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Effective management of assets in the oil and gas industry is vital in ensuring equipment availability, increased output, reduced maintenance cost, and minimal nonproductive time (NPT). Due to the high cost of assets used in oil and gas production, there is a need to enhance performance through good assets management techniques. This involves the minimization of NPT which accounts for about 20-30% of operation time needed from exploration to production. Corrosion contributes to about 25% of failures experienced in oil and gas production industry, while more than 50% of this failure is associated with sweet and sour corrosions in pipelines. This major risk in oil and gas production requires the understanding of the failure mechanism and procedures for assessment and control. For reduced pipeline failure and enhanced life cycle, corrosion experts should understand the mechanisms of corrosion, the risk assessment criteria, and mitigation strategies. This paper explores existing research in pipeline corrosion, in order to show the mechanisms, the risk assessment methodologies, and the framework for mitigation.

A multivariate regression analysis and time-dependent maximum pit depth growth model was developed using maximum pit depths and pipeline operational parameters, which includes temperature, CO 2 partial pressure, flow rate, pH, sulfate ion, chloride ion and water cut. This statistical analysis, which used ten years Ultrasonic Thickness Measurement (UTM) data from non-piggable onshore pipelines, accounted for different categories of maximum pitting rates -low, moderate, high and severe. The model was validated using field data from twelve operating pipelines and the results agreed well with the field data.

Nanostructures are playing significant roles in the development of new functions and the enhancement of the existing functions of industrial devices such as sensors, transistors, diodes, lithium-ion batteries, and photovoltaic cells, due to their piezoelectricity, biocompatibility, and pyroelectrical characteristics. This research focused on the review of the characteristics of different nanostructures and nanomaterials such as ZnO, ZnS, MoS2, GO, TiO2, SnO2, and Fe2O3, their fabrication techniques, growth mechanisms, and applications. The factors affecting the growth mechanisms, the crystallographic natures, growth models of anisotropic nanostructures, and growth of nanocrystals, were also highlighted in this research. The existence of lattice mismatch, differential thermal expansion, and high deposition temperature, have affected uniform deposition of nanoparticles on substrates and caused heteroepitaxy, which has resulted in defective nanostructures. Although heteroepitaxy has negatively affected the characteristics and potential applications of nanostructures, it has also opened new research frontiers for potential new applications of nanostructures. The use of nanostructures for gas sensing is attributed to the high specific area, change of resistance on exposure to gases, and high photoconduction abilities, while the photon-carrier collection abilities and anti-reflectance qualities are vital for solar photovoltaic cells. Nanostructures have also been used as coating pigments to prevent corrosion of facilities, reduce urban heat islands and energy consumptions, due to the near infrared (NIR) reflective characteristics.

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A continuous time non-homogenous linear growth pure birth Markov model was used to predict the future pit depth distribution of internally corroded oil and gas pipelines. A negative binomial distribution was used for calculating the transition probability functions of the pit depths whilst pit depths growth was estimated for low, moderate, high and severe pitting corrosion rates using field measured data of pit depths, temperatures, CO2 partial pressures, pH and flow rates. The Markov predicted results agreed well with field measured pit depth data from X52 grade pipeline and L-80 and N-80 grades offshore well tubing.

To estimate the internal pit depth growth and reliability of aged oil and gas pipelines, a Monte Carlo simulation approach was adopted. The average maximum pit depths of corroded pipelines were correlated with the operating parameterstemperature, CO 2 partial pressure, pH, flow rate, sulfate ion concentration, chloride ion concentration, water cut, and wall shear stress via a multivariate regression analysis. Poisson square wave model was used to predict the time lapse of the pit depth growth using the statistical best fit of the maximum pit depth and operating parameters as boundary conditions. Weibull probability function was used to determine the failure intensity and survivability of the pipelines for different distribution types, whereas inspection data from a magnetic flux leakage in-line inspected transmission pipeline were used to test the application of the model. The future pit depth distribution, survivability, and failure rate of this transmission pipeline were also determined, with the result showing that the model is vital for future internal pit depth growth and reliability estimation from single field inspection data.

Digital twins, succinctly described as the digital representation of a physical object, is a concept that has emerged relatively recently with increasing application in the manufacturing industry. This article proposes the application of this concept to the healthcare domain to provide enhanced clinical decision support and enable more patient‐centric, and simultaneously more precise and individualized care to ensue. Digital twins combined with advances in Artificial Intelligence (AI) have the potential to facilitate the integration and processing of vast amounts of heterogeneous data stemming from diversified sources. Hence, in healthcare this can provide enhanced diagnosis and treatment decision support. In applying digital twins in combination with AI to complex healthcare contexts to assist clinical decision making, it is also likely that a key current challenge in healthcare; namely, providing better quality care which is of high value and can lead to better clinical outcomes and a higher level of patient satisfaction, can ensue. In this focus article, we address this proposition by focusing on the case study of cancer care and present our conceptualization of a digital twin model combined with AI to address key, current limitations in endometrial cancer treatment. We highlight the role of AI techniques in developing digital twins for cancer care and simultaneously identify key barriers and facilitators of this process from both a healthcare and technology perspective. This article is categorized under: Application Areas > Health Care