Reliability-Centered Maintenance and Mechanical Integrity Management for Aging Onshore Oil and Gas Production Facilities

1Introduction

A large share of onshore oil and gas production capacity in the United States and elsewhere is delivered by facilities that were built decades ago and that now operate beyond the design life assumed when their equipment was specified. Separators, heater-treaters, produced-water systems, flowlines, storage tanks, compressors and pumps on these sites have been exposed for many years to produced fluids that contain carbon dioxide, hydrogen sulfide, water and solids, and their condition reflects that history [1–3]. Regulators and researchers have documented that ageing is not a matter of calendar age alone but of the accumulated degradation, obsolescence and loss of knowledge that affect equipment containing hazardous fluids [1, 2, 4, 5]. Pipeline and gathering system failure statistics show that corrosion remains a leading cause of loss of containment in oil and gas service [6–8], and storage tank accident reviews show that the consequences of integrity failures in ageing storage can be severe [9–11]. Fired heaters, boilers and furnaces in upstream service add a further class of ageing equipment whose reliability and integrity must be managed together [12], and risk-based process engineering has been proposed as a way of strengthening reliability across complex natural gas infrastructure [13]. The global cost of corrosion has been estimated at several percent of gross domestic product, much of it avoidable through better management [14].

Maintenance programmes on these facilities are often inherited from the original operator or the original equipment manufacturer, with fixed calendar intervals for overhauls, inspections and proof tests that were set when the equipment was new [15–17]. Reliability-centered maintenance (RCM) offers a structured alternative: each function and failure mode is analysed, its consequences are classified, and a task is selected only if it is technically feasible and worth doing [18–20]. RCM has been applied successfully to rotating equipment and reciprocating compressors [21, 22], and its integration with reliability, availability and maintainability analysis has been demonstrated [23]. Mechanical integrity programmes for static equipment, built on risk-based inspection (RBI) and fitness-for-service assessment, have developed in parallel through industry standards and research [24–27].

These two traditions are rarely integrated, and neither is routinely adapted to ageing. RCM studies typically assume that failure-time distributions are stationary and that the optimal task interval, once found, can be applied for the remaining life [28, 29]. Mechanical integrity programmes may update corrosion rates at each inspection, but inspection intervals are often set by maximum code intervals rather than by risk [24, 26, 30]. Proof-test intervals for safety instrumented functions are usually fixed at the design stage, even though failure rates of valves and switches exposed to produced fluids increase with age [3, 4]. Reviews of maintenance optimization have noted that the gap between models and practice persists, partly because of the data that models require and partly because models address single failure modes rather than the facility as a whole [31–33]. Studies of maintenance work-order data show that the records needed to estimate failure models are frequently incomplete or miscoded [34–36].

This paper addresses these gaps by developing and demonstrating an integrated RCM and mechanical integrity framework for ageing onshore production facilities. It makes four contributions. First, it combines RCM task selection for active failure modes, using Weibull renewal and delay-time models with a risk constraint for safety and environmental consequences, with RBI for static equipment and age-dependent failure-finding intervals for hidden failures, in a single facility-level evaluation [18, 24, 37]. Second, it compares a legacy calendar programme, an RCM programme with static intervals and an integrated age-adaptive programme over a 15-year life-extension horizon, quantifying cost, loss of containment, failures on demand, downtime and safety integrity level (SIL) compliance. Third, it tests how the ranking of the strategies changes as corrosion accelerates with rising water cut, a characteristic of mature fields [38, 39]. Fourth, it quantifies the cost of poor maintenance data by estimating failure models from small and misrecorded samples [40, 41]. The paper then reviews the relevant literature and presents the methods, results, discussion and conclusions.

2Literature review

This section reviews the six bodies of literature on which the framework draws: the RCM methodology and its applications; maintenance strategy selection and criticality analysis; maintenance performance measurement, total productive maintenance and maintenance data quality; ageing, corrosion and damage mechanisms in oil and gas equipment; risk-based inspection, fitness-for-service and remaining strength assessment; and mathematical models for maintenance and inspection optimization. The review emphasizes the elements that the framework integrates and the gaps that motivate it, following the view that maintenance management is a cycle of strategy, planning, execution and assessment that must be supported by suitable models and data [42–44]. It also reflects the terminology of the European maintenance standards, which define failure modes, maintenance types and performance indicators in a consistent way [45–47].

2.1Reliability-centered maintenance

RCM originated in civil aviation and was later codified in evaluation criteria that require any RCM process to identify functions, functional failures, failure modes, failure effects and consequences, and to select tasks that are applicable and effective [18]. Reviews of maintenance systems describe RCM alongside total productive maintenance and condition-based maintenance as the principal frameworks for maintenance planning [19, 48, 49], and a SWOT analysis of RCM identified its strengths in systematic logic and its weaknesses in data requirements and implementation effort [20]. Applications in oil and gas include an improved RCM method for reciprocating compressor units that combined the analytic hierarchy process with fuzzy comprehensive evaluation [22]. RCM has been used to determine tasks for rotating equipment [21], to establish maintenance plans by quantitative analysis [28], to manage railway infrastructure on the basis of reliability analysis [29] and to plan maintenance in ethanol plants [50]. RCM has also been combined with reliability, availability and maintainability analysis [23]. Reliability engineering and failure analysis methods for complex engineering systems supply the failure mode and failure distribution inputs on which RCM decisions rest [51], and predictive maintenance and condition monitoring practice in critical power and energy infrastructure provides the condition-based tasks that RCM logic selects for evident wear-out modes [52]. Data-driven reliability analysis of natural gas compressors has shown how operational data can feed such programmes [53]. Criticality analysis, which ranks assets before detailed RCM, has been performed with the analytic network process for identifying critical components [54].

2.2Maintenance strategy selection and criticality

Selecting the most suitable maintenance strategy for each asset has been treated extensively as a multi-criteria decision problem. Early work applied the analytic hierarchy process (AHP) to strategy selection [55] and multi-attribute approaches to the policy selection problem [56], building on the AHP method [57, 58] and its applications in operations management [59, 60]. Reviews of the strategy selection problem catalogue the methods used and their limitations [61], and a conceptual framework links strategy selection to the performance of the maintenance function [62]. Fuzzy AHP combined with grey relational analysis and TOPSIS [63], fuzzy AHP with TOPSIS [64], fuzzy analytic network process [65], hybrid multi-criteria methods [66], integer linear programming [67], multi-criterion decision models [68], goal programming with AHP and TOPSIS [69] and ELECTRE-SORT [70] have all been proposed. Applications include rolling stock [71], electric power distribution [72], multi-component systems [73], marine settings with risk evaluation [74], sustainable maintenance in manufacturing [75] and rubber industries [76], and the alignment between business and maintenance strategies [77]. Quantitative frameworks for policy development [78], decision analysis using computerized maintenance management systems [79] and reviews of time-based and condition-based maintenance [48] provide further foundations. Condition-based maintenance itself rests on diagnostics and prognostics [80], and the decision to act on risk analysis results has been examined critically [81].

Criticality analysis ranks equipment before strategy selection. Multi-criterion classification [82], fuzzy reasoning [83], empirical formulae [84], AHP-based criticality in refineries [85], power plants [86, 87], medical equipment [88], petrochemical plants [89] and process plants [90] and short-term decision support for task prioritization [91] have been reported. Failure mode and effects analysis provides the risk priority logic underlying many of these methods, and its risk evaluation approaches have been reviewed [92]; fuzzy FMEA has been applied to compressed natural gas systems [93] and fuzzy risk analysis to mechanical systems in process industries [94]. Multi-criteria classification of spare parts extends criticality to inventory decisions [95]. A risk-based process engineering framework for natural gas infrastructure applies the same ranking logic at system level, directing reliability effort to the elements whose failure most affects gas supply [13].

2.3Maintenance performance, total productive maintenance and data quality

Maintenance performance measurement provides the indicators against which RCM and mechanical integrity programmes are judged. Reviews of maintenance performance metrics [96] and performance measurement and management [97], frameworks of indicators for the maintenance function [98], terminology for value-driven maintenance [99] and the European key performance indicator standard [46] define the field. Recent work integrates sustainability into maintenance performance [100–103], proposes fuzzy sustainability assessment [104], surveys sustainable maintenance measures [105] and discusses triple-bottom-line dashboards [106]. In oil and gas, sustainability has been incorporated into the performance assessment of offshore facilities [107], maintenance management systems for upstream operations have been reviewed [16] and refinery maintenance indicators have been prioritized with fuzzy TOPSIS and AHP [108]. Studies have linked maintenance to productivity and profitability [109], examined maintenance in specific industries [110], applied lean thinking [111], described maintenance excellence [15], surveyed maintenance practices [17] and reviewed advanced maintenance services [43] and the contributions of maintenance to sustainable manufacturing [44]. Conceptual models have linked materials readiness to maintenance-driven supply chain performance [112] and inventory availability to plant uptime in energy facilities [113], showing that maintenance performance depends on the supply of spares as well as on task selection. Maintenance personnel qualification [47] and training effectiveness [114] complete the organizational picture.

Total productive maintenance and overall equipment effectiveness (OEE) provide an alternative perspective that emphasizes operator involvement and loss elimination [115]. Reviews of OEE [116, 117], practical guidance on OEE as a measure of operational improvement [118], cross-functional teams [119], line-level OEE [120], overall resource effectiveness [121] and performance measurement systems [122] describe the approach. Case studies cover TPM implementation [123], OEE assessment for improvement potential [124], workover rigs in oilfields [125], process industries [126], fuzzy OEE [127], micro downtime [128], TPM in flexible manufacturing [129], TQM and TPM [130], the five-whys technique [131] and mining equipment [132].

All of these approaches depend on maintenance data. Computerized maintenance management systems (CMMS) support decision making [133, 134], but the work orders they hold are often incomplete, inconsistently coded and written in free text [34, 40]. Methods for cleaning historical work-order data for reliability analysis [35], categorization errors in data entry [36], technical language processing [135–137], the derivation of key performance indicators from work orders [138, 139] and automated data collection to improve data quality [140] have been reported. Machine learning and data-driven methods for industrial automation and process control [141] and interpretable machine learning for early failure prediction in distributed energy assets [142] offer further ways to extract failure information, but they too depend on the quality of the underlying records. These studies show that the data needed to estimate failure models are rarely available in the volume and quality that optimization models assume.

2.4Ageing, corrosion and damage mechanisms

Ageing of process plant has been studied systematically by the UK Health and Safety Executive, which defined ageing as deterioration that affects safety and integrity and proposed management principles for equipment containing hazardous fluids or pressure [1, 2]. Life extension decision making for safety-critical systems has been reviewed [3], and condition assessment, remaining useful life prediction and selection of life extension strategies have been studied for ageing offshore assets [4, 5], together with guidelines for asset management of offshore facilities [143]. Integrated risk and asset management with RBI has been applied in oil and gas [26], and the reliability and integrity management of fired heaters, boilers and furnaces in upstream oil and gas has been reviewed [12].

Internal carbon dioxide corrosion is the dominant degradation mechanism in many production systems. Studies of this mechanism have addressed the transition between low and supercritical partial pressures [144], iron carbonate morphology [145], the effects of impurities [146, 147], pipeline steels in dense CO2 [148, 149] and oil-water emulsion flow [150]. Protection strategies against internal corrosion [151], corrosion rate prediction for carbon steel in oil and gas environments [39], localized corrosion and mitigation in oil and gas steels [152] and internal corrosion in oil pipelines [153] have been described. Microbiologically influenced corrosion has been reviewed in terms of mechanisms [154] and studied on buried pipelines [155]. Corrosion management and material selection for crude oil export equipment in marine environments have also been reviewed [156]. Gathering pipelines in mature fields show strong section-to-section differences in corrosion [38], and failures of elbows and welds in gathering systems have been analysed [157].

Erosion-corrosion arises where sand and high velocities coexist. Studies have addressed the synergy between erosion and corrosion in CO2 [158], shale gas production pipe [159], materials for the resources industry [160], oil-sand slurries [161], passive carbon steel [162], elbows [163], water injection lines [164], sand fines [165], intermittent flow [166], steels in crude oil [167], velocity effects [168] and experimental methods [169]. Because erosion-corrosion is driven by entrained solids, CFD-driven design of fluid-particle separation and filtration systems is relevant to its control upstream of the affected piping [170]. Corrosion under insulation (CUI) is a further major mechanism for ageing plant, reviewed as a critical issue for the oil and gas industry [171, 172] and addressed by guidelines and standards [173–175]. Experimental and field studies have examined CUI severity and detection [176], marine environments [177], refinery failures [178], Arabian Gulf exposure [179], dissolved metal ions [180], drain holes in jacketing [181], fuzzy prediction of CUI [182], non-destructive inspection of insulated equipment [183] and piping reliability under CUI [184].

Atmospheric storage tanks suffer floor and shell corrosion and are associated with fire and explosion accidents. Reviews of tank accidents [9, 10], fire scenarios [185], external floating roof tanks [186], recent fuel storage fires [187], oil depot accidents [188], catastrophic failures [189], the Buncefield incident [11], hazard assessment [190], fire risk management [191], fixed-roof tank explosions [192] and buckling under fire [193] show why tank integrity matters. Tank design follows API Standard 650 [194].

2.5Risk-based inspection, fitness-for-service and remaining strength

Risk-based inspection prioritizes inspection by the probability and consequence of failure and has been integrated with asset management in oil, gas and petrochemical plants [26]; inspection of pressure vessels follows API 510 [24]. An early risk-based model for inspection and maintenance of cross-country petroleum pipelines [195] was followed by a risk-based inspection concept for fitness-for-service assessment of ageing pressure vessels on offshore joint venture facilities [27], optimal inspection planning for onshore pipelines subject to external corrosion [196], probabilistic modelling of corroded pipelines at inspection time [197] and models of non-destructive testing results in RBI [198]. Bayesian approaches to the failure risk of oil and gas pipelines have been reviewed [199], and sustainability-based risk analysis of pipelines has been proposed [200]. Reviews of pipeline failure prediction models [201], failure analyses of pipelines in oil and gas [6, 202] and in-line inspection, corrosion growth rate and data management [203, 204] support inspection planning, as do comparisons of discontinuity inspection methods [205] and analyses of composite repairs [206]. Reviews of NDT-based integrity management and corrosion risk assessment for refinery process equipment [30], of NDT methods for predictive failure analysis in mechanical systems [207] and of non-intrusive inspection of pressure vessels, which reduces shutdown frequency and operating cost [208], describe the techniques that determine inspection effectiveness. Where corrosion damage is repaired by welding, NDT methods for weld integrity evaluation [209] and welding inspection standards and quality assurance practices [210] govern the acceptance of the repair. For buried lines, GIS applications in utility asset management [211] and UAV-based pipeline and corridor monitoring [212] supply the location and external condition data that inspection planning requires.

Fitness-for-service assessment under API 579-1/ASME FFS-1 [25, 213], including fatigue assessment [214], and failure assessment diagrams [215–218] determine whether a degraded component can remain in service; applications include pressure vessels with crack-like flaws [219]. Flaw assessment under BS 7910 [220], the remaining strength of corroded pipelines under ASME B31G [221, 222], DNV-RP-F101 [223], CSA Z662 [224] and best practice for pipeline defect assessment [225] provide the codified methods. A large literature evaluates burst pressure models for corroded pipelines [226–230], develops limit load solutions [231], finite element and numerical analyses [232–235], failure pressure methods [236, 237], interaction rules for multiple defects [238], models for flawless and high-strength pipes [239, 240], fracture equations [241], metal-loss criteria [242], residual strength and life prediction [243], reliability analyses [244, 245] and machine-learning predictors [246–250].

2.6Maintenance and inspection optimization models

Reviews of maintenance optimization classify models by system structure, degradation model, decision variables and criteria [31, 32, 251], and surveys of maintainability and replacement [252], multi-unit systems [253], multi-component systems [254, 255] and the chemical and process industries [33] show the breadth of the field. Classical age and periodic replacement policies are set out in maintenance theory [256], discrete replacement policies [257], random maintenance policies [258] and replacement first or last [259]. Recent work has examined continuous versus discrete scheduled times [260], two failure modes [261], age replacement versus minimal repair [262], discrete-time age replacement for phase-type lifetimes [263], coherent systems [264], dependent components [265], one-cycle criteria [266], random working cycles [267], hybrid preventive maintenance under competing failures [268] and age-based group maintenance of degrading units [269].

The delay-time concept, in which a defect becomes detectable some time before it causes failure, underpins inspection interval optimization. Its development by Christer and colleagues [270–272], reviews of delay-time operational research [37, 273], the delay time modelling framework [274], parameter estimation from deficient data [275], subjective estimation of the delay-time distribution [276] and applications in manufacturing [277] established the method. Extensions include a safety constraint [278], two types of inspections [279], three-stage failure processes [280], multi-component and multi-failure mode models [281], renewal ergodicity in block inspection [282], postponed replacement [283], two-phase inspection of preparedness systems [284], inspection for maximum future reliability [285], concrete structures [286], inspection policies for production facilities [287], fixed periodic schedules [288], fully subjective approaches [289], multi-criteria determination of inspection intervals [290] and joint optimization with spare parts [291, 292].

Multi-component systems with economic dependence allow maintenance to be grouped or performed opportunistically. Reviews of economic dependence [293], dynamic influences [294], control policies [295], generalized group maintenance [296], opportunistic replacement [297] and opportunistic maintenance [298] define the area. Dynamic grouping [299–302], condition-based multi-component maintenance [303–312], inspection and opportunistic replacement [313], non-homogeneous opportunity arrivals [314], utility systems [315], multistage manufacturing [316], variable horizons [317], dependent main and auxiliary components [318], gravity windows [319], delay-time opportunistic scheduling [320], offshore riser grouping [321], geographically dispersed assets [322], load sharing [323, 324], redundant systems [325], multi-system networks [326], manufacturing systems [327], opportunistic planning frameworks [328], component importance [329], two-component inspection with failure interaction [330–332], block replacement with failure dependence [333], genetic algorithms and Monte Carlo simulation [334, 335], human factors in grouping [336, 337] and joint maintenance and spare parts optimization [338, 339] have been studied. Supply chain studies for gas processing plants extend this link between maintenance and materials to procurement, supply chain resilience and supply chain risk management [340–342].

Failure data analysis for repairable systems connects these models to field data. Trend testing for failure-limited data [343], selection of time-to-failure models by trend analysis [344], power law processes estimated by expectation-maximization [345], unobserved heterogeneity in power law processes [346], non-homogeneous Poisson processes with frailty [347, 348], censored data [41], imperfect repair models [349–352] and past-dependent partial repairs [353] have been reported. Life-cycle cost and maintenance cost analyses include condition-based maintenance enabled by prognostics [354], importance analysis guided maintenance of repairable systems [355], cost-based importance analysis under different maintenance strategies [356], cost-benefit optimization of maintained structures by renewal models [357] and Bayesian updating of infrastructure reliability from condition data [358]. Equipment-specific studies relevant to production facilities address gas pressure-regulating installations [359], hydro-turbines [360], heat exchanger fouling [361–364], gas turbine and power machinery prognostics [365–367], centrifugal compressor degradation [368] and competing failure processes [369].

Maintenance itself can introduce failures. Human error has been incorporated into maintenance models [370], analysed in pre- and post-maintenance risk [371], integrated into condition-based maintenance optimization [372], evaluated in nuclear plants [373] and assessed by human reliability methods for refineries [374]. Integration of human factors into process hazard analysis [375] and the perceptions of maintenance personnel on petroleum production facilities [376] show that maintenance and safety are closely linked. Conceptual models for incident prevention in industrial maintenance engineering [377], for data-driven occupational safety risk control in large energy projects [378] and for balancing productivity and safety compliance in high-risk drilling [379] make the same point at the organizational level.

2.7Research gap

The literature offers mature methods for each element of maintenance and integrity management, but four gaps limit their use on ageing onshore facilities. First, RCM, RBI and proof-test optimization are usually applied separately, so their combined effect on facility cost and risk is not evaluated [23, 26, 33]. Second, optimal intervals are usually computed under stationary assumptions, whereas corrosion rates and hidden-failure rates rise with facility age and water cut [1, 3, 38]. Third, risk constraints for safety and environmental consequences are seldom imposed explicitly in interval optimization, although the delay-time literature has shown how this can be done [81, 278]. Fourth, the effect of maintenance data quality on the value of optimization is rarely quantified, even though work-order studies show that data problems are widespread [34, 36, 40]. The framework presented next addresses each of these gaps.

3Methods

This section describes the integrated framework, the representative facility, the models used for active, static and hidden failure modes, the three maintenance strategies that were compared, the data quality experiment and the computational implementation. The framework follows the RCM requirement that each failure mode be assigned a task that is applicable and effective given its consequences [18, 19], and the mechanical integrity requirement that static equipment be inspected on the basis of risk and remaining life [24, 27]. The models are deliberately standard so that the contribution lies in their integration and in the treatment of ageing rather than in new mathematics: renewal-reward and delay-time models for active failure modes [37, 256], a stochastic wall-thinning model with imperfect inspection for static equipment [196–198] and an age-dependent average probability of failure on demand for hidden failures [278]. All parameter values are representative values chosen within ranges reported in the literature cited for each equipment type; they are not field data from a specific facility, and the results should be read as a demonstration of the framework rather than as estimates for any particular site.

3.1Integrated framework

Figure 1 shows the framework. The asset register and CMMS failure records, the failure modes, effects and criticality analysis (FMECA) and an ageing assessment that covers degradation mechanisms, water cut and obsolescence feed a consequence classification of each failure mode as safety, environmental or operational, and as evident or hidden [1, 18, 92]. Active failure modes, which are evident and arise in rotating and fired equipment, are assigned to run-to-failure, scheduled restoration or condition-based tasks by renewal-reward and delay-time models [256, 270]. Static equipment, whose dominant failure mechanisms are corrosion and erosion, is managed by RBI with corrosion-rate updating and explicit inspection effectiveness [26, 203]. Hidden failures of protective devices are managed by failure-finding tasks with age-dependent intervals [3]. The three streams are integrated at facility level to give cost, loss of primary containment (LOPC) frequency, failures on demand, equipment downtime and SIL compliance across the life-extension horizon, and task execution, inspection findings and failure records return to the CMMS through a learning loop [34, 35].

3.2Representative facility

The representative facility is an onshore oil and gas production and processing site that has been in service for 25 years and is to be operated for a further 15 years, to age 40. It comprises reciprocating gas compressors driven by gas engines, crude transfer and water injection pumps, heater-treaters, separators, produced-water and gas piping, buried flowlines, crude storage tanks and the associated safety instrumented and relief systems. Equipment counts and failure modes were chosen to reflect such sites, drawing on reported failure behaviour of gas compressors [22, 53], compressor and turbine degradation [365, 368], heat transfer and fired equipment [12, 361, 362], corrosion in gathering and production systems [38, 157] and storage tanks [9, 189]. Costs are in 2022 United States dollars and include parts, labour, lost production and clean-up for failures, but exclude any monetary valuation of injury, which is reported separately as event frequencies. Cash flows were discounted at 8 percent per year when net present values are reported [354, 357].

3.3Active failure modes

Six active failure modes were modelled (Table 1). For each, a defect initiates at a random age $U$ that follows a Weibull distribution and becomes a functional failure after a delay time $H$ that follows an exponential distribution [37, 270, 274]:

where $\beta$ and $\eta$ are the Weibull shape and scale parameters and $\bar{h}$ is the mean delay time. Three task types were considered. Under run to failure, the item is renewed at failure, time $U+H$, at failure cost $C_f$. Under scheduled restoration, the item is restored at age $T$ at preventive cost $C_p$ if it has not failed. Under condition-based maintenance, the item is monitored at interval $\Delta$ at cost $C_i$ per check, and each check during the delay window detects the defect with probability $p$, after which it is restored at cost $C_p$ [279, 280, 288]. The long-run cost rate of each policy was evaluated by the renewal-reward theorem [256, 357]:

estimated by simulating 60,000 renewal cycles per policy, with the cycle length including the downtime of corrective or planned work. For scheduled restoration without monitoring this reduces to the classical age replacement expression [256, 260]:

where $R\left(t\right)$ is the survival function of the time to failure. Following the RCM consequence logic and the delay-time model with a safety constraint [18, 278], the selected task minimized $C\left(\Delta ,T\right)$ subject to a cap on the failure rate ${\lambda }_f$ for modes with safety or environmental consequences:

with ${\lambda }_{\mathrm{cap}}$ set at 0.005 failures per item-year for safety consequences and 0.25 per item-year for environmental consequences. Restoration ages were searched from 0.1 to 2 times $\eta$ and monitoring intervals from 0.1 to 1.5 times $\bar{h}$.

Code	Failure mode	Items	Consequence	Shape, scale (yr)	Delay (d)	Cp / Cf / Ci (USD k)	Detection
R1	Reciprocating compressor valves	4	Operational	1.8, 1.6	29	10 / 70 / 0.4	0.85
R2	Compressor main bearing and crosshead	4	Operational	2.6, 7.0	91	55 / 380 / 1.2	0.75
R3	Gas engine driver top end	4	Operational	3.2, 3.5	55	45 / 160 / 0.8	0.70
R4	Crude transfer pump mechanical seal	6	Environmental	1.3, 2.2	18	6 / 38 / 0.3	0.60
R5	Water injection pump plunger packing	4	Operational	1.6, 1.2	22	5 / 25 / 0.3	0.70
R6	Heater-treater fire tube	5	Safety	3.5, 14.0	365	90 / 650 / 3.0	0.80

3.4Static equipment

Five static equipment classes were modelled (Table 2), covering internal CO2 corrosion of separators [39, 145], microbiologically influenced corrosion and pitting of produced-water piping [154, 155], corrosion under insulation of gas piping [171, 184], corrosion of crude storage tank floors [189, 194] and internal corrosion of buried flowlines [38, 153]. Each item $i$ has a wall-thinning rate $R_i$ drawn from a lognormal distribution with the class mean and coefficient of variation, representing item-to-item variability in fluids, flow and protection. The remaining wall thickness $w_i$ at facility age $a$ evolves as a gamma-type increment process [196, 357]:

where ${\epsilon }_{i,a}$ has unit mean and coefficient of variation 0.5, and $m\left(a\right)$ is an acceleration factor that represents rising corrosivity as water cut increases in a maturing field [38, 164]:

with $g=0.05$ per year in the base case, so that corrosion rates double between ages 20 and 40. Loss of containment occurs when $w_i$ falls below a leak threshold $w_{\mathrm{fail}}$; the item is then repaired or replaced at failure cost $C_f$ and its wall is restored. Inspections measure the wall at the most corroded location with probability $E$, the inspection effectiveness, and otherwise measure a less corroded location, so that the loss is underestimated by a random 20 to 60 percent; measurement error has a standard deviation of 0.2 mm [197, 198, 205]. Inspection effectiveness values of 0.5, 0.7 and 0.85 were used for spot ultrasonic thickness measurement, targeted thickness measurement and risk-based selection of techniques such as automated ultrasonic and guided wave testing, consistent with the effectiveness categories used in RBI and in reviews of NDT for predictive failure analysis [26, 183, 203, 207].

At each inspection the corrosion rate is estimated from the measurement $\widehat{w}$. The long-term rate uses the wall loss since the item was last renewed, and the short-term rate uses the change since the previous inspection, following API 510 [24]:

where $w_0$ is the nominal wall, $a_0$ the age at renewal, $w_{\min }$ the minimum required thickness and $RL$ the estimated remaining life. Repair or replacement at cost $C_r$ restores the wall. The decision rules differ by strategy and are described under the strategies compared below.

Code	Equipment and mechanism	Items	Nominal, minimum and leak wall (mm)	Mean rate (mm/yr)	CoV	Ci / Cr / Cf (USD k)
C1	Separator vessels, internal CO2 corrosion	8	16.0 / 10.0 / 5.0	0.18	0.5	8 / 180 / 2,200
C2	Produced-water piping, MIC and pitting	24	7.1 / 3.0 / 1.0	0.12	0.7	2.5 / 45 / 450
C3	Gas piping, corrosion under insulation	20	6.0 / 2.8 / 1.0	0.10	0.8	4 / 60 / 900
C4	Crude storage tank floors	6	6.35 / 2.54 / 0.5	0.09	0.6	40 / 250 / 1,800
C5	Buried flowlines, internal corrosion	40	6.0 / 2.5 / 1.0	0.11	0.7	3 / 35 / 350

To give every strategy the same starting condition, the history of each item from age 0 to age 25 was simulated under the legacy programme with common random numbers, and the strategies were applied from age 25. Each class was simulated with 1,500 replications of all items.

3.5Hidden failures

Three classes of protective devices were modelled (Table 3): pressure safety valves that fail to open, emergency shutdown (ESD) valves that fail to close and high-high level switches that fail on demand. Their dangerous undetected failure rates increase linearly with facility age, reflecting fouling, wax, scale and seal deterioration in produced-fluid service [3, 4]:

The average probability of failure on demand over a proof-test interval $\tau$, with proof-test coverage $c$ and the untested fraction revealed only at overhaul every $L=6$ years, was approximated as [3, 278]:

The expected cost per device-year is the test cost $C_t/\tau$ plus the expected cost of failures on demand $D\overline{PFD}{C}_c$, where $D$ is the demand rate and $C_c$ the consequence cost. Minimizing this cost gives the familiar square-root interval, which was then constrained so that the SIL 2 limit of 0.01 is respected:

Code	Device and failure mode	Items	Base failure rate (/yr)	Ageing factor (/yr)	Demand rate (/yr)	Consequence (USD M)	Test cost (USD k)
H1	Pressure safety valves, fail to open	60	0.003	0.04	0.02	3.0	1.8
H2	Emergency shutdown valves, fail to close	25	0.008	0.04	0.10	1.5	2.2
H3	High-high level switches, fail on demand	30	0.012	0.03	0.10	0.8	0.6

3.6Strategies compared

Table 4 defines the three strategies. S0 represents the legacy calendar programme typical of inherited maintenance plans [15, 16]. S1 represents a conventional RCM programme in which tasks and intervals are optimized once, using the historical average behaviour of the equipment, and then held constant [28, 29]. S2 is the integrated, age-adaptive programme proposed here. S1 and S2 use the same tasks for active failure modes, because Weibull renewal of rotating components is not affected by facility age in the model; they differ in the treatment of static equipment and hidden failures. For S1, the fixed inspection interval for each static class was chosen by simulation to minimize total cost under the assumption of no corrosion acceleration ($g=0$), which mirrors the use of historical rates to set intervals. For S2, a baseline inspection campaign covers all static items within the first two years of the horizon, and each subsequent interval is half the estimated remaining life, using the greater of the long-term and short-term rates, bounded between one and ten years, as in API 510 practice [24]:

Items with $RL$ below two years or $\widehat{w}<w_{\min }$ are repaired. S2 uses more effective inspection techniques at 1.4 times the unit inspection cost, reflecting the higher cost of the automated and non-intrusive methods that reviews of pressure vessel inspection describe [208].

Element	S0 Legacy calendar	S1 RCM, static intervals	S2 Integrated RCM and MI, age-adaptive
Active failure modes	Calendar overhauls (R1 2 yr, R2 5 yr, R3 2.5 yr), run to failure (R4, R5), 5-yearly fire tube inspection (R6)	RCM task selection with risk cap	Same as S1
Static inspection interval	Fixed legacy (5 to 10 yr)	Optimized once assuming constant corrosion rate	Half remaining life, 1 to 10 yr, with baseline campaign
Corrosion rate used	Long-term	Long-term	Greater of long-term and short-term
Repair criterion	Measured wall below minimum	Wall projected below minimum before next inspection	Remaining life below 2 yr or wall below minimum
Inspection effectiveness	0.5	0.7	0.85 at 1.4 times cost
Proof-test interval	Fixed (PSV 5 yr, ESD 1 yr, switches 1 yr)	Optimized once at historical average failure rate	Re-optimized each year at current failure rate, SIL 2 constrained
Proof-test coverage	0.7	0.9	0.9

3.7Data quality experiment

To quantify the value of maintenance data, failure models for R2 and R3 were estimated from samples of $n=5$, 10, 20, 40 and 80 records drawn from the true Weibull distribution, with random right-censoring that left about 40 percent of records as suspensions [41, 344]. The Weibull parameters were estimated by maximizing the censored log-likelihood [41, 347]:

where $x_j$ is the recorded time and ${\delta }_j$ equals one for a failure and zero for a suspension. The restoration age that is optimal for the estimated parameters was then applied to the true distribution, and the cost penalty relative to the true optimum was recorded over 400 replications; if the estimated shape parameter did not exceed 1.05, run to failure was selected, as RCM logic requires [18, 256]. A second experiment replaced 30 percent of records with misrecorded failures at random ages, representing the categorization and data entry errors reported in CMMS studies [35, 36, 40].

3.8Implementation

The models were implemented in Python with NumPy and SciPy. Renewal-reward cost rates were estimated from 60,000 simulated cycles per policy with fixed random seeds, static equipment from 1,500 replications per class with common random numbers across strategies, and the corrosion acceleration sensitivity from 800 replications per class for $g$ between 0 and 0.10 per year. The Monte Carlo standard error of the facility-level expected LOPC count was below 2 percent of its mean in all strategies. Simulation of maintained systems in this way follows established practice in maintenance optimization [31, 334, 335]. Intervals were rounded to whole days for monitoring tasks, to hundredths of a year for restorations and to whole years for static inspections, so that the reported policies can be scheduled directly in a CMMS.

4Results

This section reports the RCM task selection for active failure modes, the performance of the static equipment strategies, the proof-test results for hidden failures, the facility-level comparison, the sensitivity to corrosion acceleration and the data quality experiment. Results are reported as expected values over the 15-year horizon from age 25 to age 40 unless stated otherwise, consistent with the life-cycle perspective adopted in cost-based maintenance studies [354, 357]. Because the inputs are representative rather than site-specific, the relative differences between strategies are more informative than the absolute values, an interpretation that is standard for comparative maintenance policy studies [31, 32].

4.1RCM task selection for active failure modes

Table 5 shows the task selected for each active failure mode. Condition-based monitoring was selected for the compressor valves and the transfer pump seals, at intervals of about 16 and 15 days, which correspond to routine monitoring rounds or online monitoring. Scheduled restoration was selected for the compressor bearings and crossheads at 3.15 years, the gas engine top end at 2.1 years, the water injection pump packing at 0.66 years and the heater-treater fire tubes at 5.6 years. For the fire tubes, the risk cap was binding: the legacy 5-yearly inspection gave 0.065 failures per item-year, thirteen times the cap, whereas scheduled restoration at 5.6 years gave 0.004. Figure 2 shows the cost-rate curves for two modes. For the compressor bearings, the cost rate has a clear minimum at 3.15 years that is less than half the run-to-failure cost rate, which reflects the strong wear-out ($\beta =2.6$) and the large ratio of failure to preventive cost [256, 260]. For the compressor valves, the cost rate is flat between about 12 and 25 days, so the monitoring interval can be set by practical considerations within that band.

Code	Selected task	Interval	Legacy cost (USD k/yr)	RCM cost (USD k/yr)	Legacy failures (/yr)	RCM failures (/yr)
R1	Condition-based	16 d	40.8	27.7	0.556	0.196
R2	Scheduled restoration	3.15 yr	33.8	28.3	0.067	0.032
R3	Scheduled restoration	2.1 yr	31.1	30.3	0.104	0.072
R4	Condition-based	15 d	18.3	18.1	0.481	0.242
R5	Scheduled restoration	0.66 yr	22.0	17.9	0.880	0.474
R6	Scheduled restoration	5.6 yr	43.7	18.5	0.065	0.004

At facility level, RCM task selection reduced the 15-year cost of the active failure modes from USD 12.58 million to USD 9.28 million, or 26 percent, and the expected number of failures from 144.6 to 68.6, or 53 percent. Failures with safety or environmental consequences fell from 48.2 to 22.1. Equipment downtime fell from 28.0 to 24.1 item-days per year; the reduction was smaller than the reduction in failures because the selected restorations themselves take equipment out of service.

4.2Static equipment

Table 6 reports the static equipment results by class. Over the 15-year horizon, the legacy programme produced 16.9 expected LOPC events, of which 3.8 had safety consequences. S1 reduced these to 7.1 and 1.3, and S2 to 4.6 and 0.9. Relative to S1, S2 reduced LOPC by 35 percent and safety LOPC by 26 percent. The optimized fixed intervals in S1 were 7 years for separators and produced-water piping, 5 years for CUI-prone gas piping and 8 years for tank floors and flowlines. The largest absolute reductions were in buried flowlines and produced-water piping, which have many items, high variability in corrosion rate and relatively low inspection cost per item.

Code	S1 interval (yr)	S0 LOPC	S1 LOPC	S2 LOPC	S0 cost (USD M)	S1 cost (USD M)	S2 cost (USD M)
C1	7	0.29	0.10	0.07	1.51	1.43	1.57
C2	7	3.72	1.58	1.02	2.20	1.52	1.47
C3	5	3.48	1.17	0.88	3.70	2.13	2.03
C4	8	0.77	0.28	0.14	1.99	1.73	1.99
C5	8	8.60	3.94	2.47	3.81	2.53	2.44
Total		16.86	7.07	4.59	13.20	9.34	9.50

Figure 3 shows how the strategies diverge over time. Under S0, the expected LOPC frequency rises steadily from 0.63 per year at age 25 to 1.65 per year at age 40, because fixed intervals and spot inspections cannot keep pace with accelerating corrosion. Under S1, the frequency falls after the first inspections but rises again in the middle of the horizon as intervals set for historical corrosion rates become too long. Under S2, the baseline campaign brings the frequency down to about 0.1 per year by age 28, after which it rises slowly to about 0.35 per year as acceleration continues. The cumulative static cost of S2 is higher than that of S1 in the early years because of the baseline campaign, but the two converge by age 40 at about USD 9.3 to 9.5 million, and both are well below the USD 13.2 million of S0, whose costs are dominated by failures [14, 26].

4.3Hidden failures

Table 7 reports the proof-test results. Under S0 the average PFD of all three device classes exceeded the SIL 2 limit throughout the horizon, reaching 0.021, 0.026 and 0.033 for pressure safety valves, ESD valves and level switches by age 40. Under S1, the higher coverage and optimized intervals reduced PFD by about a third, but because the intervals were set at the historical average failure rate, PFD still exceeded 0.01 in every year and rose to between 0.016 and 0.017 by age 40. Only S2, which re-optimized intervals each year at the current failure rate, kept every class at or below the limit in every year. To do so, S2 shortened the ESD valve test interval from 8.5 months at age 25 to 4.9 months at age 40 (Figure 4), the level switch interval from 4.6 to 2.2 months and the pressure safety valve interval from 3.0 to 2.2 years. Expected failures on demand over the horizon fell from 2.52 under S0 to 1.47 under S1 and 1.00 under S2. The pattern matches the life extension literature, which warns that protective systems degrade with age and that test regimes fixed at design cannot be assumed to remain adequate [3, 4].

Code	Strategy	Interval at age 25	Interval at age 40	PFD at age 25	PFD at age 40	Test cost (USD k)	Failures on demand
H1	S0	5.0 yr	5.0 yr	0.0161	0.0205	324	0.33
H1	S1	3.9 yr	3.9 yr	0.0123	0.0157	421	0.25
H1	S2	3.0 yr	2.2 yr	0.0100	0.0100	635	0.18
H2	S0	12 mo	12 mo	0.0202	0.0258	825	0.86
H2	S1	14 mo	14 mo	0.0135	0.0172	696	0.58
H2	S2	8.5 mo	4.9 mo	0.0100	0.0100	1,549	0.38
H3	S0	12 mo	12 mo	0.0265	0.0328	270	1.33
H3	S1	8.2 mo	8.2 mo	0.0128	0.0159	397	0.65
H3	S2	4.6 mo	2.2 mo	0.0100	0.0100	1,035	0.45

4.4Facility-level comparison

Table 8 combines the three streams. The integrated age-adaptive strategy S2 had a 15-year cost of USD 23.46 million, 23 percent below the USD 30.55 million of S0 and 5.4 percent above the USD 22.27 million of S1; in present value terms at 8 percent the figures were USD 14.03, 17.40 and 13.36 million. Relative to S1, S2 spent USD 1.16 million more on inspection, USD 1.71 million more on proof testing and USD 0.35 million more on repair, and recovered USD 1.35 million in avoided static failure costs and USD 0.67 million in avoided costs of failures on demand. It averted 2.48 LOPC events and 0.47 failures on demand, so the net premium of USD 1.19 million corresponds to about USD 0.40 million per averted event, or USD 0.23 million in present value terms. Relative to S0, S2 averted 12.3 LOPC events, 2.8 safety LOPC events and 1.5 failures on demand while saving USD 7.1 million. The cost of averting an event under S2 is modest compared with the failure costs assumed for the static classes, which range from USD 0.35 to 2.2 million per event, consistent with the economic case for proactive integrity management made in corrosion cost and asset integrity studies [14, 26].

Measure	S0 Legacy	S1 RCM static	S2 Integrated
Active failure mode cost (USD M)	12.58	9.28	9.28
Active failures	144.6	68.6	68.6
Equipment downtime (item-days per year)	28.0	24.1	24.1
Static inspection cost (USD M)	1.24	1.19	2.35
Static repair cost (USD M)	2.12	4.28	4.63
Static failure cost (USD M)	9.84	3.87	2.52
Static LOPC events	16.86	7.07	4.59
Static LOPC events with safety consequence	3.77	1.27	0.95
Proof-test cost (USD M)	1.42	1.51	3.22
Expected cost of failures on demand (USD M)	3.35	2.14	1.46
Failures on demand	2.52	1.47	1.00
Years meeting SIL 2 for all classes (of 15)	0	0	15
Total cost (USD M)	30.55	22.27	23.46
Present value at 8 percent (USD M)	17.40	13.36	14.03

4.5Sensitivity to corrosion acceleration

Figure 5 shows how the static equipment results change with the corrosion acceleration rate $g$. With no acceleration, S1 is the cheapest static strategy at USD 5.82 million against USD 6.18 million for S2, although S2 still has fewer LOPC events, 1.83 against 2.86. As acceleration increases, the cost advantage of S1 disappears: the two are equal at $g=0.05$, and at $g=0.075$ and $g=0.10$ S2 is both cheaper, at USD 11.48 and 13.75 million against USD 11.54 and 14.15 million, and safer, with 6.3 and 8.5 LOPC events against 10.1 and 13.9. The legacy strategy deteriorates fastest, from 6.7 LOPC events at $g=0$ to 29.4 at $g=0.10$. The results confirm that the value of age-adaptive intervals lies in their robustness to degradation that is faster than history suggests [3, 38, 39].

4.6Value of maintenance data

Table 9 and Figure 6 report the data quality experiment. With five records, the restoration age selected from estimated parameters raised the expected cost of the compressor bearing mode by 22.5 percent on average above the true optimum, and by 99.8 percent at the 90th percentile; for the gas engine top end the figures were 13.1 and 56.8 percent. The penalty fell quickly with sample size: with 20 records, the mean penalty was 1.4 and 1.0 percent, and with 80 records, 0.3 and 0.2 percent. The estimated shape parameter was biased upward in small samples, a known property of maximum likelihood estimation with few failures [41, 346]. When 30 percent of the records were misrecorded, the shape parameter was biased towards random failure, with a median of 2.04 at 80 records against a true value of 2.6, and the mean penalty remained above 0.6 percent even with 80 records and was 3.7 percent with 20 records, more than twice the penalty with clean data. These results quantify the claim, made in studies of work-order data, that data quality limits the benefits of reliability analysis [34, 35, 40].

Records	R2 mean	R2 90th percentile	R2 median shape estimate	R3 mean	R3 90th percentile	R2 misrecorded mean	R2 misrecorded median shape estimate
5	22.5	99.8	3.21	13.1	56.8	18.7	2.50
10	5.5	11.3	2.90	3.1	6.8	7.6	2.24
20	1.4	3.7	2.85	1.0	2.7	3.7	2.13
40	0.5	1.6	2.68	0.4	1.0	1.3	2.06
80	0.3	0.5	2.66	0.2	0.3	0.6	2.04

5Discussion

This section interprets the results in relation to the literature, draws implications for the management of ageing onshore facilities, considers how the framework can be implemented with the data and organization that such facilities typically have, and sets out the limitations of the study. The discussion is guided by the view that maintenance and integrity management must balance cost, risk and performance across the life cycle [42, 354, 357], and by the evidence from ageing studies that the management of deterioration, rather than calendar age itself, determines the safety of older plant [1–3]. It also draws on the observation that the gap between maintenance optimization models and practice is often organizational and data-related rather than mathematical [31, 32, 377].

5.1Principal findings

Three findings stand out. First, RCM task selection alone produced the largest single cost reduction, because the legacy programme combined over-maintenance of some modes, such as frequent compressor valve overhauls, with under-maintenance of others, such as run-to-failure of seals and packing and infrequent fire tube inspection. This confirms the experience of RCM applications to compressors and rotating equipment, in which much of the value of RCM comes from reallocating effort between failure modes rather than increasing it [21, 22]. The binding risk cap for heater-treater fire tubes illustrates that, for modes with safety consequences, RCM logic can require a more intensive task than cost alone would justify, consistent with the safety-constrained delay-time model [278] and with the RCM evaluation criteria [18].

Second, static intervals optimized once on historical behaviour lose effectiveness as facilities age. S1 reduced static LOPC events by 58 percent relative to the legacy programme, but its frequency rose again in the middle of the horizon and it failed to keep any class of protective device within its SIL 2 limit. Age-adaptive intervals in S2 removed a further 35 percent of static LOPC events and brought all safety instrumented functions into compliance, at a cost premium of about 5 percent over S1. This supports the argument of life extension studies that ageing must be managed by continual reassessment of condition and remaining life rather than by extending existing programmes [3–5], and it is consistent with risk-based inspection of ageing pressure vessels for fitness-for-service [27] and with optimal inspection planning for corroding onshore pipelines [196].

Third, the advantage of the integrated strategy grows as corrosion accelerates. When corrosion rates remain at historical levels, S1 is cheaper but less safe; when they rise by 5 percent per year or more after age 20, S2 is at least as cheap and substantially safer. Because water cut, and with it corrosivity, typically increases as fields mature [38, 164], the conditions under which S2 dominates are the conditions that ageing onshore facilities are most likely to face. The data quality experiment adds that these benefits depend on the maintenance records available: with fewer than about ten clean records per failure mode, or with substantial misrecording, the expected cost penalty of optimized restoration ages is several percent and can be much larger in unfavourable samples [34, 40, 41].

5.2Implications for practice

For operators planning the life extension of an onshore facility, the results suggest a sequence of actions. The first is an RCM review of active failure modes, focusing on those with high failure costs, strong wear-out and safety or environmental consequences, using criticality analysis to set priorities [82, 85, 89, 90] and multi-criteria methods where consequences are difficult to monetize [55, 61, 63]. The second is a baseline integrity campaign for static equipment, using inspection techniques selected for the damage mechanism, such as automated ultrasonic or guided wave testing for internal and under-insulation corrosion [171, 183, 205, 208], followed by intervals based on remaining life and the greater of long-term and short-term corrosion rates [24, 30]. Where thinning approaches the minimum thickness, fitness-for-service assessment and remaining strength methods can justify continued operation or define repair [25, 221, 223, 225]. The third is an annual review of proof-test intervals for safety instrumented functions and relief devices, using failure rates updated from proof-test results, so that intervals shorten as devices age [3, 278].

The integrated strategy also changes how maintenance performance should be measured. Indicators that reward low inspection cost or long intervals would favour S1 in the early years of a life extension, even though S2 performs better over the horizon. Maintenance performance frameworks that combine leading indicators, such as inspection backlog, proof-test compliance and remaining-life coverage, with lagging indicators such as LOPC events would better reflect the value of age-adaptive management [96–98]. The European indicator standard [46], and sustainability-oriented indicator sets [100, 102, 103] provide a starting point. Integration of opportunistic maintenance, in which inspections and restorations are grouped when equipment is shut down for other reasons, could reduce the cost premium of S2 further [293, 298, 299].

5.3Data and organizational requirements

The framework requires failure and inspection data of a quality that many ageing facilities do not have. The data quality experiment shows that the marginal value of additional clean records is high up to about 20 records per failure mode and modest beyond that, which suggests that pooling records across similar equipment, using generic data as prior information and cleaning historical work orders are worthwhile investments [35, 135, 136]. Technical language processing of free-text work orders can recover failure information that is not coded [137–139], and automated data collection can reduce entry errors [128, 140]. Bayesian updating of reliability from condition data provides a principled way to combine generic and site-specific information [199, 358]. Interpretable machine learning for early failure prediction [142] and data-driven methods drawn from industrial automation [141] can use the same records, provided that their inputs are cleaned first. For buried flowlines, GIS-based asset registers and UAV corridor surveys can add location and external condition data to the CMMS record [211, 212]. Trend tests and models for repairable systems should be used to check whether failure rates are changing over time before a stationary model is adopted [343–345].

Organizationally, the integrated framework crosses the boundaries between maintenance, inspection and instrumentation functions, which in many companies have separate budgets and systems [16, 17, 77]. Total productive maintenance experience shows that cross-functional teams and operator involvement are needed to sustain improvement [115, 119, 123], and studies of maintenance personnel and training show that competence must be maintained as the workforce changes [47, 114, 376]. The higher task frequencies of S2 also place demands on materials readiness and spare parts supply, so procurement and supply chain planning for gas processing plants should be aligned with the revised maintenance plan [112, 113, 341]. Because maintenance tasks can themselves introduce failures, increasing the frequency of restorations and proof tests should be accompanied by attention to maintenance error, for example through human reliability analysis of critical tasks [370, 371, 374, 375].

5.4Relation to maintenance optimization research

The models used here are deliberately standard, but the results bear on several strands of research. The delay-time model remains a practical way to set monitoring intervals when defect detection is imperfect [37, 270, 273], and the flat cost-rate curves for some modes support the use of practical intervals within a band rather than a precise optimum [277, 288]. The static equipment model is close to the stochastic deterioration models used for maintained structures [286, 357], and the inspection effectiveness parameter links it to probability of detection models used in RBI [197, 198]. Extending the framework to multi-component economic dependence [254, 293, 294], imperfect repair [350, 351, 353], competing failure processes [268, 369] and joint optimization of spare parts [113, 292, 338] would increase realism. Machine-learning prognostics for compressors and turbines [52, 365, 367, 368] and data-driven burst strength models for corroded pipelines [246–248] could supply the condition information that age-adaptive intervals require.

5.5Limitations

The study has several limitations. All parameters are representative values selected from published ranges rather than data from a specific facility, so the absolute results should not be transferred to any site without recalibration [31, 32]. Failure modes were modelled independently, without economic or stochastic dependence between them, which ignores opportunities for grouping and the possibility of cascading failures [293, 333]. Corrosion was represented by a single acceleration function common to all classes, whereas in practice different mechanisms respond differently to water cut, temperature and solids [158, 164, 171]. The consequence of failures on demand was represented by an expected cost rather than a full consequence analysis, and injury was reported as event frequency without monetary valuation [81, 278]. Finally, the data quality experiment considered only Weibull estimation for two failure modes; corrosion rate estimation from inspection data is also subject to data quality problems that were represented only through inspection effectiveness [198, 203].

6Conclusion

This study developed an integrated reliability-centered maintenance and mechanical integrity framework for ageing onshore oil and gas production facilities and demonstrated it on a representative 25-year-old facility over a 15-year life extension. The framework assigns RCM tasks to active failure modes with a risk cap for safety and environmental consequences, manages static equipment by risk-based inspection with corrosion-rate updating and inspection effectiveness, and sets proof-test intervals for protective devices that shorten as failure rates rise with age.

Relative to a legacy calendar programme, RCM task selection halved active failures and reduced their cost by about a quarter. Static intervals optimized once on historical behaviour cut static loss of containment by more than half but left every class of protective device above its SIL 2 limit. The integrated age-adaptive strategy removed a further third of static loss of containment events, kept all protective devices within their limits in every year and reduced failures on demand by 60 percent relative to the legacy programme, at a cost 23 percent below the legacy programme and 5 percent above the static RCM programme. The advantage of the integrated strategy grew as corrosion accelerated, and the value of all optimized programmes depended on having more than a handful of clean failure records per mode.

For operators, the results support a sequence of RCM review, baseline integrity campaign with remaining-life-based intervals and annual review of proof-test intervals as the core of a life extension programme, supported by investment in maintenance data. Further work should calibrate the framework with field data from ageing onshore facilities, add dependence between failure modes and opportunistic grouping, and extend the treatment of data quality to inspection and corrosion monitoring records.

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