Fiscal stabilisation in real time: an exercise in risk management

In recent years, there has been increasing talk of the rapid entry of artificial intelligence into risk management. All the benefits it would bring over the whole process are often commented on: real-time results, processing large amounts of data, more complete risk identification, more accurate risk assessment, etc. There are also negative moods that make various experts feel threatened by their need to be replaced by artificial intelligence. Another problematic issue that arises is related to the transparency of algorithms and the increase in cyber risks [6]. This material aims to identify the individual elements at the stages of risk management in which artificial intelligence (AI) can and should be applied alone, in combination with expert opinion or not. Here it is shown that because of the use of AI the efficiency of the whole process is significantly increased, first of all by conducting in-depth analyses, and the decisions are made by the risk management experts. This proves its usefulness and increases the confidence of experts in it.

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Review of the Configuration Risk Management Methodologies

10.1115/icone28-64281 ◽

2021 ◽

Author(s):

Yuhang Zhang ◽

Zhijian Zhang ◽

He Wang ◽

Lixuan Zhang ◽

Dabin Sun

Keyword(s):

Risk Management ◽

Real Time ◽

Nuclear Power ◽

Safety Assessment ◽

Power Plants ◽

Nuclear Power Plants ◽

Probabilistic Safety Assessment ◽

Technical Specifications ◽

Risk Monitor ◽

Probabilistic Safety

Abstract To ensure nuclear safety and prevent or mitigate the consequences of accidents, many safety systems have been set up in nuclear power plants to limit the consequences of accidents. Even though technical specifications based on deterministic safety analysis are applied to avoid serious accidents, they are too poor to handle multi-device managements compared with configuration risk management which computes risks in nuclear power plants based on probabilistic safety assessment according to on-going configurations. In general, there are two methodologies employed in configuration risk management: living probabilistic safety assessment (LPSA) and risk monitor (RM). And average reliability databases during a time of interest are employed in living probabilistic safety assessment, which may be naturally applied to make long-term or regular management projects. While transient risk databases are involved in risk monitor to measure transient risks in nuclear power plants, which may be more appropriate to monitor the real-time risks in nuclear power plants and provide scientific real-time suggestions to operators compared with living probabilistic safety assessment. And this paper concentrates on the applications and developments of living probabilistic safety assessment and risk monitor which are the mainly foundation of the configuration risk management to manage nuclear power plants within safe threshold and avoid serious accidents.

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Successful Collision Risk Management with Realtime Advanced Survey Correction in A Challenging Horizontal Well

10.2523/iptc-21858-ms ◽

2021 ◽

Author(s):

Tongkum Tossapol ◽

Khamawat Siritheerasas ◽

Feras Abu Jafar ◽

Trinh Dinh Phu ◽

Pham Nam Hieu

Keyword(s):

Risk Management ◽

Real Time ◽

Separation Factor ◽

3D Visualization ◽

Management Technique ◽

Collision Risk ◽

Time Saving ◽

Complex Design ◽

Measurement While Drilling ◽

Difficulty Index

Abstract The Well X in Nong Yao field, is an infill-well designed for the Gulf of Thailand which presented several interesting challenges due to its complexity, tortuosity, and potential collision risks with other wells. This paper demonstrates the application of a Real-time Advanced Survey Correction (RASC) with Multi Station Analysis (MSA) to correct the Measurement While Drilling (MWD)'s azimuth. The Well X is a 3D Complex design with a high drilling difficulty index (DDI) at 6.9, high tortuosity of 316 degree, and which has an aggressive build on inclination and azimuthal U-turning well path. The well also creates difficult doglegs severity (DLS) up to 5.5deg/100ft, which is near the limit of the flexibility required to achieve the horizontal landing point. The conventional MWD survey, with proximity scanning with the nearby Well A, demonstrates high risk with a calculated Oriented Separation Factor (OSF) of 1.01. The RASC-MSA method is applied with a clearly defined workflow during execution in real-time and provide significant improvement in calculated OSF. RASC-MSA is applied for every 1,000 ft interval drilling below the 9.625in casing shoe. The workflow ensures that the directional driller follows the corrected survey along the well path and especially in the last 300 ft before reaching the electrical submersible pump (ESP) tangent section. The result from RASC-MSA, indicated a 29 ft lateral shift on the left side of the MWD standard surveys. Without this technique, Well X has a high potential to collide with Well A and Well B (Figure 1) as the actual OSF may less than 1 while drilling. The final 3D Least Distance proximity scanning with Well A shows a minimum OSF = 1.35, which is a 30% improvement compared to the conventional MWD survey. Another nearby well, Well B, indicates a minimum OSF=1.66 and passed the anti-collision OSF rule. In consideration of the drilling efficiency, availability, cost effectiveness and time saving, the RASC-MSA analysis to correct the MWD's azimuth are applied and the separation factor can be improved by 30%. In conclusion, the collision risk management technique applied successfully met the complex challenges of Well X, which was successfully drilled and safely delivered. Figure 1 3D visualization to exhibit the collision issue between Well X and nearby existing Wells A and Well B.

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Collaboration and the Extended Workplace: Real-Time Productivity Gains, Real-Time Risk Management

ISSE 2005 — Securing Electronic Business Processes ◽

10.1007/978-3-322-85237-3_35 ◽

2005 ◽

pp. 335-344

Author(s):

Jack Nagle

Keyword(s):

Risk Management ◽

Real Time ◽

Productivity Gains

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Real Time Risk Management in Cloud Computation

Security Engineering for Cloud Computing ◽

10.4018/978-1-4666-2125-1.ch007 ◽

2013 ◽

pp. 123-145

Author(s):

Kashif Kifayat ◽

Thar Baker Shamsa ◽

Michael Mackay ◽

Madjid Merabti ◽

Qi Shi

Keyword(s):

Information Technology ◽

Cloud Computing ◽

Risk Management ◽

Real Time ◽

Use Case ◽

The Real ◽

Security Issues ◽

Processing Resources ◽

Cloud Computation ◽

Time Assessment

The rise of Cloud Computing represents one of the most significant shifts in Information technology in the last 5 years and promises to revolutionise how we view the availability and consumption of computing storage and processing resources. However, it is well-known that along with the benefits of Cloud Computing, it also presents a number of security issues that have restricted its deployment to date. This chapter reviews the potential vulnerabilities of Cloud-based architectures and uses this as the foundation to define a set of requirements for reassessing risk management in Cloud Computing. To fulfill these requirements, the authors propose a new scheme for the real-time assessment and auditing of risk in cloud-based applications and explore this with the use case of a triage application.

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