Dynamic safety analysis of decommissioning and abandonment of offshore oil and gas installations

The global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical Bayesian Analysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation.The global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical Bayesian Analysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation

Safety analysis of offshore decommissioning operation through Bayesian network

Decommissioning of offshore platforms is becoming increasingly popular. The removal of these heavy steel structures is characterised by high risks that may compromise personnel safety and loss of assets. The removal operation relies on dedicated barges and heavy lift vessels that may descent or capsize because of mechanical or structural failure. The knowledge of associated hazards is driven by experience and failure data are often obtained empirically through analogous operations, which further introduces uncertainty to the risk analysis. This paper proposes an integrated safety analysis approach for conducting a decommissioning risk analysis of offshore installations. The approach incorporates hierarchical Bayesian analysis (HBA) with Bayesian network (BN) to assess the accident causations leading to futile decommissioning operation. First, the overall system failure of a lifting vessel was reviewed with an emphasis on where safety issues arise. In addition, the failure data obtained from expert judgements were aggregated through statistical distribution based on HBA. The aggregated failure data are then used to conduct dynamic safety analysis using BN, to assess and evaluate the risks of offshore jacket removal operations. The accident model is illustrated with a case study from Brent Alpha decommissioning technical document to demonstrate the capability of incorporating HBA with BN to conduct a risk analysis

Safety analysis of plugging and abandonment of oil and gas wells in uncertain conditions with limited data

Well plugging and abandonment are necessitated to ensure safe closure of a non-producing offshore asset. Little or no condition monitoring is done after the abandonment operation, and data are often unavailable to analyze the risks of potential leakage. It is therefore essential to capture all inherent and evolving hazards associated with this activity before its implementation. The current probabilistic risk analysis approaches such as fault tree, event tree and bowtie though able to model potential leak scenarios; these approaches have limited capabilities to handle evolving well conditions and data unavailability. Many of the barriers of an abandoned well deteriorates over time and are dependent on external conditions, making it necessary to consider advanced approaches to model potential leakage risk. This paper presents a Bayesian network-based model for well plugging and abandonment. The proposed model able to handle evolving conditions of the barriers, their failure dependence and, also uncertainty in the data. The model uses advanced logic conditions such as Noisy-OR and leaky Noisy-OR to define the condition and data dependency. The proposed model is explained and tested on a case study from the Elgin platform's well plugging and abandonment failure

Hierarchical Bayesian model for failure analysis of offshore wells during decommissioning and abandonment processes

Risk analysis of offshore wells decommissioning, and abandonment processes is challenging due to limited life-cycle information of the well, and failure data of safety barriers in place. To this end, it is essential to capture and implement the variability associated with the sparse data for conducting risk analysis with considerable confidence level. The hierarchical Bayesian analysis provides a viable alternative to address the uncertainty of the data through aggregation for each causation. Bayesian network, through its robust computation engine, is used to define dependence of causations and uses Bayes' theorem to update the analysis as new information becomes available. In addition, the Bayesian network helps to represent complex dependencies among causations through appropriate relaxation strategy to minimize uncertainty in the data, link parameter of interest, and overall accident scenario modelling. This paper presents the integration of Hierarchical Bayesian model with a Bayesian network to conduct the risk analysis of well decommissioning and abandonment processes. The proposed methodology is illustrated using a well plugging and abandonment operational failure reported by the Department of Mineral Management Service (MMS). The results demonstrate the potential of the proposed approach as a robust means to study complex well decommissioning activities

Marine accident learning with fuzzy cognitive maps (MALFCMs) and Bayesian networks

Addressing safety is considered a priority starting from the design stage of any vessel until end-of-life. However, despite all safety measures developed, accidents are still occurring. This is a consequence of the complex nature of shipping accidents where too many factors are involved including human factors. Therefore, there is a need for a practical method, which can identify the importance weightings for each contributing factor involved in accidents. As a result, by identifying the importance weightings for each factor, risk assessments can be informed, and risk control options can be developed and implemented more effectively. To this end, Marine Accident Learning with Fuzzy Cognitive Maps (MALFCM) approach incorporated with Bayesian networks (BNs) is suggested and applied in this study. The MALFCM approach is based on the concept and principles of fuzzy cognitive maps (FCMs) to represent the interrelations amongst accident contributor factors. Thus, MALFCM allows identifying the importance weightings for each factor involved in an accident, which can serve as prior failure probabilities within BNs. Hence, in this study, a specific accident will be investigated with the proposed MALFCM approach

Cyclic stress strain behavior of high strength steel

High strength steels are generally known to possess excellent bendability, weld ability as well as cutting properties but trade off press-formability. They offer better strength-to-weight ratio due to their relatively low-carbon and manganese content. Optim 700MC is a thermo-mechanically rolled (M), cold formable (C) steel which has continue to find application in the frame structures of mobile vehicles, surface structures of commercial vehicles and also in the material handling industry. The aim of this thesis is to investigate by means of laboratory test, the cyclic stress strain behaviour of the specimen in order to investigate whether the material hardens or softens. This has been done by conducting a low-cycle-fatigue (LCF) test at a stress ratio, R = -1 on the test specimen. The specimen consists of a steel grade, As-Received (AR) and HFMI-1mm, OPTIM 700MC. The behaviour was then analysed and a model was fit for the stress amplitude vs. cycle curve, using the 5% stress drop failure criterion. The study concentrated upon the correlation between the material model constant and the plastic strain amplitude. The relationship was found to be linear and the tested materials both undergo cyclic work softening with the plastic strain range of the thermo mechanically treated specimen being larger than the as-received. It was observed that the yield strength in the HFMI-1mm specimen is much larger than the AR and there are three distinctive phases in the softening trend for both specimens. Therefore, the material model constant is responsible for the direction of propagation of the yield surface center between the stress and plastic strain rates

Design and Thermodynamic Analysis of Solar Updraft Tower

Solar updraft towers is a robust green energy plant that produce electricity based on a relatively simple but proven method of harnessing the energy of the sun to an air collector, which acts like a greenhouse heat exchanger. It is a solar thermal power plant consisting of an air collector, a central updraft tower for generating solar induced convective flow and a turbine unit driven by the heat transfer fluid, HTF entrapped beneath the greenhouse, to produce energy in the form of electricity. This paper presents the thermodynamic analysis of an educational prototype, analyze the fluid flow across the tower both in the ideal and real case, radiation optimization of the greenhouse heat exchanger and evaluate the possible humidification effect around the heating zone. First, the thermo-fluid theory of the wind turbine and solar tower is described. Then, results from the modeled design, simulation using CFD and characteristic curves are presented. Also, the result of humidifying air on the performance of the turbine was analyzed. Consequently, Recommendations for future and on-going solar tower projects are offered.Aufwindkraftwerke ist eine robuste grüne Energie Anlage, die Strom produzieren basierend auf einem relativ einfachen, aber bewährte Methode zur Nutzung der Energie der Sonne zu einem Luft-Kollektor, der wie ein Gewächshaus Wärmetauscher wirkt. Es ist eine solarthermische Anlage, bestehend aus einem Luftkollektor, eine zentrale Aufwindkraftwerk zur Erzeugung von Solar-induzierte Konvektionsströmung und eine Turbine Einheit durch den Wärmeträger getrieben, gefangen HTF unter dem Gewächshaus, um Energie in Form von Strom zu produzieren. Dieses Papier stellt die thermodynamische Analyse eines pädagogischen Prototypen, analysieren die Strömung über den Turm sowohl in der idealen und realen Fall Strahlung Optimierung der Treibhausgas-Wärmetauscher und bewerten die möglichen Befeuchtung Effekt um die Heizzone. Zunächst wird die thermo-fluid Theorie der Windturbine und Solarturm beschrieben. Dann ergibt sich aus dem Modell-Design, sind Simulation mit CFD und Kennlinien vorgestellt. Darüber hinaus wurde das Ergebnis der Luftbefeuchtung auf die Leistung der Turbine analysiert. Daher sind Empfehlungen für die künftige und laufende Solarturm-Projekte angeboten.Solar väärinpäin tornit on vankka vihreä energia kasvi, joka tuottaa sähköä perustuu suhteellisen yksinkertainen mutta luotettava menetelmä valjastaa auringon energia on ilmaa keräilijä, joka toimii kuin kasvihuone lämmönvaihdin. Se on aurinkoenergia voimalaitos koostuu ilma keräilijä, Keski väärinpäin torni tuottamisesta auringon aiheuttama konvektiivisia virtaus ja turbiinin yksikkö ohjaa lämmönkeruunesteen, HTF ansaan alla kasvihuone, tuottaa energiaa sähkönä. Tässä kirjassa esitellään termodynaaminen analyysi koulutuksen prototyyppi, analysoida nestevirtausta koko tornin niin ihanteellinen ja todellinen tapaus, säteilyn optimointi kasvihuonekaasujen lämmönvaihtimen ja arvioimaan mahdollisia kostutus ympärille Lämmitysryhmien. Ensinnäkin Thermo-neste teoria tuulivoimala ja aurinko torni on kuvattu. Sitten tulokset mallinnettu suunnittelu, simulointi käyttäen CFD ja ominaiskäyrät on esitetty. Myös tulos kostutukseen ilman suorituskyvystä turbiinin analysoitiin. Näin ollen Suosituksia tulevia ja käynnissä aurinko torni Hankkeita on tarjolla

Dynamic Risk Assessment of Decommissioning Offshore Jacket Structures

The need to develop an integrated dynamic safety and risk analysis model for decommissioning offshore jacket structures is driven by the risky, expensive and complex nature of the operation. Many of the existing risk analysis techniques applicable to offshore assets failed to recognise and capture evolving risks during different stages of the decommissioning operation. This paper describes risk-based safety model to conduct quantitative risk analysis for offshore jacket decommissioning failure. First, a bow-tie technique is developed to model the accident cause-consequence relationship. Subsequently, a Bayesian belief network is used to update the failure probabilities of the contributing elements and thus, provides a more case-specific and realistic safety analysis when compared to the static nature of a bow-tie. This paper also presents the application of experiential learning in the dynamic safety analysis. The proposed technique is tested using a real-life case study from the Shell Brent Alpha platform. An algorithm to limit the effect of generic failure data was also developed. It is observed that the proposed technique helps to identify hazards shortly before they occur and sensitivity analysis revealed the most critical elements of the operation that must be managed to prevent catastrophe and consequently, reduce associated costs of remediation

Marine accident learning with fuzzy cognitive maps (MALFCMs) and Bayesian networks : a case study on maritime accidents

Aiming to improve maritime safety, there is a need for a practical method that is capable of identifying the importance weightings for each contributing factor involved in accidents. Hence, Marine Accident Learning with Fuzzy Cognitive Maps (MALFCM) incorporated with Bayesian networks is suggested and applied in this study. MALFCM approach is based on the concept and principles of Fuzzy Cognitive Maps (FCMs) to represent the interrelations amongst accident contributor factors. Hence, in this study, grounding/stranding accidents were investigated with the proposed MALFCM approach. As a result, inadequate leadership and supervision, lack of training and unprofessional behavior were identified as the most probable causes of grounding accident. In addition, in the accident scenario analysis, it was observed that the lack of safety culture contributed most to the system failure based on the posterior to prior failures ratio