Asset Integrity Management Research Articles

Predictive maintenance in oil and gas facilities, leveraging ai for asset integrity management

This paper explores the application of AI in predictive maintenance within oil and gas facilities, discussing its benefits, challenges, and future prospects. Through the integration of AI-driven analytics and real-time data monitoring, oil and gas companies can enhance their asset integrity management practices, ultimately driving cost savings and operational excellence. Predictive maintenance has become indispensable in the oil and gas industry, serving as a pivotal strategy to uphold operational efficiency and preserve asset integrity. This paper delves into the profound impact of artificial intelligence (AI) technologies on predictive maintenance, ushering in a new era of proactive equipment management. By harnessing AI capabilities, oil and gas companies can preempt equipment failures, curtail downtime, and refine maintenance protocols, thereby optimizing overall operational performance. The integration of AI in predictive maintenance marks a paradigm shift, offering a proactive approach to asset management. Leveraging AI-driven analytics and real-time data monitoring, oil and gas facilities can fortify their asset integrity management practices. Through predictive algorithms and machine learning models, these technologies empower companies to forecast equipment malfunctions with unprecedented accuracy, allowing for timely interventions and mitigating potential risks the benefits of AI-powered predictive maintenance in the oil and gas sector are multifaceted the future of predictive maintenance in the oil and gas industry is brimming with promise. As AI technologies continue to evolve, we can anticipate further advancements in predictive analytics, fault detection, and decision support systems. By embracing innovation and collaboration, oil and gas companies can harness the full potential of AI-driven predictive maintenance, cementing their position as industry leaders in asset management and operational efficiency.

OPERATIONAL READINESS AND ASSURANCE (OR&A) IN ASSET INTEGRITY: STRATEGIES FOR EXCELLENCE IN FACILITY MANAGEMENT

Operational readiness and assurance (OR&A) play a pivotal role in ensuring the integrity and reliability of assets within the oil and gas sector. This review explores methodologies and best practices aimed at achieving excellence in facility management, with a focus on meticulous operational readiness and assurance within the asset integrity framework. The review delves into the critical importance of OR&A in mitigating operational risks, enhancing safety, and maximizing asset performance throughout their lifecycle. It examines the key components of effective facility management, including maintenance planning, risk assessment, compliance management, and emergency preparedness. Furthermore, the review highlights the methodologies and strategies employed to ensure operational readiness and assurance, emphasizing the significance of proactive maintenance, robust inspection protocols, and data-driven decision-making. It discusses the role of advanced technologies such as predictive analytics, condition monitoring systems, and digital twins in enhancing operational efficiency and asset reliability. Moreover, the review underscores the importance of a holistic approach to facility management, encompassing people, processes, and technology. It examines case studies and industry examples that demonstrate successful implementation of OR&A strategies, showcasing tangible benefits such as improved uptime, reduced downtime, and optimized resource utilization. Overall, this review underscores the critical role of operational readiness and assurance in asset integrity management within the oil and gas sector. By adopting meticulous facility management practices and leveraging advanced technologies, organizations can enhance operational excellence, mitigate risks, and achieve sustainable performance across their asset portfolios.
 Keywords: OR&A, Asset Integrity, Strategies, Excellence, Facility Management.

Cyber–physical system architecture of autonomous robot ecosystem for industrial asset monitoring

Driven by advancements in Industry 4.0, the Internet of Things (IoT), digital twins (DT), and cyber–physical systems (CPS), there is a growing interest in the digitalizing of asset integrity management. CPS, in particular, is a pivotal technology for the development of intelligent and interconnected systems. The design of a scalable, low-latency communication network with efficient data management is crucial for connecting physical and digital twins in heterogeneous robot fleets. This paper introduces a generalized cyber–physical architecture aimed at governing an autonomous multi-robot ecosystem via a scalable communication network. The objective is to ensure accurate and near-real-time perception of the remote environment by digital twins during robot missions. Our approach integrates techniques such as downsampling, compression, and dynamic bandwidth management to facilitate effective communication and cooperative inspection missions. This allow for efficient bi-directional data exchange between digital and physical twins, thereby enhancing the overall performance of the system.This study contributes to the ongoing research on the deployment of cyber–physical systems for heterogeneous multi-robot fleets in remote inspection missions. The feasibility of the approach has been demonstrated through simulations in a representative environment. In these experiments, a fleet of robots is used to map an unknown building and generate a common 3D probabilistic voxel-grid map, while evaluating and managing bandwidth requirements. This study represents a step forward towards the practical implementation of continuous remote inspection with multi-robot systems through cyber–physical infrastructure. It offers potential improvements in scalability, interoperability, and performance for industrial asset monitoring.

Development of a methodology for asset integrity management in Universities and Research Centers

This article describes the application of a differentiated methodology that I developed to enhance and facilitate asset management work in renowned universities and research centers. Frequently, important scientific studies and research are interrupted or wasted due to unexpected failures in equipment and infrastructure systems provided by universities and research centers for researchers’ use. To address this issue, this methodology incorporates structured concepts and practices commonly employed in the field of asset integrity. These are uniquely integrated into comprehensive engineering assessment reports developed over several years during my fieldwork in various countries. This integration occurs within a computerized maintenance management system. The ultimate goal of this process is to maximize the reliability and performance of critical systems and equipment used in the infrastructure and conduct of highly relevant research at the national and international levels. Through case studies conducted at two major universities in the United States, the efficiency of this innovative methodology was analyzed quantitatively and qualitatively. As presented in this article, the main results obtained include a reduction in the number of unplanned repairs or shutdowns, improved quality and efficiency of maintenance programs, increased reliability and lifespan of critical research equipment and systems, an increase in the number of assets with an appropriate maintenance plan, and the enhancement of computerized maintenance management systems used by these institutions. These results translate into faster completion of scientific studies and research with more reliable data.

General education and training of NDT personnel, including NDT Education at Universities. in South Africa

Following the 16th World Conference in Durban, South Africa, The South African Institute for NDT (SAINT) was mandated to invest the monies made from the WCNDT to establish a professional qualification for NDT personnel. In South Africa, a professional qualification must comply with the national qualification framework (NQF) requirements, which stipulate the rules to assure that qualifications for different occupations are at the same level. For NDT this meant, that the qualifications to ISO 9712 (ISO TC 25107 syllabus) would not suffice to serve as a qualification. There exists a requirement that classroom training, classroom practical training and work integrated practical training should each make up at least 30 % of the work. Anybody who had a look at ISO 9712 will realise that 160 hours of classroom training and 180 days (1080 hours) practical training would not cut it, as the training content for the 1080 hours is not defined in any standard. This state of affairs required the definition (curriculum development) of what has to be taught and how. The paper will elucidate this outcome. Concerning university education in South Africa one has to mention the NDT diploma and advanced diploma in NDT offered by the Vaal University of Technology (VUT). These courses focus on teaching NDT theory across a broad spectrum. Students have to undergo work integrated learning (WIL) and then successfully pass the practical examination at NDT Level II in order to be certificated to perform NDT in line with codes and standards. The Tshwane University of Technology (TUT) offer a subject in NDT as part of the applied physics diploma, where the emphasis is on Nuclear Technology and Photonics. At the University of Cape Town (UCT) in the department of mechanical engineering, digital shearography is offered as a research field for master degree studies. They have a well-equipped optical laboratory for this purpose and have manufactured a shearography system which they support and improve on an ongoing basis. At the University Pretoria (UP) the subject Non-destructive Testing was offered at the post graduate degree level, as part of the course work for Asset Integrity Management. Most practical NDT technology development in South Africa is performed in industry at the company’s own costs. Some examples will be mentioned.

Integrating Life Cycle Cost Analysis into Pipeline Asset Integrity Management: A Comprehensive Approach in Decision Support Systems

Integrating Life Cycle Cost Analysis into Pipeline Asset Integrity Management: A Comprehensive Approach in Decision Support Systems

Probabilistic residual strength assessment of smart composite aircraft panels using guided waves

Typical prognosis methods in Structural Health Monitoring (SHM) link the degradation mechanisms to the extracted health indicators. Such approaches however do not provide estimations on the current strength loss when a new damage is detected. This study presents a framework that enables the SHM-informed asset integrity management of composites. First, the integrated guided wave based SHM system is used to inform on the existence, location and size of the damage and subsequently, the residual strength of the structure is predicted. The framework combines physics-based and data-driven models to mitigate the incompleteness of the former and address issues relating to the representativeness in the training datasets of the later. Detailed finite element models are used to create a digital representation of the structure while a building-block program is initiated to quantify and propagate the uncertainties observed experimentally. A 1.6 m composite panel with a skin-stringer delamination, equipped with a network of 24 piezoelectric transducers, is used as a case study to demonstrate the prognostic capabilities of the framework. The framework estimated the damage size with a mean absolute percent error (MAPE) of approximately 10% while the residual strength of a destructively tested damaged panel was predicted with a MAPE of 5%.

Review of synergy between machine learning and first principles models for asset integrity management

The chemical process industry (CPI) accumulated a rich data asset through industrial 4.0. There is a strong drive to develop and utilize effective approaches for process performance prediction and improvement, process control, sensor development, asset management, etc. The synergy between machine learning and first principles models can bring new insights and add tremendous value to the CPI. This paper reviews various applications of the synergies towards asset integrity management. An overview of some related commercial software packages are also provided.

Asset integrity management in mature process plants

AbstractSeveral plants in Brazil have mature assets or use equipment that comes from decommissioned facilities. In addition, the culture of process safety in Brazil is an under‐researched topic that has only recently begun to attract corporate attention. Since catastrophic accidents are not very common, any accident related to the process can result in very large losses in terms of damage to equipment and loss of human and social capital. In mature process plants, these issues are even more sensitive because their organization may not yet have developed an effective integrity management program. Nevertheless, it is a proven fact that the assets of these plants are often nearing the end of their residual lifetime. Therefore, the present work aims to develop a high‐level checklist to evaluate the mechanical integrity and identify possible failures in the integrity management of vessels and tanks, based in good engineering practices, Regulatory Standard 13 (NR 13), API 579‐1/ASME FFS‐1, API RP 580, API RP 581, HSE (Health and Safety Executive) requirements, and other relevant standards as references. The checklist ts divided into six sections, namely (1) Brazilian Regulatory Standard 13 (NR 13); (2) Design and manufacturing; (3) Operation; (4) Management systems; (5) Deterioration mechanism; and (6) Risk‐based inspection, as the most relevant criteria for evaluating the integrity of mature pressure vessels and tanks.

IMPLEMENTATION OF MAINTENANCE AND RELIABILITY MANAGEMENT SYSTEM (MRMS) IN PERTAMINA HULU ENERGY SUBHOLDING UPSTREAM (PHE SHU) THROUGH FIELD ASSESSMENT OF ISO 55001

As part of the integrated efforts that need to be made to achieve the target of becoming a world-class energy company, PHE SHU seeks to carry out the implementation of the Asset Integrity Management System (AIMS) and develop a MRMS which involves a solid team and is able to complete implementation in a timely, effective, efficient and highly competitive manner, to achieve a production target of 1 million bbl/day and 12 bcf/day in 2030 for national energy security referring to the international standard ISO 55001. MRMS is a reference for the governance of the reliability program and maintenance of production facility assets in the Upstream Subholding environment in the operational phase of the Pertamina Group asset management life cycle. identification of Failure Mode Effect Analysis (FMEA) preparation of maintenance strategy programs in the form of task selection for failure mode control based on studies in FMEA namely preparation of detection work programs in rotating equipment in the form of Predictive Maintenance (PdM) activities, task selection of detection programs in static equipment in the form of RBl activity. Task selection prevention in the form of Preventive Maintenance (PM) activities. Failure finding (function test), including determining time intervals for carrying out inspection activities for the PdM, RBI, PM, function test, ORDC (Operator Routine Duty Check List) programs based on the historical failure rate stated in PAREDA in ensuring that Upstream Oil and Gas Assets are optimized, sustainable, safe & reliable operations.

Qualification of digital twins for predicting failure in welded structures

Significant cost savings in Asset Integrity Management (AIM) are achieved by replacing condition-based with predictive-based maintenance strategies. However, this requires the gathering and analysis of huge amounts of real-time data from Structural Health Monitoring (SHM) sensors and periodic Non-Destructive Testing (NDT). This presents a problem that lends itself to Digital Twin (DT) Technology. In the case of process plant, DT technology has already been implemented using “Big-pipe” data from Condition Monitoring (CM) sensors to predict failures in pumps, compressors and other machinery on a company-wide scale. In the case of welded structures however, for example pressure vessels, pipelines and storage tanks, implementation of DT technology is hampered by a lack of confidence in the use of “Smallpipe” data from periodic NDT and continuous SHM. In “Big-pipe” data, data streams are very rapid flowing, with information directly available from the data. In “Small-pipe” data, data streams are very slow, accumulated over long periods of time and require careful interpretation to provide information for Engineering Critical Analysis (ECA) that is essential for predicting failure in the asset. This presentation will describe TWI’s approach to developing a DT for predicting failure in welded structures.

A probabilistic model for quantifying uncertainty in the failure assessment diagram

Failure assessment diagrams are an integral component of asset integrity management in a variety of industrial sectors. They allow for the assessment of the significance of cracks in structures, between the domains of brittle fracture and plastic collapse. Numerical modelling, or empirically determined assessment lines across this continuum, are the basis of the guidance in current industrial standards. However, as the requirement of such assessments progresses from demonstrating safety to optimising resilience and resource allocation, it will become necessary to compute more informative (probabilistic) estimates, which are compatible with decision analysis.In this paper, Bayesian regression models are proposed as a suitable method of quantifying (aleatory and epistemic) uncertainty in the limit state on a failure assessment diagram. The data considered in this study consists of laboratory tests completed on wide plate fracture specimens. This work is intended to address the inconsistencies in current editions of industrial standards, and limitations (regarding flexibility and application) of existing scientific literature on the topic.Potential applications are discussed, including the use of fracture mechanics in meaningful reliability analysis, quantitative risk management, and optimising experimental design for future material tests.

Reliability of quantitative risk models: a case study from offshore gas production platform

In response to the competing factors governing the operation of oil and gas facilities, i.e., the stringent safety and environmental regulations, and the challenging business environment that entails minimizing the running cost, a risk-based inspection (RBI) program became a vital part of all Asset Integrity Management (AIM) frameworks. The objective is to ensure asset mechanical integrity while optimizing the maintenance and inspection resources and minimizing production downtime. There are different risk models being used to manage operational risk for equipment. The decision-maker should be attentive to the subjectivity and reliability of the risk results to establish an adequate risk target that can achieve the ultimate goal of RBI by determining the cost-effective inspection and maintenance plan without compromising plant safety, integrity or reliability. This paper presents evaluations of the most quantitative RBI models through a case study from an offshore gas producing platform. A case study was implemented for topside equipment on an offshore platform. The study analyzed the impact of contributing factors to the probability of failure (PoF) model through a sensitivity analysis to quantify the reliability and subjectivity in the failure probabilities. A sensitivity analysis and comparison between both API consequence modelling methodologies (i.e., CoF level 1 and 2) were performed to manifest the reliability of risk results. The sensitivity analysis revealed the variance in the calculated risk and demonstrated that a risk target/threshold should be established based on the deployed risk model. Using the same risk target for different risk models cannot effectively define all equipment items that actually need more resources to mitigate the risk. And can result in omitting critical equipment which can jeopardize asset integrity and lead to major losses, or spend resources on unnecessary equipment.

Towards resilience of offshore wind farms: A framework and application to asset integrity management

This paper develops a general probabilistic framework for resilience modeling and analysis of offshore wind farm (OWF), and illustrates how such a framework may be implemented within the modeling techniques and tools commonly applied in the industry. Based on this framework the significance of prevailing uncertainties and the effects of different decision alternatives relevant in the context of asset integrity management (AIM) are studied and discussed. In the framework, OWFs are modeled as system-of-systems by a hierarchical model where the life-cycle performances of each system, as well as the dependencies between these systems, are represented probabilistically. The quantification of resilience is undertaken based on a scenario-based modeling of life cycle benefits and costs in which resilience failure is defined as the exhaustion of the economic capacity accumulated by the system over time. Moreover, this paper introduces resilience-informed decision-making for OWF in the context of AIM. The proposed framework is applied to the OWFs populated with NREL 5MW offshore wind turbines (OWTs). Events of typhoon-induced waves and winds are considered as the two random environmental load processes affecting the OWF’s dynamic responses and for which their resilience performances are carried out. Finally, the resilience performances of the OWFs are studied and discussed for a range of decision alternatives relevant to AIM.

Engineering Poster E11: Using unsupervised machine learning to identify risk of failure at ageing oil and gas assets

Poster E11 Asset integrity management of ageing oil and gas assets is an ongoing challenge. This paper uses unsupervised algorithms (i.e. clustering technique) to identify carbon steel piping with increased probability of failure due to various internal corrosion mechanisms. The application used over 20 variables including wellhead planktonic bacterial counts, Fe2+ levels, oil and water production rates, historical Non-Destructive Testing (NDT) records, remaining life of downstream equipment, previous remediation data and geographical location data. An unsupervised machine learning clustering algorithm was written grounded in mathematical techniques of Principal Component Analysis (PCA) and k-means clustering. The probabilistic algorithm identified implicit patterns, which were then used to identify critical and non-critical piping clusters. Outputs from the clustering model were used to prioritise field measurements, and while these are ongoing there appears to be a good agreement with model predictions. The paper further discusses the measures that have a higher impact on the classification accuracy of the algorithm. To access the poster click the link on the right. To read the full paper click here

Numerical Analysis of Surface Rolling Effects on Fatigue Life Enhancement of Wire Arc Additively Manufactured Parts

With the advancements in additive manufacturing (AM) technologies, it is expected that fast and efficient production using the AM techniques will gradually replace the traditional manufacturing processes. An important consideration in the design and life assessment of AM-built parts is the asset integrity management and in particular fatigue life enhancement of such components. Surface rolling treatment is known to be an efficient way to introduce deep compressive residual stresses into engineering components, and therefore it has been considered as a practical and effective life enhancement technology. In this study, the surface rolling effects on the fatigue life enhancement of wire arc additively manufactured (WAAM) parts have been investigated. For this purpose, a compact tension specimen geometry was modeled in ABAQUS and the rolling process was simulated by means of finite element simulations to predict the extent of compressive residual stresses induced into a WAAM built part. The simulation results have been discussed in terms of the beneficial effects of surface rolling on fatigue life enhancement of WAAM built parts.

Using unsupervised machine learning to identify risk of failure at ageing oil and gas assets

Asset integrity management of ageing oil and gas assets is an ongoing challenge. This paper uses unsupervised algorithms (i.e. clustering technique) to identify carbon steel piping with increased probability of failure due to various internal corrosion mechanisms. The application used over 20 variables including wellhead planktonic bacterial counts, Fe2+ levels, oil and water production rates, historical Non-Destructive Testing (NDT) records, remaining life of downstream equipment, previous remediation data and geographical location data. An unsupervised machine learning clustering algorithm was written grounded in mathematical techniques of Principal Component Analysis (PCA) and k-means clustering. The probabilistic algorithm identified implicit patterns, which were then used to identify critical and non-critical piping clusters. Outputs from the clustering model were used to prioritise field measurements, and while these are ongoing there appears to be a good agreement with model predictions. The paper further discusses the measures that have a higher impact on the classification accuracy of the algorithm.

A unifying review of NDE models towards optimal decision support

Non-destructive evaluation (NDE) inspections are an integral part of asset integrity management. The relationship between the condition of interest and the quantity measured by NDE is described with probabilistic models such as PoD or ROC curves. These models are used to assess the quality of the information provided by NDE systems, which is affected by factors such as the experience of the inspector, environmental conditions, ease of access, and the precision of the measurement device. In this paper, we review existing probabilistic models of NDE andshow how they are connected within a unifying framework. This frameworkprovides insights into how these models should be learned, calibrated, and applied. We investigate and highlighthow the choice of the model can affect the maintenance decisions taken on the basis of NDE results. In addition, we analyze the impact of experimental design on the performance of a given NDE system in a decision-making context.

A New Area of Utilizing Industrial Internet of Things in Environmental Monitoring

Due to environmental regulations continually reducing emission quantity allowed over time, there is a growing need for adaptable and feasible environmental monitoring, such as emission, wastewater quality, and air pollution monitoring, for the process industry (and surrounding communities). Alternative environmental monitoring and process monitoring technologies based on industrial internet of things (IIoT) and artificial intelligence (AI) enable the process industry to take a proactive approach toward the environment and asset integrity management. The monitoring devices can be deployed in a stationary or dynamic manner. In this study, the emerging trend and various applications of IIoT and advanced data analytics methodologies in environmental monitoring are reviewed. An example showing challenges and research needs in sensor placement is given. Future directions in technology, regulation, and application have been discussed as well.

Dynamic cost-based integrity assessment of oil and gas pipeline suffering microbial induced stochastic degradation

This research paper presents an adaptive methodology that integrates the Bayesian network (BN) with the Monte Carlo algorithm for the cost-based assessment of an offshore asset in a harsh corrosive environment. The BN explores the various integrity influential factors from design to failure of an offshore pipeline, considering the dynamic interrelationships among core elements. This is defined based on the unstable and stochastic nature of the degradation influential variables, such as those in microbial corrosion mechanisms. The model captures the variability in the cost parameters using the dynamic discretization formalism in the BN structure to predict the corrosion integrity cost for predefined offshore operations. The Monte Carlo algorithm is used to forecast the failure characteristics of the asset based on the formulated failure functions. The cost function output is used as the baseline cost rate estimator for the multiple failure modes' cost prediction for the period under consideration. The proposed methodology is demonstrated with a subsea pipeline. The outcomes reveal the effect of the cost elements’ variability and the corrosivity index on the overall integrity management strategy. The proposed adaptive approach provides a cost-based asset integrity management tool for offshore operations suffering stochastic degradation.