Optimizing re-inspection intervals for aboveground storage tanks utilizing risk-based approach and advanced tank bottom scanning

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Introduction

Aboveground Storage Tanks (ASTs) play a crucial role in industries like oil and gas, chemicals, and agriculture, offering essential storage for liquids and gases. Ensuring the structural integrity of these tanks is vital for operational safety, environmental protection, and cost-effectiveness.

In Kazakhstan, the growth of large-scale storage facilities was raised on a high level during the COVID-19 pandemic, which posed unprecedented challenges to industries globally, with the oil and gas sector being significantly affected. The crisis caused a sharp drop in global demand, resulting in a need to store produced oil, storage overcapacity, and economic stagnation. This period underscored the critical need for robust AST integrity management to safeguard assets during market downturns and to facilitate recovery as global demand rebounded.

Here’s a Tab. 1 of AST capacities in cubic meters by country, based on available data. The figures primarily refer to crude oil and petroleum products storage capacities.

Table 1. AST capacities in cubic meters by country

Source: statista.com

Optimizing re-inspection intervals is vital for AST reliability. Advanced NDT methods help detect hidden deterioration, reducing failure risks. Combining RBI with techniques like tank bottom scanning improves inspection efficiency, extends asset life, and supports better risk management.

In Kazakhstan, current regulations mandate fixed inspection intervals – for example, every eight years – under the ST RK standard. While this ensures compliance, it doesn’t consider dynamic risks like localized corrosion or changing conditions, leading to inefficiencies and missed chances for targeted maintenance¹.

In contrast, the RBI methodology, as outlined in API Recommended Practice 580 [1] and the quantitative framework provided in API Recommended Practice 581 [2], encourages the use of non-invasive inspection techniques that allow for accurate assessment of material condition and damage mechanisms without compromising operational safety. These techniques have been shown to offer superior reliability in detecting localized corrosion, cracking, and other integrity-related anomalies, particularly in corrosion-prone zones of aboveground storage tanks. While the national standard mandates external visual inspection to evaluate the condition of the tank surface, internal visual inspection is generally not considered a standard practice within RBI programs. Instead, advanced non-destructive testing methods, such as MFL, ACFM, Eddy-current, are employed to monitor internal surfaces based on the identified degradation mechanisms, thus minimizing personnel exposure and enhancing inspection efficacy.

In summary, while national regulations in Kazakhstan mandate periodic hydrostatic testing to maintain tank integrity, modern RBI-based approaches – aligned with international standards like API RP 580 and 581 – offer a more efficient, risk-focused strategy. These methods enhance reliability, reduce inspection frequency for low-risk equipment, and support global safety and sustainability goals.

Figure 1. Overview of NDT methods used for AST

In red – advanced NDT methods

Although RBI is not yet widely implemented

in Kazakhstan, the legal framework for its adoption exists. Resolution No. 717 (December 30, 2011)², provides a methodology for risk assessment in state control and supervision, laying the groundwork for risk-based practices. However, practical application of RBI remains limited and is still evolving across industries.

A major challenge is demonstrating the clear benefits of RBI and advanced inspection technologies over traditional time-based methods. This calls for in-depth analysis and field validation to prove their value in optimizing inspection schedules, improving safety, cutting operational costs, and maintaining regulatory compliance.

Numerous studies highlight the effectiveness of RBI in international settings. One notable example is the Kuwait Oil Company (KOC), which successfully transitioned from fixed-interval inspections to a structured RBI approach aligned with API RP 581 and NFPA guidelines. KOC classified tanks by risk level, identified key damage mechanisms – such as bottom plate corrosion and roof integrity issues – and adjusted inspection intervals based on risk. This shift enabled KOC to prioritize critical tanks, optimize inspection resources, and enhance overall asset integrity. The case demonstrates how RBI can improve maintenance planning and operational efficiency in large-scale industrial operations [3].

However, in Kazakhstan, there is a lack of officially published studies on the application of RBI to ASTs. Addressing this gap is essential for ensuring the long-term reliability and efficiency of the country’s storage tank infrastructure – especially as aging facilities and changing operational conditions call for a more strategic approach to risk management. The OECD’s Risk Governance Scan of Kazakhstan also underscores this need, pointing to shortcomings in the country’s disaster risk management frameworks and emphasizing the importance of forward-looking, risk-informed planning to enhance infrastructure resilience [4].

The objective of the study is to compare re-inspection intervals for ASTs in Kazakhstan by employing an RBI framework complemented by advanced NDT methods, such as the TBIT Ultra technology. By addressing these factors within the context of Kazakhstan’s industrial and regulatory landscape, the study aims to provide a comprehensive framework for improving AST inspection practices while balancing safety, environmental stewardship, and economic viability.

Materials and methods

The RBI methodology applied in this study offers a structured approach to prioritize inspection and maintenance of ASTs by assessing the Probability of Failure (PoF) and Consequences of Failure (CoF). These two factors define the overall risk level, guiding decisions on inspection intervals, techniques, and repairs. This approach aligns with API RP 580 and 581, which provide industry-recognized frameworks and quantitative tools for risk-based inspections in oil, gas, and petrochemical sectors.

In practice, a small portion of equipment often accounts for the majority of risk. RBI enables teams to focus resources on high-risk tanks while optimizing efforts for lower-risk ones. It involves identifying degradation mechanisms, linking them to potential failures, and developing targeted inspection plans. API RP 581 offers quantitative methods to evaluate PoF and CoF, enabling data-driven risk ranking and planning.

Unlike Kazakhstan’s current prescriptive model (e.g., ST RK standards), which mandates fixed-interval inspections, RBI allows for condition-based prioritization. Adopting RBI in line with API standards would enhance safety and cost-efficiency in managing storage tank infrastructure.

Figure 2. RBI process [5]

Integration of NIMA Integrity Management (IM) Software

In my work, I utilized NIMA Integrity Management (IM) software as a core tool for implementing RBI methodologies. Developed by ROSEN, NIMA is an integrated platform that supports data-driven asset integrity management by consolidating, visualizing, and interpreting large volumes of inspection and operational data. It enables comprehensive risk assessments by integrating in-line inspection results, material properties, degradation mechanisms, and historical performance trends.

The software played a vital role in processing inspection data, performing quantitative risk calculations, and supporting predictive modeling to assess both PoF and CoF. This allowed for dynamic adjustment of inspection intervals based on evolving risk factors, improving planning efficiency and resource allocation.

Within this study, NIMA IM provided a structured framework for evaluating asset integrity, aggregating inspection data, and generating risk matrices to prioritize high-risk equipment. By automating the correlation between degradation mechanisms, failure probabilities, and inspection schedules, the software enhanced consistency, reduced subjectivity, and improved the accuracy of risk assessments.

Additionally, NIMA IM served as a centralized database for managing inspection histories, corrosion data, and maintenance records, ensuring compliance with API RP 580 and API RP 581 standards and facilitating audit readiness [6].

The use of NIMA IM is particularly valuable in contexts where infrastructure is aging and regulatory pressure for risk transparency is increasing, such as in Kazakhstan. In this regard, NIMA not only supports operational reliability but also strengthens strategic risk governance and sustainability in asset-intensive industries.

Risk Calculation and RBI Framework

Below is an overview of the key criteria and algorithms used in risk calculation within the RBI framework:

Probability of Failure (PoF): PoF is determined by factors such as corrosion rates, environmental conditions, and the effectiveness of protective measures like coatings and cathodic protection. Tools like the TBIT Ultra system enhance accuracy by quantifying metal loss and detecting critical defects.

Consequence of Failure (CoF): CoF measures the potential impact of tank failure, including downtime, environmental damage, safety risks, and financial loss. Tanks storing hazardous or volatile substances – like gasoline in floating

roof tanks – have higher CoF due to fire, explosion, or vapor release risks. Environmental regulations also raise the significance of CoF due to spill prevention requirements.

Risk Calculation. Risk is calculated using the formula (1):

Risk = PoF × CoF (1)

A risk matrix (Fig. 3) visually categorizes equipment into risk levels – low, medium, high, or critical – based on PoF and CoF. This matrix supports prioritization of inspections and optimized maintenance planning. High-risk tanks require immediate action, while low-risk tanks may have longer inspection intervals.

Figure 3. Example of Risk matrix generated in NIMA Integrity Management Software

By combining qualitative and quantitative assessments, the RBI method improves safety, reduces costs, and enhances operational efficiency through targeted, risk-driven inspections [7].

Time-Based vs. Risk-Based Approach

Historically, time-based inspection protocols have been the primary method for monitoring ASTs. Tab. 2 below outlines the regulated intervals for key maintenance and inspection activities for vertical ASTs under this approach. Unlike RBI, the time-based method schedules tasks at fixed intervals, regardless of the equipment’s actual risk level. These standards guide maintenance practices for storage tanks in Kazakhstan and support operational planning at industrial sites.

Table 2. The industry standards timeframes in Kazakhstan³

According to the Standard of the Republic of Kazakhstan ST RK 3731–2021 titled “Oil and gas industry. Technical inspection of equipment based on risk factors”, the determination of inspection intervals under the RBI methodology is directly linked to the calculated risk level of the equipment. This standard emphasizes that inspection frequency should be based on maintaining an acceptable risk level for each corrosion circuit or equipment item, rather than following fixed, prescriptive intervals. The RBI approach provides a flexible yet structured framework, where the higher the risk level, the shorter the inspection interval, ensuring that critical equipment is monitored more frequently while low-risk equipment can be inspected less often without compromising safety or reliability.

Tab. 3 below presents the recommended inspection intervals in accordance with ST RK 3731–2021, reflecting the principle that inspection efforts should be proportionate to the identified risk levels:

Table 3. Recommended Inspection Intervals under RBI

However, traditional time-based methods often overlook variable risk factors affecting tank performance, leading to inefficient resource use and higher integrity risks. In contrast, RBI aligns inspections with actual risk levels, improving efficiency by up to 20% over conventional approaches. This is achieved by focusing resources on high-risk equipment, where they have the most impact. Advanced NDT techniques like ultrasonic testing further strengthen RBI by targeting critical vulnerabilities such as tank bottom corrosion and roof seal issues, enhancing the detection and monitoring of potential failures.

Table 4. Comparison of TBI and RBI

Unlike traditional time-based inspections, RBI prioritizes inspections based on risk assessments, ensuring that critical components receive more attention [8]. According to Tab. 4, TBI is cost-effective, requires fewer personnel, and extends equipment life, offering 12–18% savings over reactive maintenance. However, it may still lead to failures, includes unnecessary maintenance, and can be labor-intensive. RBI, on the other hand, reduces downtime, enhances safety, and lowers parts and labor costs, providing 8–12% savings over TBI. Its downsides are higher upfront investments in equipment and training, with benefits not always clear to management. TBI emphasizes simplicity and cost control, while RBI aims for efficiency and safety at a higher initial cost.

Case Study

ASTs data sets overview

This study analyzed 27 ASTs across various regions of Kazakhstan using RBI assessments and NIMA IM software. A detailed evaluation was carried out for each tank, with “Tank X” used as a representative example in Tab. 5. The assessment included key parameters to determine structural integrity, risk level, and overall condition within the RBI framework.Corrosion data – such as rates, repair thresholds, RWT (before/after repair), and corrosion allowances – were collected. Remaining Life (RL) and Minimum Inspection Intervals (MII) were calculated using ROSEN methodology, factoring in internal/external corrosion rates and regional climate influences.

Table 5. Summary table for Tank X

All data were processed in NIMA IM for degradation analysis and life prediction. To support RBI implementation, input data were divided into two tables: one for fixed design/operational parameters, and another for variable corrosion-related data, allowing a clear and systematic evaluation of tank conditions and inspection priorities.

Tab. 6 presents constant parameters common to all ASTs, including design features, inspection confidence levels, internal lining, cathodic protection, fluid characteristics, and product price. These serve as a consistent baseline for corrosion risk evaluation. Tab. 7 contains variable data for each tank, such as measured corrosion rates (soil-side, product-side, and combined), wall thickness, inspection and installation dates, storage temperature, and potential production loss costs. This information forms the basis for estimating degradation, remaining life, and identifying high-risk tanks.

Table 6. Constant Data

Table 7. Corrosion and Operational Data

Despite identical operational conditions across all ASTs – such as crude oil storage, internal lining, cathodic protection, and temperature – corrosion rates vary, driven by local factors like soil chemistry, microbial activity, and material inconsistencies.

The highest combined corrosion rate recorded is 0.181 mm/year, signaling a more aggressive local environment. Given a nominal wall thickness of 5 mm, this indicates faster structural degradation and reduced service life. Production loss costs also vary, reaching up to $22,500/day, making it critical to prioritize tanks with both high corrosion and economic impact in RBI planning. These corrosion rates support tank prioritization for inspection or repair, enable predictive remaining life modeling, and guide proactive maintenance.

Fig. 4 consolidates soil-side (pink), product-side (blue), and combined (purple) corrosion rates to visually compare degradation patterns across tanks. Soil-side corrosion reflects external factors like moisture, soil chemistry, and microbial activity. Higher rates suggest inadequate cathodic protection or low soil resistivity. Product-side corrosion varies more widely, affected by stored media, lining condition, and temperature. Tanks with high internal degradation require closer inspection and potential relining.

Figure 4. Comparison of Soil-Side, Product-Side, and Combined Corrosion Rates Across Storage Tanks

Combined corrosion rates offer a holistic view of degradation. Tanks with high values on both sides represent critical risk and demand prioritized inspection and advanced NDT. Visualizing all three corrosion types together reveals interactions between internal and external degradation mechanisms, reinforcing the need for a risk-based rather than fixed-interval inspection approach.

Understanding these corrosion patterns supports smarter resource allocation, enhancing safety and long-term asset reliability.

Results

The risk assessment performed under the RBI methodology includes the evaluation of both PoF and CoF for 27 ASTs. Three different assessment scenarios were considered to analyze the impact of varying risk acceptance criteria on inspection planning.

Scenario 1: Risk-Based Inspection (RBI) with a PoF Target of 0.1 incidents/year

In the first scenario, a conservative RBI strategy was implemented with a PoF target of 0.1 incidents per year and a financial risk threshold of $30,000 per year. This configuration prioritizes safety and reliability, ensuring frequent inspections to mitigate the likelihood of failure.

The risk matrices in Fig. 5 and Fig. 6 show that Tank No. 19 and Tank No. 24 exceed the risk acceptance criteria due to wall loss over 50%, placing them in the high-risk zone. This necessitates urgent inspection or maintenance. The updated risk matrix for the next inspection date confirms that the proposed strategy is effective, with all tanks expected to fall within acceptable risk levels, demonstrating the value of RBI in maintaining equipment integrity and preventing failures.

Figure 5. Risk matrix at initial inspection date showing current risk levels across tanks based on PoF and CoF

Figure 6. Risk matrix after re-inspection planning showing mitigated risk levels across all tanks

The average interval between inspections in this scenario was determined to be 5.6 years, reflecting a stringent approach where failure mechanisms such as corrosion and mechanical degradation are detected at an early stage. The inspection planning chart for this scenario Fig.7 illustrates a higher frequency of scheduled inspections, particularly in earlier years.

Figure 7. Inspection planning chart for Scenario 1 (PoF target: 0.1 incidents/year)

In addition, in Fig. 8 ISO-risk plot serves as a graphical summary of risk distribution across the tank population. It clearly illustrates the correlation between PoF and financial consequences, supporting risk ranking and enabling informed decision-making in the maintenance planning process.

Figure 8. ISO-risk plot displaying distribution of tanks based on Probability of Failure and Financial Risk

This conservative approach minimizes operational risk by ensuring early detection of deterioration, thereby reducing the probability of severe failures. However, this comes at the cost of increased inspection and maintenance expenses, which must be weighed against the benefits of enhanced reliability and regulatory compliance.

Scenario 2: Risk-Based Inspection (RBI) with a PoF Target of 0.3 incidents/year

The second RBI scenario explores the impact of increasing the PoF target to 0.3 incidents per year and adjusting the financial risk threshold to $50,000 per year. This scenario demonstrates how adjusting risk tolerance affects inspection planning by extending inspection intervals.

With these revised parameters, the average time between inspections increased to 6.5 years, as indicated in the inspection planning chart in Fig. 9. This reduction in inspection frequency is a direct result of the increased acceptable risk level, allowing for more extended operational periods before re-inspection.

Figure 9. Inspection planning chart for Scenario 2 (PoF target: 0.3 incidents/year

This scenario illustrates the sensitivity of inspection intervals to risk tolerance levels. By accepting a higher PoF target, the number of scheduled inspections is reduced, optimizing maintenance resources and decreasing operational disruptions. However, this approach also elevates the probability of undetected deterioration, necessitating the implementation of additional monitoring techniques, such as real-time corrosion asses-sment and predictive analytics.

Scenario 3: TBI Approach

Unlike the RBI methodologies, the TBI approach follows fixed inspection intervals, independent of actual asset condition or degradation rates. This traditional methodology assumes a uniform degradation progression, scheduling inspections at predetermined timeframes.

The limitations of TBI arise from its inflexibility and inefficiency compared to RBI strategies. Two key inefficiencies include:

1.Over-inspection: When degradation occurs at a slower rate than estimated, unnecessary inspections increase costs without a proportional risk reduction.

2.Under-inspection: When degradation is faster than anticipated, fixed intervals may lead to unplanned failures due to undetected deterioration.

While TBI remains a viable method under regulatory or operational constraints, its lack of adaptability makes it less efficient than RBI approaches. The ability to adjust inspection intervals based on evolving risk assessments, as seen in Scenarios 1 and 2, presents a more effective strategy for asset integrity management.

Risk-Based Inspection Planning and Assessment

The inspection planning results were analyzed based on the RBI methodology, considering factors such as inspection priority, scheduled inspection dates, PoF, and financial risk assessment. These factors play a crucial role in optimizing inspection intervals while ensuring asset integrity, minimizing operational risks, and reducing maintenance costs. Tab. 8 presents the results of this analysis, detailing the planned inspection schedules and associated risk metrics for the assessed aboveground storage tanks.

Table 8. Inspection planning results

Tab. 8 corresponds to Scenario 1, where inspection planning is conducted using the initial RBI methodology without modifications from alternative assessment strategies. This scenario establishes a baseline approach by prioritizing inspections based on calculated risk factors, including the PoF and financial risk.

The table presents key parameters that determine inspection scheduling. The inspection priority indicates the urgency of each inspection, ensuring that higher-risk tanks are assessed first. The next and last inspection dates provide a structured timeline for evaluating maintenance history and ensuring regulatory compliance. The installation date helps assess the long-term degradation of each tank.

The PoF, expressed in incidents per year, ranges from 0.04 to 0.1, indicating varying levels of structural risk. Tanks with higher PoF values require closer monitoring to reduce the likelihood of failure. The financial risk of failure, measured in dollars per year, ranges from $26,577 to $30,000, emphasizing the economic impact of unplanned failures and the importance of timely inspections.

By applying this structured RBI methodology, Scenario 1 provides a data-driven framework for optimizing inspection intervals, enhancing asset reliability, and minimizing maintenance costs.

Conclusion

This study highlights the effectiveness of RBI combined with advanced tank bottom scanning technologies in optimizing re-inspection intervals for ASTs in Kazakhstan. Compared to traditional TBI, RBI offers a more systematic and data-driven approach, improving safety, reducing maintenance costs, and allowing better resource allocation. The integration of NDT methods like TBIT enables early detection of corrosion and structural issues, enhancing inspection accuracy.

However, implementation challenges remain. Kazakhstan’s regulations are still largely prescriptive, requiring fixed-interval inspections and lacking probabilistic risk assessment integration. Limited industry awareness, insufficient training, and high investment costs in advanced technologies also hinder broader adoption. To enable a successful transition, regulatory updates, standardized risk thresholds, and workforce training are essential.

Future research should expand RBI to more facilities and explore integrating AI and machine learning for enhanced predictive maintenance. By adopting risk-based strategies and modern technologies, Kazakhstan can align with global standards, improve asset integrity, and ensure long-term industrial sustainability.

ADDITIONAL INFORMATION

Funding source. This study was not supported by any external sources of funding.

Competing interests. The authors declare that they have no competing interests.

Authors’ contribution. All authors made a substantial contribution to the conception of the work, acquisition, analysis, interpretation of data for the work, drafting and revising the work, final approval of the version to be published and agree to be accountable for all aspects of the work. The greatest contribution is distributed as follows: Zhanna Dyussenova – collection, processing, and analysis of experimental data, conducting research, detailed analysis and interpretation of results, manuscript writing; Abdugaffor Mirzoev – manuscript editing, analysis revision and review.

ДОПОЛНИТЕЛЬНО

Источник финансирования. Авторы заявляют об отсутствии внешнего финансирования при проведении исследования.

Конфликт интересов. Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с публикацией настоящей статьи.

Вклад авторов. Все авторы подтверждают соответствие своего авторства международным критериям ICMJE (все авторы внесли существенный вклад в разработку концепции, проведение исследования и подготовку статьи, прочли и одобрили финальную версию перед публикацией). Наибольший вклад распределён следующим образом: Дюсенова Ж. – сбор, обработка и анализ экспериментальных данных, проведение исследования, детальный анализ и интерпретация результатов, написание рукописи; Мирзоев А. – редактирование рукописи, вычитка и проверка выполненного анализа.

¹ «Мұнай және мұнай өнімдеріне арналған резервуарларды пайдалану және жөндеу кезіндегі өнеркәсіптік қауіпсіздікті қамтамасыз ету қағидаларын бекіту туралы» Қазақстан Республикасы Төтенше жағдайлар министрінің 2021 жылғы 15 маусымдағы № 286 бұйрығы. Қазақстан Республикасының Әділет министрлігінде 2021 жылғы 17 маусымда № 23068 болып тіркелді.

² Совместный приказ Министра нефти и газа Республики Казахстан от 25 августа 2011 года № 149 и и.о. Министра экономического развития и торговли Республики Казахстан от 31 августа 2011 года № 272. Зарегистрирован в Министерстве юстиции Республики Казахстан 12 сентября 2011 года № 7177 «Об утверждении критериев оценки степени риска в сфере частного предпринимательства в области проведения нефтяных операций».

³ СТ РК 3731-2021. Промышленность нефтяная и газовая. Техническое освидетельствование оборудования с учетом факторов риска

About the authors

Zhanna Ualiyeva

Author for correspondence.
Email: jannadyussenova0@gmail.com
ORCID iD: 0009-0004-7703-5145
Kazakhstan, Almaty

Abdugaffor Mirzoev

Email: gmirzoev@rosen-group.com
ORCID iD: 0009-0009-9416-8974
Kazakhstan, Almaty

References

Supplementary files

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1. JATS XML

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2. Figure 1. Overview of NDT methods used for AST

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3. Figure 2. RBI process [5]

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4. Figure 3. Example of Risk matrix generated in NIMA Integrity Management Software

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5. Figure 4. Comparison of Soil-Side, Product-Side, and Combined Corrosion Rates Across Storage Tanks

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6. Figure 5. Risk matrix at initial inspection date showing current risk levels across tanks based on PoF and CoF

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7. Figure 6. Risk matrix after re-inspection planning showing mitigated risk levels across all tanks

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8. Figure 7. Inspection planning chart for Scenario 1 (PoF target: 0.1 incidents/year)

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9. Figure 8. ISO-risk plot displaying distribution of tanks based on Probability of Failure and Financial Risk

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10. Figure 9. Inspection planning chart for Scenario 2 (PoF target: 0.3 incidents/year

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11. Table 4

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