Noise modelling, vibro-acoustic analysis, artificial neural networks on offshore platform

PhD ThesisDue to the limitations of the present noise prediction methods used in the offshore industry, this research is aimed to develop an efficient noise prediction technique that can analyze and predict the noise level for the offshore platform environment during the design stage as practically as possible to meet the criteria for crews’ comfort against high noise level. Several studies have been carried out to improve the understanding of acoustic environment onboard offshore platform, as well as the present prediction techniques. The noise prediction methods for the offshore platform were proposed from three aspects: by empirical acoustic modeling, analytical computation or neural network method. First, through evaluating the five-selected empirical acoustic models originated from other applications and statistical energy analaysis with direct field (SEA-DF), Heerema and Hodgson model was selected for calculating the sound level in the machinery room on the offshore platform. Second, the analytical model modeled three-dimensional fully coupled structural and acoustic systems by considering of the structural coupling force and the moment at edges, and structural-acoustic interaction on the interface. Artificial spring technique was implemented to illustrate the general coupling and boundary conditions. The use of Chebyshev expansions solutions ensured the accuracy and rapid convergence of the three-dimensional problem of single room and conjugate rooms. The proposed model was validated by checking natural frequencies and responses of against the results obtained from finite element software. Third, a modified multiple generalised regression neural network (GRNN) was first proposed to predict the noise level of various compartments onboard of the offshore platform with limited samples available. By preprocessing the samples with fuzzy c-means (FCM) and principal component analysis (PCA), dominant input features can be identified before commencing the GRNN’s training process. With optimal spread variables, the newly developed tool showed comparable performance to the SEA-DF and empirical formula that requires less time and resources to solve during the early stage of the offshore platform design.Singapore Economic Development Board (EDB) for providing the funding for the research under EDB-Industrial Postgraduate Programme (IPP) with SembCorp Marine in Singapore

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring

Automated anomaly recognition in real time data streams for oil and gas industry.

There is a growing demand for computer-assisted real-time anomaly detection - from the identification of suspicious activities in cyber security, to the monitoring of engineering data for various applications across the oil and gas, automotive and other engineering industries. To reduce the reliance on field experts' knowledge for identification of these anomalies, this thesis proposes a deep-learning anomaly-detection framework that can help to create an effective real-time condition-monitoring framework. The aim of this research is to develop a real-time and re-trainable generic anomaly-detection framework, which is capable of predicting and identifying anomalies with a high level of accuracy - even when a specific anomalous event has no precedent. Machine-based condition monitoring is preferable in many practical situations where fast data analysis is required, and where there are harsh climates or otherwise life-threatening environments. For example, automated conditional monitoring systems are ideal in deep sea exploration studies, offshore installations and space exploration. This thesis firstly reviews studies about anomaly detection using machine learning. It then adopts the best practices from those studies in order to propose a multi-tiered framework for anomaly detection with heterogeneous input sources, which can deal with unseen anomalies in a real-time dynamic problem environment. The thesis then applies the developed generic multi-tiered framework to two fields of engineering: data analysis and malicious cyber attack detection. Finally, the framework is further refined based on the outcomes of those case studies and is used to develop a secure cross-platform API, capable of re-training and data classification on a real-time data feed

Plantwide simulation and monitoring of offshore oil and gas production facility

Monitoring is one of the major concerns in offshore oil and gas production platform since the access to the offshore facilities is difficult. Also, it is quite challenging to extract oil and gas safely in such a harsh environment, and any abnormalities may lead to a catastrophic event. The process data, including all possible faulty scenarios, is required to build an appropriate monitoring system. Since the plant wide process data is not available in the literature, a dynamic model and simulation of an offshore oil and gas production platform is developed by using Aspen HYSYS. Modeling and simulations are handy tools for designing and predicting the accurate behavior of a production plant. The model was built based on the gas processing plant at the North Sea platform reported in Voldsund et al. (2013). Several common faults from different fault categories were simulated in the dynamic system, and their impacts on the overall hydrocarbon production were analyzed. The simulated data are then used to build a monitoring system for each of the faulty states. A new monitoring method has been proposed by combining Principal Component Analysis (PCA) and Dynamic PCA (DPCA) with Artificial Neural Network (ANN). The application of ANN to process systems is quite difficult as it involves a very large number of input neurons to model the system. Training of such large scale network is time-consuming and provides poor accuracy with a high error rate. In PCA-ANN and DPCA-ANN monitoring system, PCA and DPCA are used to reduce the dimension of the training data set and extract the main features of measured variables. Subsequently ANN uses this lower-dimensional score vectors to build a training model and classify the abnormalities. It is found that the proposed approach reduces the time to train ANN and successfully diagnose, detects and classifies the faults with a high accuracy rate

Uncertainty reduction in reservoir parameters prediction from multiscale data using machine learning in deep offshore reservoirs.

Developing a complete characterization of reservoir properties involved in subsurface multiphase flow is a very challenging task. In most cases, these properties - such as porosity, water saturation, permeability (and their variants), pressure, wettability, bulk modulus, Young modulus, shear modulus, fracture gradient - cannot be directly measured and, if measured, are available only at small number of well locations. The limited data are then combined with geological interpretation to generate a model. Also increasing the degree of this uncertainty is the fact that the reservoir properties from different data sources - like well logs, cores and well test - often produce different results, thus making predictions less accurate. The present study focussed on three reservoir parameters: porosity, fluid saturation and permeability. These were selected based on literature and sensitivity analysis, using Monte Carlo simulations on net present value, reserve estimates and pressure transients. Sandstone assets from the North Sea were used to establish the technique for uncertainty reduction, using machine learning as well as empirical models after data digitization and cleaning. These models were built (trained) with observed data using other variables as inputs, after which they were tested by then using the input variables (not used for the training) to predict their corresponding observed data. Root Mean Squared Error (RMSE) of the predicted and the actual observed data was calculated. Model tuning was done in order to optimize its key parameters to reduce RMSE. Appropriate log, core and test depth matching was also ensured including upscaling combined with Lorenz plot to identify the dominant flow interval. Nomographic approach involving a numerial simulation run iteratively on multiple non-linear regression model obtained from the dataset was also run. Sandstone reservoirs from the North Sea not used for developing the models were then used to validate the different techniques developed earlier. Based on the above, the degree of uncertainty associated with porosity, permeability and fluid saturation usage was demonstrated and reduced. For example, improved accuracies of 1-74%, 4-77% and 40% were achieved for Raymer, Wyllie and Modified Schlumberger, respectively. Raymer and Wyllie were also not suitable for unconsolidated sandstones while machine learning models were the most accurate. Evaluation of logs, core and test from several wells showed permeability to be different across the board, which also highlights the uncertainty in their interpretation. The gap between log, core and test was also closed using machine learning and nomographic methods. The machine learning model was then coded into a dashboard containing the inputs for its training. Their relationship provides the benchmark to calibrate one against the other, and also to create the platform for real-time reservoir properties prediction. The technology was applied to an independent dataset from the Central North Sea deep offshore sandstone reservoir for the validation of these models, with minimum tuning and thus effective for real-time reservoir and production management. While uncertainties in measurements are crucial, the focus of this work was on the intermediate models to get better final geological models, since the measured data were from the industry

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

Modelling oil and gas flow rate through chokes: A critical review of extant models

Oil and gas metering is primarily used as the basis for evaluating the economic viability of oil wells. Owing to the economic implications of oil and gas metering, the subject of oil and gas flow rate measurement has witnessed a sustained interest by the oil and gas community and the academia. To the best of the authors’ knowledge, despite the growing number of published articles on this subject, there is yet no comprehensive critical review on it. The objective of this paper is to provide a broad overview of models and modelling techniques applied to the estimation of oil and gas flow rate through chokes while also critically evaluating them. For the sake of simplicity and ease of reference, the outcomes of the review are presented in tables in an integrated and concise manner. The articles for this review were extracted from many subject areas. For the theoretical pieces related to oil and gas flow rate in general, the authors relied heavily upon several key drilling fluid texts. For operational and field studies, the authors relied on conference proceedings from the society of petroleum engineers. These sources were supplemented with articles in peer reviewed journals in order to contextualize the subject in terms of current practices. This review is interspersed with critiques of the models while the areas requiring improvement were also outlined. Findings from the bibliometric analysis indicate that there is no universal model for all flow situations despite the huge efforts in this direction. Furthermore, a broad survey of literature on recent flow models reveals that researchers are gravitating towards the field of artificial intelligence due to the tremendous promises it offers. This review constitutes the first critical compilation on a broad range of models applied to predicting oil and gas flow rates through chokes