Condition-based maintenance for multi-component systems with degradation interactions in oil and gas processing plants


Price: 2000 Naira (BSC, MSC)




1.1 Background of the Study

An oil production plant is a facility which processes production fluids from oil wells in order to separate out key components and prepare them for export. This is distinct from an oil depot, which does not have processing facilities.

Typical oil well production fluids are a mixture of oil, gas and produced water. Many permanent offshore installations have full oil production facilities (Ken Arnold and Maurice Stewart, 1998). Smaller platforms and subsea wells export production fluids to the nearest production facility, which may be on a nearby offshore processing installation or an onshore terminal. The produced oil may sometimes be stabilised (a form of distillation) which reduces vapour pressure and sweetens “sour” crude oil by removing hydrogen sulphide, thereby making the crude oil suitable for storage and transport.

Emerging challenges in the oil and gas industry are proving to be a catalyst for change in the management offacilities, new and old, worldwide. The growth in demand for crude oil and natural gas is unlikely to show any significant signs of slowing over the next decade or more, but the reality is that companies are faced with greaterextraction costs due to: (1) the increasing scarcity of conventional crude oil and natural gas reserves, and (2) the difficulties associated with the extraction and/or processing of unconventional reserves found in oil sands, coal seam gas (CSG), shale gas (Yeo, B., 2010), underground coal gas (UCG) (Businoska, A., 2010), and reserves located in the depths of the oceans.

Offshore drilling is the leading extraction method for oil and gas which are the main resources to fulfill global energy demand. Since year 2000, contribution of offshore production to global oil and gas production has been 30% and 27% respectively (E. Lange, 2014). Since 1950, the average water depth of offshore oil and gas platforms increased from 650 ft to 13000 ft, causing logistic and technical challenges in operating these platforms. In practical, plant components are forced to work in harsh environment conditions which accelerate the component degradation and increase the failure probabilities. Such challenges not only increase the economic burden in terms of capital investment and operational costs but also increase the associated risk and the complexity of decision making for routine operations such as those performed to keep equipment, machinery, and supporting utilities in operable condition and to prevent breakdowns, or to restore the plant in operating condition in case of failures. Such operations are called maintenance and can be broadly classified into two categories – preventive maintenance and corrective maintenance. Preventive maintenance can be further classified into two categories, condition-based predictive maintenance and periodic-based predictive maintenance. Achieving a high plant availability requires a well-designed maintenance process. An ill-planned maintenance strategy can lead to frequent outages or breakdowns and can have a severe impact on the plant profitability. Periodic-based predictive maintenance is widely used for industrial systems characterized by high levels of risk and complexity like oil and gas plants. Although periodic-base predictive maintenance can be effective in decreasing the risk of unexpected failure, it leads to increase the level of unnecessarily maintenance actions and interventions and in (P. Baraldi and E. Patelli, 2006), it is shown that moving from corrective maintenance policy to periodic-based predictive maintenance policy leads to a reduction of average plant production of oil and gas. Decrease the probabilities of unexpected failure, decrease level of the risk, decrease the unnecessarily maintenance actions and enhance components availability and system performance are some of the reasons to move from corrective maintenance and periodic maintenance to condition-based predictive maintenance.

It should be noticed that, although, preventive maintenance helps prevent multiple failures, it might lead to increased unavailability due to frequent interventions, if not properly planned.

Condition monitoring is now increasingly employed to supplement maintenance decisions, minimize system downtimes and maximize the process efficiency.

The main advantages from moving to preventive maintenance strategy can be defined as:

 Reduce number of failures (but number of organized stops for maintenance will increase)

 Decreasing the repair time which lead to decrease unavailability of the component

 Lower maintenance cost

 Less spare parts necessary

 With decreasing unavailability of the component, availability of the system will increase and the average useful life time

 And with reducing number of failure, level of safety of the system will be increased

Offshore platforms, particularly those in harsh environmental conditions or in remote locations, can lead to an increased difficulty in maintenance operations due to transportation challenges. A corrective maintenance strategy in such plants can have a severe economic impact. As offshore plants have little spare inventories, a breakdown can lead to large costs incurred not only in bringing in new parts but also in terms of the time that the platform remains out of service. A preventive maintenance strategy coupled with condition monitoring becomes even more impactful in such cases (M. Shafiee, 2015).

The effect of condition-based predictive maintenance on component availability were studied in (C. Bérenguer, 2003) and (H. Liao, 2006). Although, (C. Bérenguer, 2003) and (H. Liao, 2006) show that condition-based predictive maintenance is able to increase system availability and decrease system down-time. in bothC. Bérenguer, 2003) and (H. Liao, 2006), it was assumed that the condition of the system, degradation level, at time 𝑡 can be summarized by a scaler aging variable 𝑋𝑡 which increases as the system deteriorate. From a practical point of view, the previous assumption can’t be applied easily as the degradation state of the component can be more complicated to be represented in a single number depending on a single signal from one of the sensors installed on it. As an example, for a gas turbine, detecting an abnormal condition related to increase of exhaust gases temperature isn’t enough to perform a diagnosis or detect the degradation level as it could be because of failure of fuel control valve or a blow-off valve on the compression stage. A Prognostics and Health Management System (PHMS) is needed to aggregate and process all the signals from all the sensors installed on the component to perform a component diagnosis and prognosis. Degradation level can be expressed using PHMS output which is residual useful life (RUL). A model able to describe the effect of equipping the component with PHMS on its availability is introduced in (M. Compare, 2017). Although the model in M. Compare, 2017 was able to evaluate the effect of equipping the component with a PHMS considering a specific maintenance rule, it is not applicable in general cases wherein predictive maintenance is performed based on degradation level, or state, of the component.

The first novelty in this work is modifying the model in M. Compare, 2017 and develop a model able to evaluate the effect of equipping the component with a PHMS considering a general maintenance policy based on degradation level of the component which can be represented or described using the PHMS outputs.

Allocating scarce resources and investments creates a need to prioritize the components within the system with respect to the benefits resulting from improvement activities and moving to condition-based predictive maintenance is one of those activities. Because of that, there is need for a metrics able to measure the importance of the components within the system. These metrics are called importance measures and there are five classical importance measures, Birnbaum importance, criticality importance, Reliability or Risk Achievement worth (RAW), Reliability Reduction Worth (RRW) and Fussell-Vesely importance (E. Zio and L. Podofillini, 2003 and J. F. Espiritu, 2007). Classical importance measures apply to systems made up of binary components and characterized also by binary states. This hypnosis does not fit of the real functioning of many systems. Modification on classical importance measures have been made in (E. Zio and L. Podofillini, 2003) and (J. F. Espiritu, 2007) allowed them to be used for multi-state components within multi-state system. The previous modifications cannot be used to quantify the impact of equipping the component with a PHMS as they made to quantify the impact of component reliability improvement on the overall system performance while installing a PHMS improve mainly maintenance operation performance to decrease component downtime.

Further modification of classical importance measures is required and, in this thesis work, risk achievement worth (RAW) is chosen to be used as it quantifies the impact of improvement activities on the component.

The second novelty of this thesis work is modifying RAW importance measure to quantify the impact of installing PHMS on a multi-state component within a multi-state system.

At the end, an application to an oil and gas offshore installation consists of 6 components and characterized by 7 production levels is proposed. Benefits of PHMS installation for each component is quantified. By applying the modified RAW importance measure, prioritize the component has been made to identify the component which is more convenient to install a PHMS.

1.2 Statement of Problem

In this thesis work, we considered an industrial plant made by n components. Each component 𝑖 can be in 𝑁𝑖 different states which corresponding to different level of degradation and different production level. The overall industrial plant can provide 𝑁 different levels of production that result from the different possible configuration of the individual component states.

1.3 Objective of the Study

1- Prioritize the components according to the expected benefits in term of overall plant performance of equipping them with a PHMS.

2- Quantify the plant performance improvement when a component is equipped with a PHMS.

In order to answer the previous questions, we assumed to have available:

a) A stochastic multi-state model of the individual component degradation and failure process;

b) A stochastic model of the duration of the maintenance interventions which take into account the component degradation state;

c) Information on the performance of the PHMS;

d) A model which associates to the different plant component configurations the corresponding plant production state;

With respect to a), given the complexity of the components used in complex industrial plants which typically renders unfeasible the use of physic-based models (M. Compare, 2016).we consider a multi-state degradation model based on the discretization of the degradation process in three or more states characterized by different values of suitable degradation indicators or different performance level or different symptoms. The main advantage of this approach over the binary model which only considers the two states of the component “operating” and “failing” is that it provides a more accurate description of the sequential component degradation phases. Transition time from one state to another are assumed to be distributed according to exponentially probability distribution.

With respect to d), we consider a stochastic model which integrates at the plant level the individual component degradation and failure process modeled in a) taking into account the component process capacities, the functional and operation dependencies and the effects of component availability on plant production. The model will be evaluated by Monte Carlo simulation to provide the expected production of the plant. (P. Baraldi and E. Patelli, 2006).

With respect to b), we consider a stochastic model which represent the duration of the maintenance interventions as a random variable distributed according to a probability distribution whose repair rate is a function of the degradation state of the component. The model is based on the assumptions of H. Liao, 2006 and C. Bérenguer, 2003.

With respect to c), PHM performance metric introduced in M. Compare, 2017 are considered and it is assumed that the PHMS developer provides their values.

1.4 Scope and Limitation of the Study

The scope of this study is centered on the Condition-based maintenance for multi-component systems with degradation interactions in oil and gas processing plants.

Theresearchworkislimitedtooil and gas plants.Themainconstraintoftheresearchisdividedintothefollowing parts.

  1. Timeconstraints-duetotheshorttimegivenforthestudy,the researchercouldnotgetalltherequiredinformationneededforthe study.
  2. Finance-asaresultofmoneyconstrainttheresearcherhadnot enoughmoneytocarryoutthestudybeyondthelevel.The researchercouldnotvisitplaceswherenecessaryinformation relevantto thestudycouldbeobtained.

1.5 Definition of Terms

Maintenance: The technical meaning of maintenance involves functional checks, servicing, repairing or replacing of necessary devices, equipment, machinery, building infrastructure, and supporting utilities in industrial, business, governmental, and residential installations.

Condition Based Maintenance:  is a maintenance strategy that monitors the actual condition of an asset to decide what maintenance needs to be done. CBM dictates that maintenance should only be performed when certain indicators show signs of decreasing performance or upcoming failure.

Oil Production Plant: An oil production plant is a facility which processes production fluids from oil wells in order to separate out key components and prepare them for export. This is distinct from an oil depot, which does not have processing facilities. Typical oil well production fluids are a mixture of oil, gas and produced water.

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