von Brownfields in digitale Ölfelder kann bei der Energieproduktion helfen, indem verschiedene Ziele erreicht werden, einschließlich erhöhter betrieblicher Effizienz, Produktionsoptimierung, Zusammenarbeit, Datenintegration, Entscheidungsunterstützung und Workflow-Automatisierung. Diese Arbeit zielt darauf ab, ein Rahmenwerk für diese Anwendungen zu präsentieren, insbesondere durch die Implementierung virtueller Sensoren, Vorhersageanalytik mithilfe von Vorhersagewartung für die Produktionshydraulik-Systeme (mit dem Schwerpunkt auf elektrischen Unterwasserpumpen) und präskriptiven Analytik für die Produktionsoptimierung in Dampf- und Wasserflutprojekten. In Bezug auf virtuelle Messungen ist eine genaue Schätzung von Mehrphasenströmen für die Überwachung und Verbesserung von Produktionsprozessen entscheidend. Diese Studie präsentiert einen datengetriebenen Ansatz zur Berechnung von Mehrphasenströmen mithilfe von Sensormessungen in elektrischen untergetauchten Pumpbrunnen. Es wird eine ausführliche exploratorische Datenanalyse durchgeführt, einschließlich einer Ein Variablen Studie der Zielausgänge (Flüssigkeitsrate und Wasseranteil), einer Mehrvariablen-Studie der Beziehungen zwischen Eingaben und Ausgaben sowie einer Datengruppierung basierend auf Hauptkomponentenprojektionen und Clusteralgorithmen. Feature Priorisierungsexperimente werden durchgeführt, um die einflussreichsten Parameter in der Vorhersage von Fließraten zu identifizieren. Die Modellvergleich erfolgt anhand des mittleren absoluten Fehlers, des mittleren quadratischen Fehlers und des Bestimmtheitskoeffizienten. Die Ergebnisse zeigen, dass die CNN-LSTM-Netzwerkarchitektur besonders effektiv bei der Zeitreihenanalyse von ESP-Sensordaten ist, da die 1D-CNN-Schichten automatisch Merkmale extrahieren und informative Darstellungen von Zeitreihendaten erzeugen können. Anschließend wird in dieser Studie eine Methodik zur Umsetzung von Vorhersagewartungen für künstliche Hebesysteme, insbesondere bei der Wartung von Elektrischen Untergetauchten Pumpen (ESP), vorgestellt. Conventional maintenance practices for ESPs require extensive resources and manpower, and are often initiated through reactive monitoring of multivariate sensor data. Um dieses Problem zu lösen, wird die Verwendung von Hauptkomponentenanalyse (PCA) und Extreme Gradient Boosting Trees (XGBoost) zur Analyse von Echtzeitsensordaten und Vorhersage möglicher Ausfälle in ESPs eingesetzt. PCA wird als unsupervised technique eingesetzt und sein Ausgang wird weiter vom XGBoost-Modell für die Vorhersage des Systemstatus verarbeitet. Das resultierende Vorhersagemodell hat gezeigt, dass es Signale von möglichen Ausfällen bis zu sieben Tagen im Voraus bereitstellen kann, mit einer F1-Bewertung größer als 0,71 im Testset. Diese Studie integriert auch Model-Free Reinforcement Learning (RL) Algorithmen zur Unterstützung bei Entscheidungen im Rahmen der Produktionsoptimierung. Die Aufgabe, die optimalen Injektionsstrategien zu bestimmen, stellt Herausforderungen aufgrund der Komplexität der zugrundeliegenden Dynamik, einschließlich nichtlinearer Formulierung, zeitlicher Variationen und Reservoirstrukturheterogenität. Um diese Herausforderungen zu bewältigen, wurde das Problem als Markov-Entscheidungsprozess reformuliert und RL-Algorithmen wurden eingesetzt, um Handlungen zu bestimmen, die die Produktion optimieren. Die Ergebnisse zeigen, dass der RL-Agent in der Lage war, den Netto-Barwert (NPV) durch kontinuierliche Interaktion mit der Umgebung und iterative Verfeinerung des dynamischen Prozesses über mehrere Episoden signifikant zu verbessern. Dies zeigt das Potenzial von RL-Algorithmen, effektive und effiziente Lösungen für komplexe Optimierungsprobleme im Produktionsbereich zu bieten.As technology continues to advance and become more integrated in the oil and gas industry, a vast amount of data is now prevalent across various scientific disciplines, providing new opportunities to gain insightful and actionable information. The convergence of digital transformation with the physics of fluid flow through porous media and pipelines has driven the advancement and application of machine learning (ML) techniques to extract further value from this data. As a result, digital transformation and its associated machine-learning applications have become a new area of scientific investigation. The transformation of brownfields into digital oilfields can aid in energy production by accomplishing various objectives, including increased operational efficiency, production optimization, collaboration, data integration, decision support, and workflow automation. This work aims to present a framework of these applications, specifically through the implementation of virtual sensing, predictive analytics using predictive maintenance on production hydraulic systems (with a focus on electrical submersible pumps), and prescriptive analytics for production optimization in steam and waterflooding projects. In terms of virtual sensing, the accurate estimation of multi-phase flow rates is crucial for monitoring and improving production processes. This study presents a data-driven approach for calculating multi-phase flow rates using sensor measurements located in electrical submersible pumped wells. An exhaustive exploratory data analysis is conducted, including a univariate study of the target outputs (liquid rate and water cut), a multivariate study of the relationships between inputs and outputs, and data grouping based on principal component projections and clustering algorithms. Feature prioritization experiments are performed to identify the most influential parameters in the prediction of flow rates. Model comparison is done using the mean absolute error, mean squared error and coefficient of determination. The results indicate that the CNN-LSTM network architecture is particularly effective in time series analysis for ESP sensor data, as the 1D-CNN layers are capable of extracting features and generating informative representations of time series data automatically. Subsequently, the study presented herein a methodology for implementing predictive maintenance on artificial lift systems, specifically regarding the maintenance of Electrical Submersible Pumps (ESPs). Conventional maintenance practices for ESPs require extensive resources and manpower and are often initiated through reactive monitoring of multivariate sensor data. To address this issue, the study employs the use of principal component analysis (PCA) and extreme gradient boosting trees (XGBoost) to analyze real-time sensor data and predict potential failures in ESPs. PCA is utilized as an unsupervised technique and its output is further processed by the XGBoost model for prediction of system status. The resulting predictive model has been shown to provide signals of potential failures up to seven days in advance, with an F1 score greater than 0.71 on the test set. In addition to the data-driven modeling approach, The present study also in- corporates model-free reinforcement learning (RL) algorithms to aid in decision-making in production optimization. The task of determining the optimal injection strategy poses challenges due to the complexity of the underlying dynamics, including nonlinear formulation, temporal variations, and reservoir heterogeneity. To tackle these challenges, the problem was reformulated as a Markov decision process and RL algorithms were employed to determine actions that maximize production yield. The results of the study demonstrate that the RL agent was able to significantly enhance the net present value (NPV) by continuously interacting with the environment and iteratively refining the dynamic process through multiple episodes. This showcases the potential for RL algorithms to provide effective and efficient solutions for complex optimization problems in the production domain. In conclusion, this study represents an original contribution to the field of data-driven applications in subsurface energy systems. It proposes a data-driven method for determining multi-phase flow rates in electrical submersible pumped (ESP) wells utilizing sensor measurements. The methodology includes conducting exploratory data analysis, conducting experiments to prioritize features, and evaluating models based on mean absolute error, mean squared error, and coefficient of determination. The findings indicate that a convolutional neural network-long short-term memory (CNN-LSTM) network is an effective approach for time series analysis in ESPs. In addition, the study implements principal component analysis (PCA) and extreme gradient boosting trees (XGBoost) to perform predictive maintenance on ESPs and anticipate potential failures up to a seven-day horizon. Furthermore, the study applies model-free reinforcement learning (RL) algorithms to aid decision-making in production optimization and enhance net present value (NPV)

Selection of artificial lift method

Faculty: Graduate School of Science, Art, and Technology Department: Petroleum Engineering Speciality: Development of Oil and Gas Fields Supervisor: Yelena Shmoncheva (Ph.D. in technology, associate professor)For years, operating firms have used artificial lift optimization (ALO) technologies to boost oil and gas production. Notwithstanding these exceptional results in the oil business, conventional and unconventional gas wells have made little headway with an artificial lift. This is owing, in part, to the unavailability of artificial lift tools that can directly add energy to the gas to improve production and recoverability until now. Although wellhead compressors have proven to be helpful for operators, actual experience has demonstrated that these tools can accelerate liquid loading, especially in unconventional wells with a greater critical lifting velocity and lower production fluid density. As a result, productivity falls and early retirement occurs. Due to faster rates of decline than conventional wells, a growing proportion of unconventional shale oil and shale gas wells now contain artificial lift at the start of well production, even though mature wells are more frequently the recipients of artificial lift systems. Furthermore, field production can be increased by utilizing various ALO solutions at various stages of a well's production life. For example, to maximize estimated ultimate recovery (EUR), rod pump systems installed later in the well's life can be combined with the installation of a jet pump or an electric submersible pump (ESP) system during the early and transitional phases of the well's life, respectively. Evaluation of the initial artificial lift systems in place to find better ways to manufacture the existing assets and, as a result, strategies to optimize and reduce lifting costs for the operator. This method applies a number of filters to the user-supplied well, fluid, and field data, resulting in a summary page providing viable and preferable well specific artificial lift alternatives for future exploration. For the extraction of hydrocarbons from underground formations, a variety of production methods might be chosen. The built into energy found within the reservoir itself can be used to lift reservoir fluids to the surface, or artificial lift techniques can be used. The primary goal of this thesis work is to pick artificial lift technologies for hydrocarbon production in the ARC field in Mexico utilizing a customized computer software program. (called PROSPER)

Predicting Non-Newtonian Fluid Electric Submersible Pump failure using Deep Learning and Artificial Neural Network

The monitoring of electric submersible pumps (ESPs) is essential for optimal petroleum artificial lifting operations. Most ESP research are aimed at operation improvement and optimization of the centrifuge multi-stage pump motor and the load that the pump has to discharge which is a function of the pumps mechanical properties and characteristics, liquid compositions, pressure and temperature. ESPs failure often lead to oil production losses or “oil deferment” which affects revenue for all the parties involved. Also, pulling the ESP out of the wellbore of interest, requires mobilization of a rig because it is installed several hundred meters down the wellbore. To prevent these loses, a predictive approach is needed to avert these scenarios. In the current decade, machine learning algorithms studies have spurred real- time technologies research interest due to their abilities to predict future outcomes using already existing data sets. This study presents a predictive approach for Electric Submersible Pump failure during artificial lift operations. The study creates an “algorithm” that helps to predict via Machine learning, the failure of an ESP with the assumption that failure is usually caused by pressure build-ups. A deep learning model for predicting ESP failure was proposed and artificial neural network was used in developing the suggested model. Based on the outcomes of this study, it can be concluded that the selected AI algorithm and its characteristics, are suitable for applications in detecting ESP failure before it happens using upstream-data

MODEL BASED ON BOOLEAN LOGIC FOR SCREENING AND SELECTION OF THE ARTIFICIAL LIFT METHODS

Artificial lift methods are used in oil wells when the natural energy of the reservoir is not enough to bring the reservoir fluids at the surface. Therefore, these methods are used to add the missing energy. There are many types of artificial lift methods with different characteristics. The selection of an artificial lift technique for a well is crucial because it influences long-term well production and operating costs. In our paper, we propose an application that allows a quick screening and selection of the best artificial lift technique. The application uses Boolean logic as a mathematical and conceptual approach. The model is based on a memory set of data which is working like a database and compares the information in order to select the best artificial lift method for a well data set. The results are presented in two layers such as a front page used for input data set and a second one where it will be displayed the selected method and its advantages and disadvantages

Cost reduction strategies for the rural village energy concept

Tese de mestrado integrado em Engenharia da Energia e do Ambiente, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2013A agência internacional de energia estima que cerca de um quinto da população mundial não tem, acesso a eletricidade, particularmente em zonas rurais, maioritariamente na África Subsaariana e no Sudeste Asiático. A taxa de eletrificação rural na África Subsaariana era, em 2009, cerca de 14 % - consideravelmente abaixo da média mundial de 68%. As Nações Unidas apontam que a disponibilidade de energia (em especial energia elétrica) sustentável com baixo custo, tem o potencial de promover a educação, o acesso a água potável, a igualdade de géneros, diminuição da pobreza, e sustentabilidade ambiental – assim, a disponibilidade de energia pode ter um impacto direto e considerável na realização dos oito Objetivos de Desenvolvimento do Milénio. Os sistemas descentralizados de energia como o KUDURA podem providenciar energia limpa e água potável a comunidades rurais ou remotas. Apesar disso, este tipo de abordagens requerem um maior atual desenvolvimento, de modo a que se consiga aumentar a sua competitividade tecnológica e económica, e assim, a sua flexibilidade em ser distribuído em zonas rurais. O presente trabalho estuda duas tecnologias e case-studies em específico: Gaseificação de biomassa de pequena dimensão para a geração de eletricidade no Norte de Moçambique, utilizando casca de caju como combustível; e micro-hídrica para geração e armazenamento de eletricidade no litoral do Quénia – estas aplicações foram contempladas como possíveis formas de reduzir os atuais custos do KUDURA. Estas tecnologias são comparadas e analisadas com recurso ao software HOMER – uma ferramenta de análise para a avaliação de diferentes tecnologias e recursos energéticos e sua otimização com base em critérios económicos. O custo de energia para o sistema híbrido de gaseificação a biomassa é de 0.46 €/kWh, em oposição aos 0.53 €/kWh do sistema KUDURA. Estes resultados mostraram ser sensíveis a variáveis como o preço do caju, a potência do sistema solar fotovoltaico e, mais importante, sensíveis ao custo de operação e manutenção – em particular, o salário dos técnicos locais. Em relação ao sistema hídrico de fio-de-água proposto, é mostrado nesta análise que o custo de energia situa-se na gama de 0.17-0.27 €/kWh, tornando este sistema particularmente adequado a regiões com um recurso hídrico abundante. Por outro lado, como opção de armazenamento hídrico de energia através de bombagem de água, os resultados simulados sugerem que pode não tornar-se economicamente competitivo com as formas tradicionais de armazenamento de energia eletroquímica.The International Energy Agency estimates that about one-fifth of the world’s population does not have access to electricity in particular in rural areas, mainly in sub-Saharan Africa and South Asia. In 2009, the rate of rural electrification in sub-Saharan Africa was 14%, considerably below the global average of 68%. The United Nations has found that access to affordable and sustainable energy, particularly electricity, can promote education, access to potable and safe water, gender equity, poverty’s end and environmental sustainability, thus electricity can have a direct impact on achieving the eight Millennium Development Goals. Decentralised energy systems, like the KUDURA concept, have the ability to provide clean energy and potable water to rural or remote communities. Nevertheless, these approaches require further development to increase its cost-effectiveness and deployment flexibility. The present work looks at two specific technologies and case-studies: small-scale biomass gasification for power generation using cashew nut shells as feedstock for the northern region of Mozambique; and micro hydro-power for power generation and energy storage for coastal Kenya – which were seen as possible cost reduction routes. These technologies are compared and analysed through the use of the HOMER software, an analytic tool for evaluating different energy technologies and resources and optimization based on economic criteria. The levelised cost of energy (LCOE) of the optimized hybrid biomass gasification system may reach €0.46/kWh, significantly below the KUDURA baseline cost of €0.53/kWh. These results are sensitive to variables such as the feedstock cost, the photovoltaic array required and, most importantly, to the costs associated to operation and maintenance, in particular the salaries of the local technicians. Regarding the proposed run-of-the-river-type hydro system, it is shown that it may achieve a LCOE in the range of €0.17-0.27/kWh, making it particularly suitable for regions with an abundant hydro resource. On the other hand, as a form of energy storage via pumped water storage, the simulation results suggested it cannot become competitive with standard electrochemical energy storage

No-Load and Load Tests on 3 Phase Induction Motors used in Irrigation Pumping with Balanced and Unbalanced Supply

Agriculture is the major ocupation in villages and is backbone of India, where irrigated agriculture sector plays an important role in economic development and poverty alleviation of the nation. About 75% of present population of India obtains its livelyhood from the same. Iirrigation is harnessing of water resources for the crops by using motor pumps. Usually wells, tanks, perennial canal and multipurpose river valley projects are worked out. As electrical motors are very affordable and cheaper they are the usual choice to drive pumps. Due to the variation in supply to the pump motor the expected performance is not achieved and lead to variation in machine parameters. The operation of 3 phase motor on single phase supply leads to negative effects like overheating, insulation failure, torque pulsation, de rating and reduction in efficiency. In spite of these adverse effects on motor and irrigation power supply feeder, it is observed that farmers run their 3 phase motor on reduced voltage condition using capacitor splitter and other such. A study of operation of 3 phase motor on no-load and load is performed with balanced and unbalanced voltage condition. Simulation is performed in MATLAB/SIMULINK package for the comparison and justification

Applications of control theory

Applications of control theory are considered in the areas of decoupling and wake steering control of submersibles, a method of electrohydraulic conversion with no moving parts, and socio-economic system modelling

An integrated model for asset reliability, risk and production efficiency management in subsea oil and gas operations

PhD ThesisThe global demand for energy has been predicted to rise by 56% between 2010 and 2040 due to industrialization and population growth. This continuous rise in energy demand has consequently prompted oil and gas firms to shift activities from onshore oil fields to tougher terrains such as shallow, deep, ultra-deep and arctic fields. Operations in these domains often require deployment of unconventional subsea assets and technology. Subsea assets when installed offshore are super-bombarded by marine elements and human factors which increase the risk of failure. Whilst many risk standards, asset integrity and reliability analysis models have been suggested by many previous researchers, there is a gap on the capability of predictive reliability models to simultaneously address the impact of corrosion inducing elements such as temperature, pressure, pH corrosion on material wear-out and failure. There is also a gap in the methodology for evaluation of capital expenditure, human factor risk elements and use of historical data to evaluate risk. This thesis aims to contribute original knowledge to help improve production assurance by developing an integrated model which addresses pump-pipe capital expenditure, asset risk and reliability in subsea systems. The key contributions of this research is the development of a practical model which links four sub-models on reliability analysis, asset capital cost, event risk severity analysis and subsea risk management implementation. Firstly, an accelerated reliability analysis model was developed by incorporating a corrosion covariate stress on Weibull model of OREDA data. This was applied on a subsea compression system to predict failure times. A second methodology was developed by enhancing Hubbert oil production forecast model, and using nodal analysis for asset capital cost analysis of a pump-pipe system and optimal selection of best option based on physical parameters such as pipeline diameter, power needs, pressure drop and velocity of fluid. Thirdly, a risk evaluation method based on the mathematical determinant of historical event magnitude, frequency and influencing factors was developed for estimating the severity of risk in a system. Finally, a survey is conducted on subsea engineers and the results along with the previous models were developed into an integrated assurance model for ensuring asset reliability and risk management in subsea operations. A guide is provided for subsea asset management with due consideration to both technical and operational perspectives. The operational requirements of a subsea system can be measured, analysed and improved using the mix of mathematical, computational, stochastic and logical frameworks recommended in this work

District heting system of IZTECH Campus and its integration to the existing system

Thesis (Master)--Izmir Institute of Technology, Mechanical Engineering, Izmir, 2003Includes bibliographical references (leaves: 116-119)Text in English; Abstract: Turkish and Englishxviii, 137 leavesIzmir Institute of Technology (IZTECH), founded in 1992, is the third state university of Izmir. At present IZTECH Campus has individual fuel boiler heating systems for each faculty building and the Campus is still under development. But the Campus has also a geothermal source. In 2002, 5 gradient wells were drilled. Of these, one well has a geothermal fluid of 33°C is obtained but the actual flowrate of the geothermal fluid has not been measured yet. The aim of this Thesis is to investigate this source whether it can be used for district heating application for IZTECH Campus. Mainly two heating system types have been considered;. Heat pump heating system (HPHS) (using a renewable energy source, geothermal energy), . Fuel boiler heating system (FBHS) (using a conventional energy source, fuel-oil). HPHS is considered as HPO type since the existing geothermal fluid temperature is low. While HPHS is considered only as district, FBHS is considered as district and individual. Each heating system is simulated using hourly outdoor temperature data. For these heating simulations, the main control parameter is the indoor temperature of the buildings. Mathematical models are derived using Matlab [16] and EES [17] programs. Various heating regime alternatives have been studied for HPHS for the various condenser outlet temperature and geothermal fluid flowrate. Consequently, the heating regime with 35°C condenser inlet and 45°C condenser outlet temperature with 120 kg/s geothermal fluid flowrate considered to be the best option. FBHSs are also simulated for various boiler set temperatures. Boiler set emperatures have been recommended by Demirdöküm [39], is the best alternative with the least fuel consumption and best indoor temperature around 20°C. Besides heating system simulations, piping network simulation is made using the software Pipelab [18]. The piping network of the Campus has been considered with two loops as geothermal and Campus. Each loop contains supply and return main. The location of the heat centre and the pressure loss per unit length are common design parameters for economy of the system. Therefore, several alternatives have been studied and because of the lowest investment and operational cost, Alternative 3, where the heat centre is in the middle of the Campus, is considered to be the best option with target pressure loss of 150 Pa/m. For installation type of piping network, underground (buried) pipeline installation is selected. Furthermore, economic analysis has also been done for each heating system alternative depending on investment and operational costs. For operational cost, 3 heating scenarios are considered depending on the heating period of the buildings in the Campus. According to the results of economical analyses, while heat pump district heating system (HPDHS) has the biggest investment cost with 3,040,125 US$, it has minimum operational cost. The alternatives are evaluated according to internal rate of return (IRR) method, which shows the profit of the investment. The results indicate that, the HPDHS has minimum 3.02% profit according to the fuel boiler district heating system (FBDHS) at the end of the 20-year period. The profit increases with increasing operating period of the heating systems

Optimisation of Balçova-Narlıdere geothermal district heating system

Thesis (Master)--Izmir Institute of Technology, Mechanical Engineering, Izmir, 2003Includes bibliographical references (leaves: 118-121)Text in English; Abstract: Turkish and Englishxiv, 121 leavesThe main goal of this study is to determine optimum control strategy of Balçova-Narlıdere geothermal district heating system to minimise the energy consumption. First heat demand model of the system was constructed by using statistical method called time series analysis. This model provides the heat demand forecast of next day, by considering ambient temperature forecast of the next day. Then geothermal pipeline system and city distribution system have been modelled in the PIPELAB district heating simulation program. To model the system close to the actual case, database of Balçova geothermal company was used as an input, and the code of PIPELAB program was adapted to be used in geothermal pipeline system. Once the sysem was modelled in PIPELAB, it would be possible to obtain pressure and temperature distribution along the pipe networks in the system. To determine the optimum operation strategy of the wells according to the changing heat demand first the energy consumption of each well pump was defined as a function of their heat production rate. Then these functions were inserted into dynamic programming algorithm which selects the optimum well operation strategy among thousands of options. Also power consumption models of circulation pumps were built and calibrated with actual values. Finally optimum control strategy for the system was determined and the system model was operated by using optimum control strategy according to ambient temperature data of 2001 and 2002. The acual energy consumption values were compared with the optimum energy consumption values and decisive factors in efficient control and operation of the system have been defined