Evaluation of the Inter-repair Operation Period of Electric Submersible Pump Units

In recent years, in the oil and gas industry of Azerbaijan, the use of electric submersible pumps (SEP) as one of the effective way to increase the level of production of well products. Currently, electric centrifugal pumping units (ECPU) are widely used both on land and in offshore fields. Currently, a total of about 15 % of SOCAR\u27s oil wells are produced using electric submersible pumping units.ECPU effectiveness is largely determined by both the period of their operation and the frequency of repair and restoration work.It is established that the use of ECPUs contributes to an increase in the service life of equipment and the effectiveness of a mechanized method of oil production. To assess the benefits of the latter, the most important factor is the inter-repair period (Tir) of the equipment.Existing methods for determining the inter-repair period of oilfield equipment are accompanied by large errors, which significantly reduce their reliability.In this regard, the article is tasked with developing a more practical and reliable method for determining the inter-repair period, where the point of change in the nature of the failure rate is adopted as the determining paramete

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)

EVALUATION OF THE INTER-REPAIR OPERATION PERIOD OF ELECTRIC SUBMERSIBLE PUMP UNITS

In recent years, in the oil and gas industry of Azerbaijan, the use of electric submersible pumps (SEP) as one of the effective way to increase the level of production of well products. Currently, electric centrifugal pumping units (ECPU) are widely used both on land and in offshore fields. Currently, a total of about 15 % of SOCAR’s oil wells are produced using electric submersible pumping units. ECPU effectiveness is largely determined by both the period of their operation and the frequency of repair and restoration work. It is established that the use of ECPUs contributes to an increase in the service life of equipment and the effectiveness of a mechanized method of oil production. To assess the benefits of the latter, the most important factor is the inter-repair period (Tir) of the equipment. Existing methods for determining the inter-repair period of oilfield equipment are accompanied by large errors, which significantly reduce their reliability. In this regard, the article is tasked with developing a more practical and reliable method for determining the inter-repair period, where the point of change in the nature of the failure rate is adopted as the determining paramete

Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint, more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become greener, and hence to act in a manner not required previously. While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance technologies, that would foresee potential failures, making production safer, lowering downtime, increasing productivity and diminishing maintenance costs. Many efforts were applied in order to define the most accurate and effective predictive methods, however data scarcity affects the speed and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection, using the open public data from Petrobras, that was developed by experts. For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM and GRU backbones, were implemented for multi-class classification of undesirable events on naturally flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on Genetic Algorithms as being the most advanced methods for such kind of tasks. The research concluded with the best performing algorithm with 2 stacked GRU and the following vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units 47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%. As the world faces many issues, one of which is the detrimental effect of heavy industries to the environment and as result adverse global climate change, this project is an attempt to contribute to the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it more efficient, transparent and sustainable

EVALUATION OF THE INTER-REPAIR OPERATION PERIOD OF ELECTRIC SUBMERSIBLE PUMP UNITS

In recent years, in the oil and gas industry of Azerbaijan, the use of electric submersible pumps (SEP) as one of the effective way to increase the level of production of well products. Currently, electric centrifugal pumping units (ECPU) are widely used both on land and in offshore fields. Currently, a total of about 15 % of SOCAR’s oil wells are produced using electric submersible pumping units.ECPU effectiveness is largely determined by both the period of their operation and the frequency of repair and restoration work.It is established that the use of ECPUs contributes to an increase in the service life of equipment and the effectiveness of a mechanized method of oil production. To assess the benefits of the latter, the most important factor is the inter-repair period (Tir) of the equipment.Existing methods for determining the inter-repair period of oilfield equipment are accompanied by large errors, which significantly reduce their reliability.In this regard, the article is tasked with developing a more practical and reliable method for determining the inter-repair period, where the point of change in the nature of the failure rate is adopted as the determining paramete

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

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

APPLYING MACHINE LEARNING MODELS TO DIAGNOSE FAILURES IN ELECTRICAL SUBMERSIBLE PUMPS

Electrical Submersible Pump (ESP) failures are unanticipated but common occurrences in oil and gas wells. It is necessary to detect the onset of failures early and prevent excessive downtime. This study proposes a novel approach utilizing multi-class classification machine learning models to predict various ESP specific failure modes (SFM’s). A comprehensive dataset and various machine learning algorithms are utilized. The prediction periods of 3 hours to 7 days before the failure are evaluated to minimize false alarms and predict the true events. The ML models are based on field data gathered from surface and downhole ESP monitoring equipment over five years of production of 10 wells. The dataset includes the failure cause, duration of downtime, the corresponding high-frequency pump data, and well production data. According to these data, most ESP operational failures are characterized as electrical failures. Four modeling designs are used to handle the data and transform them into actionable information to predict various ESP failure modes at different prediction periods. Several ML models are tested and evaluated using precision, recall, and F1-score performance measures. The K-Nearest Neighbor (KNN) model outperforms the other algorithms in forecasting ESP failures. Some other tested models are Random Forest (RF), Decision Tree (DT), Multilayer Perceptron (MLP) Neural Network, etc. The findings of these ML models reveal that as the prediction period extends beyond three days, it becomes more challenging to predict the true failures. Furthermore, all tested designs show similarly good performances in predicting ESP specific failures. The design that integrates the impacts of gas presence and pump efficiency while minimizing the number of input variables is suggested for general use. Based on the field data, a Weibull model is built to estimate the probability of failure. The mean time between failure (MTBF) values are utilized as inputs to the Weibull analysis. The Weibull shape and scale parameters are estimated using Median Rank Regression. Then the Weibull Probability plots are generated with high R2 values (86.5-99.4%) and a low p-value for all wells. The results show increases in pump unreliability with time for all the wells. By integrating the outcomes of the ESP Failure prediction ML model with the Weibull unreliability model, a powerful tool is provided. This tool allows the engineers to detect failures early, diagnose potential causes, and propose preventive actions. It is crucial in aiding the operators in transitioning from reactive to proactive and predictive maintenance of artificial lift operations

AI-driven Maintenance Support for Downhole Tools and Electronics Operated in Dynamic Drilling Environments

Downhole tools are complex electro-mechanical systems that perform critical functions in drilling operations. The electronics within these systems provide vital support, such as control, navigation and front-end data analysis from sensors. Due to the extremely challenging operating conditions, namely high pressure, temperature and vibrational forces, electronics can be subjected to complex failure modes and incur operational downtime. A novel Artificial Intelligence (AI)-driven Condition Based Maintenance (CBM) support system is presented, combining Bottom Hole Assembly (BHA) data with Big Data Analytics (BDA). The key objective of this system is to reduce maintenance costs along with an overall improvement of fleet reliability. As evidenced within the literature review, the application of AI methods to downhole tool maintenance is underrepresented in terms of oil and gas application. We review the BHA electronics failure modes and propose a methodology for BHA-Printed Component Board Assemblies (PCBA) CBM. We compare the results of a Random Forest Classifier (RFC) and a XGBoost Classifier trained on BHA electronics memory data cumulated during 208 missions over a 6 months period, achieving an accuracy of 90 % for predicting PCBA failure. These results are extended into a commercial analysis examining various scenarios of infield failure costs and fleet reliability levels. The findings of this paper demonstrate the value of the BHA-PCBA CBM framework by providing accurate prognosis of operational equipment health leading to reduced costs, minimised Non-Productive Time (NPT) and increased operational reliability

Energy saving by reducing motor rating of Sucker-Rod Pump systems Energy

Acknowledgments The research in this paper is supported and funded by programs with China Scholarship Council (Grant 201708130108), and National Natural Science Foundation of China (Grant 51974276).Peer reviewedPostprin