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The topic is Risk Management Strategies in Mega Projects, Oil and Gas. The previous part is attached. The instructions are in a Word file attached. Notes are also attached.
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ASSIGNMENT: TWO
Literature Review: Gaining Empirical Knowledge
SUBMISSION DATE: 8th March 2024
Find a minimum of Fifteen (15) recent journal articles (between 2018 to 2022)
that have information content related to your research area. Some of the
journals should have MSU citations/research publications (can refer to
https://www.lens.org/). Read the articles and answer the following questions:
1. Read the literature review relevant to your research. Use the keywords as
your guide.
2. Summarize your literature in a table comprising of the items below,
a. Author and Year published
b. Problems / Purpose / Aims
c. Theories
d. VARIABLES
e. Findings
f. Limitations and Future Studies
g. Conclusions
3. List of references (APA 7th Edition).
Example:
Author /
Year
Problem
/
Purpose
/ Aim
Theory
Variables
Limitations
and
Findings
Conclusions/synthesize
Future
Study
(15%)
Task:
You are required to check each assignment accordingly.
Guidelines:
1. You should write in proper academic English
2. All assignment must include Cover Page (CP), Table of Contents
(TOC) and References (Ref).
3. All research ideas must be within MSU 25 Key Research Areas (refer
to https://msu.edu.my/25-key-research-area)
4. No maximum page limits.
Appropriate Style:
1. File Types: Word.
2. Font: 12 point, Times New Roman
3. Text: 1.5 Spacing, Block Aligned (Justified)
4. Referencing Style: APA 6th Edition Format
5. Page Layout: 1-inch margins on all sides with page numbers at the
bottom centered and no header/footer
Graduate School of Management (GSM)
ASSIGNMENT COVER SHEET
***This cover sheet must be stapled to the front of your assignment
Office Use Only
Program & Intake
Assignment Received
Module Code & Name
Lecturer Name
□ Before due date & time
□ Late Submission
Assignment Title
Please note that it is your responsibility to retain copies of your assignment. Copying someone else’s work is
plagiarism and is unacceptable. The University may impose severe penalties for plagiarism. All work must be
submitted by the due date. If an extension of work is granted, this must be specified with the signature of the
lecturer/tutor.
Name & Signature □ Before due date & time
Extension granted until (date)
of lecturer/tutor
□ Late Submission
For Individual Assignment
I declare that all material in this assignment is my own work except where there is clear acknowledgement or
reference to the work of others, and I have complied and agreed to the University statement on Plagiarism and
Academic Integrity.
Student Name as per I/C
Matrix Number
Signature
For Group Assignment
We declare that all material in this assignment is our own work except where there is clear acknowledgement
or reference to the work of others, and we have complied and agreed to the University statement on Plagiarism
and Academic Integrity. By signing the cover sheet, group members are indicating that they agree that each
member of the group made a fair and reasonable contribution to the assignment or project.
Student Name as per I/C
Office Use Only
Lecturer Name
Marking Comment & Feedback
Results/ Grade
Copyright © Management & Science University (MSU)
Matrix Number
Signature
RUBRIC
Attribute
Poor
Below
average
Satisfactory
Good
Excellent
Originality
(30 marks)
Just cut and
paste
Minimal effort
to indicate
originality
Modifications
into ‘own’
effort
Shows effort
of producing
originality
work
High
evidence of
original work.
Due credit is
given to the
original
source.
5
10
15
20
30
Content
Richness
(30 marks)
Inability to
synthesize
Not enough
points of
argument
Acceptable
effort
The content
is more than
adequate
Very
competence
5
Shoddy work
10
Not well
planned
15
Evidence of
linkage
between
points of
argument
20
The good
linkage
between
points of
arguments
30
Very well
thought out
1
5
10
15
20
Poor
expressions
and full of
jargon
Poor
command of
language
Uneven
Simple and
easy
Good
journalism
2
4
6
8
10
Full of
grammatical
error
Too many
grammatical
error
Acceptable
level of
grammatical
error
Minimal
grammatical
error
No
grammatical
error
2
4
6
8
10
Flow of
Ideas
(20 marks)
Writing
Style
(10 marks)
Grammar
(10 marks)
Total = 100
Score
/100
The Rubric page should be the last page in your submission
Chapter 2
Literature Review
AP DR. ARUN KUMAR TAROFDER
MANAGEMENT AND SCIENCE UNIVERSITY
1
Component of Chapter 2
• Details About the Area of your Research
• Theoretical Foundation
• Empirical Foundation
• Developing Conceptual Model
• Explain the relationship between variables in the
model with appropriate prior studies.
• Develop hypothesis.
• Summary of the chapter.
2
Literature Survey
• The preliminary information gathering and data analysis
might give a good idea(s) about the real problem. However,
surveying the literature will help the researcher to see how
others have perceived such factors or problem in other work
setting and defined the problem.
3
Sources
• Book
• Bibliographies
• Journals
• Government Reports
• Research Abstracts
• Thesis
• News papers
• Magazines
4
Purposes of LR
• To identify other people working in this area
• To gain breath of knowledge of your study area.
• To identify opposing views
• To identify appropriate methodology, research design, method of measuring
concepts and technique of analysis.
5
Presentation of the Review
• By Chronological order
• By Topic
• Problem – Solution
• Cause – effect
• Argument and Counter argument
• Group on the basis of a particular Variable
6
By Topic
• Topic: Factors Determining Productivity of the
workers
• Education and Productivity
• Training and Productivity
• Work environment and productivity
• Job Satisfaction and Productivity
7
Theoretical Foundation
• Select the most appropriate theory related to your
problem.
• Give details about the theory including the key
points of the theory.
• Justify the appropriateness of this theory for your
study.
8
Empirical Study
9
Conceptual Model
10
Basic Concepts
• Concepts
• Fact
• Variables
• Theory
• Laws
• Model
11
Types of Variables
• Dependent Variables
• Independent Variables
• Moderating Variables
• Intervening Variables
12
13
14
15
Theory
• A theory can be defined as an interrelated set of
statements of relationship whose purpose is to
explain and predict. When a provisional hypothesis
is tested and verified and found to be true, it is
designated as a scientific theory.
• Examples:
Capital
Asset
Pricing
Theories,
Performance Satisfaction Theories, Consumer
behavior theories.
16
Model
• A model is a highly formalized representation of a
theoretical network, generally designed through the
use of symbols or other such physical analogs.
Models are used as representations of theoretical
systems so that they can be tested, examined and
generally analyzed by those who create them. It is
otherwise known as a simplified version of
phenomena that are of interest to the researcher.
17
Hypotheses
• Hypotheses are conjectural statements of the
relationship between two or more variables that
carry clear implications for testing the stated
relations. The assembled facts are transformed by a
researcher into constructs. The constructs are them
assembled into a provisional hypothesis.
18
19
20
Formats of Hypotheses
• Propositions
• If – Then Statements
• Directional and Non-directional
• Null and Alternative Hypotheses
21
22
Graduate School of Management (GSM)
ASSIGNMENT COVER SHEET
***This cover sheet must be stapled to the front of your assignment
Program & Intake
Doctorate of Philosophy in Management and Business
Module Code & Name
Advanced Research Methodology
Lecturer Name
Arun Kumar Tarofder
Assignment Title
1. Risk Management Strategies in Mega Projects, Oil and Gas
Office Use Only
Assignment Received
□ Before due date & time
□ Late Submission
Please note that it is your responsibility to retain copies of your assignment. Copying someone else’s work is
plagiarism and is unacceptable. The University may impose severe penalties for plagiarism. All work must be
submitted by the due date. If an extension of work is granted, this must be specified with□the
signature of the
Before due date & time
lecturer/tutor.
□ Late Submission
Name & Signature
Extension granted until (date)
of lecturer/tutor
For
Individual
Assignment
I declare that all material in this assignment is my own work except where there is clear acknowledgement or
reference to the work of others, and I have complied and agreed to the University statement on Plagiarism and
Academic Integrity.
Student Name as per I/C
Matrix Number
Signature
Mahmoud Mohammed Mahmoud Khder El Sayes 012024021467
For
Group
Assignment
We declare that all material in this assignment is our own work apart from areas with clear acknowledgement or
reference to the work of others, and we have complied and agreed to the University statement on Plagiarism
and Academic Integrity. By signing the cover sheet, group members indicate their agreement that they made a
fair and reasonable contribution to the assignment or project.
Student Name as per I/C
Matrix Number
Office Use Only
Lecturer Name
Marking Comment & Feedback
Results/ Grade
Copyright © Management & Science University (MSU)
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Signature
Contents
1.
Introduction ……………………………………………………………………………………………………………………. 4
1.1.
Background ………………………………………………………………………………………………………………… 5
1.2.
Problem Statement…………………………………………………………………………………………………….. 12
1.3.
Research Question …………………………………………………………………………………………………….. 13
1.4.
Research Objectives …………………………………………………………………………………………………… 13
1.5.
Significance of Study………………………………………………………………………………………………….. 13
References ……………………………………………………………………………………………………………………………….. 14
Table of Figures
Figure 1 Nine Compelling Use Cases of AI in Oil and Gas (Srivastava, 2024) ……………………………………. 8
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Figure 2 Expectation Challenges in Oil and Gas Projects. (Amy Chronis, 2024) ……………………………….. 10
Figure 3 Unlocking Value from Generative AI. (Amy Chronis, 2024) ……………………………………………… 11
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Risk Management Strategies in Mega Projects, Oil and Gas
1. Introduction
As we know the oil and gas field is very important and this field is having a lot of risks when it
comes to safety and health, especially for offshore projects. Here, I’m going to talk about the risk
associated with oil and gas, especially for offshore projects, and how artificial intelligence (AI)
can help in minimizing and avoiding this risk. The most important point that I will concentrate on
is poor planning and how AI can help in improving this poor planning by providing accurate output
to avoid and minimize the risk. If we have an AI and feed it with planning information for any
project, we expect that the AI will help in providing the output that can help reduce the associated
risk for offshore oil and gas projects. For the time being no system can help to forecast whether
the actual results in the planning stage are meeting the expectation or not.
Global oil and gas initiatives cause increased capital usage, multiplication of risks, and changes in
the contracts. For stakeholders to use this information to make better decisions in unknown
moments, it is prudent to understand these global initiatives. (Cheng Cheng, 2018). As a result,
this paper lays a foundation for Artificial Intelligence in evaluating the prevailing risk.
Most people believe that Oil and gas pipelines are the most convenient and cheap means of ferrying
petroleum products. While they are considered effective, they always fall short because of
accidents that may occur and lead to fatalities such as mechanical, operational, and natural
occurrences. Moreover, the OGP transport systems are difficult to unravel because of their
association with risks. Since they are usually critical and unforeseen, their prevalence keeps
growing widening the daily challenges facing petroleum transport, resulting from economic
instability. The identification of OGP’s situation should be used to seek solutions to prevent any
disruptions within businesses or economic phenomena. In response, the OGP risk management
field identifies possible mitigation measures such as mass awareness, availing historical findings,
and adopting better risk assessment facilities in developing countries.
Following the OGP realization, it is prudent to conduct risk management research and convert the
findings to reviewable and easily understood findings for the sake of creating conducive
environments for such operations. While we may not exhaust OGP risks, treating all of them with
their respective weights is necessary to save time and finances. The entire process can be eased
through the adoption of better assessment measures to avoid any form of pipeline breakdown
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through the reduction of their impacts. It involves steps such as identification of the risk, its
analysis, responding to and mitigating all of them, and monitoring and controlling them.
i.
Risk identification is the foundation of the entire process, where all factors are identified
with their influence on the project’s success. We render this step vital because the process
assumes that the factors are easily identified and the attempt to defend a system without
understanding its risks is not feasible.
ii.
After risk identification, we analyze the risks together with their factors to understand the
likelihood of the risk occurring, its frequency, fatality, and influence. The first two steps of
this process call for an adept understanding and availability of reliable data that will be
used during evaluation. During this step, risks are classified according to their demand for
attention hence there is a need to holistically approach this project. Proper registration of
the risk and its assessment are crucial for the sake of better evaluation.
iii.
Next, the stakeholders consider mitigating and responding to the risk. This step involves
the basic means of responding and selecting activities that could minimize the chances of
its occurrence while reducing any possible consequences.
iv.
Finally, the risk is monitored and controlled after identification, analysis, and mitigation.
Existing risks are also considered during this step to reduce the chances of their occurrence
in the future. (Layth Kraidi, 2019).
1.1. Background
We can define risk as the probability of an unfortunate thing happening. It comprises of unsurety
regarding the outcomes of the activity considering another thing that is valuable to human beings.
The best example could be health, well-being, wealth or their surroundings and risks usually
concentrate on negative and unwanted impacts.
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International opportunities: The oil and gas field is internationally recognized, which means that
there are opportunities to work in different countries and regions around the world. Varied career
options: The oil and gas industry offers a wide range of career options, from engineering and
geology to finance and marketing, that’s why we have to pay more and more attention to the risk
when it comes to poor planning, safety, and health, especially for offshore oil and gas which are
having a lot of unexpected risks such as working in poor weather condition which might affect the
human’s safety and demand and supply, and trade and investment within the crude oil and natural
gas. Normally the planning is rough planning without taking into consideration the associated risk
to achieve the market needs and customer satisfaction. Hence, in my study, I want to introduce AI
which will help in improving risk mitigation.
The importance of managing risk management in oil and gas is very important as follows:
–
Operational Risks: Operational risks such as oil spillage, equipment breakdown,
environmental challenges, and gas expositions form part of this industry. They affect the
utilization of oil and gas within reputable companies because of the prevailing instabilities.
Risk management could help identify, assess, and mitigate these risks creating employee,
surrounding, and general security. (Samimi, 2020).
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–
Financial Risks: Financial problems have been in existence within this field because of
global economic challenges, political instability, and the pandemic. For instance, there has
been an imbalance in the fuel process, low demand, and an imbalance in the supply chain.
Managing risks may reduce financial burdens such as price liquidity, and changes in
currency values and this may cause mismanagement of finances by the business
organizations which jeopardizes their profitability. (Samimi, 2020)
–
Regulatory Risks: Regulation of this industry is important in creating a healthy
environment for all workers. If one does not follow these policies, it is necessary to include
fines and legal practices to ensure discipline and fairness. Eventually, organizations will
follow the regulations, reducing the possibility of facing legal and monetary charges.
–
Reputation Risks: In most cases, this field is scrutinized openly and the most common
findings include effects of the surroundings, ethical concerns, and the employees’ wellbeing. If these risks are never managed, the organization may lose the trust of stakeholders
leading to unforeseen negative results. It helps in the identification and mitigation of these
unfortunates and using moral and adequate business operations. (Samimi, 2020)
Conclusion:
“Risk management is essential in the oil and gas industry, especially in this period of pandemics,
international inflation, and war. Identification, assessment, and mitigation of operational, financial,
regulatory, and reputation risks, businesses organizations can achieve employee safety, protect the
environment, comply with regulations, and maintain financial stability and reputation.” (Kalejaiye,
2023)
The requirement for advanced risk management technologies and techniques from the fact that
today’s businesses face complex and progressing risks that require sophisticated approaches to
identify, assess, and manage. As a result, businesses are turning to advanced technologies to help
them manage risks and make better decisions such as AI. (Kalejaiye, 2023)
Why do oil and gas need advanced technology and techniques for managing it, advanced
technology such as AI is needed due to the following:
– Risk complexity and variety.
– Huge volume of data available which can’t be handed by the current and normal techniques.
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– Decision Making.
– Cost Efficiency.
To conclude, the demand for advanced risk management technologies is driven by the complexity
of risks, large volumes of data, the need for speed in decision-making, and the need to cut costs.
Businesses can use these technologies to better manage risks and make more informed decisions,
which leads to increased operational efficiency, lower costs, and fewer accidents. (Kalejaiye,
2023)
By 2028, the AI market in oil and gas is expected to be worth $4.21 billion, with a CAGR of
12.09%. It is changing the way businesses operate due to its ability to analyze large datasets and
learn from patterns. AI is changing the industry landscape, from predictive maintenance that
prevents costly equipment failures to supply-chain optimization that ensures efficient operations.
(Srivastava, 2024)
There are nine top Use Cases of Artificial Intelligence in this Sector as follows:
Figure 1 Nine Compelling Use Cases of Artificial Intelligence in Oil and Gas (Srivastava, 2024)
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It is necessary to major in the one of them that is most applicable to securing the available risks.
This field prefers people’s health, their security, and their surroundings. The inclusion of Artificial
Intelligence in this field will help business organizations revolutionize the influence of these risks,
leading to more convenient operations.
AI solutions for oil and gas improve safety through predictive maintenance. By continuously
monitoring equipment health and performance, AI algorithms can detect anomalies and potential
failures before they become safety risks. This popular initiative urges organizations to conduct
alternative and maintenance practices that ensure less prevalence of accidents, ensuring safe
working conditions for their workers. (Choubey, 2021)
Moreover, Artificial Intelligence inclusion in this field ensures real-time safety monitoring. AIpowered sensors monitor environmental conditions, equipment performance, and personnel
activity in offshore drilling operations. The data is instantly analyzed, and if there are any safety
deviations, immediate alerts, and corrective actions are issued, reducing the likelihood of incidents
and improving emergency response.
Artificial intelligence actively participates in the transportation of oil and gas products. AI helps
optimize transportation routes, lowering the risk of accidents while transporting hazardous
materials. AI solutions for oil and gas assist businesses in identifying safer and more efficient
transportation routes by analyzing traffic data, weather conditions, and road infrastructure. AI
integration is a game changer in terms of improving worker safety. (Srivastava, 2024)
In my study, I’m going to target organizations that might use AI to mitigate the risk. Also, the
personnel will be one of the targeted goals as they are the ones who are going to feed the AI with
the required information for getting the required result. Hence, the personnel is the main factor
here so good training is required for the personnel to understand what is the AI and the advantages
and disadvantages of using it.
The below figure shows the most expected risks and challenges in the oil and gas industries
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Figure 2 Expectation Challenges in Oil and Gas Projects. (Amy Chronis, 2024)
The below figure shows how AI can enhance the operational sustainability of O&G
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Figure 3 Unlocking Value from Generative AI. (Amy Chronis, 2024)
Using generative AI to harness value across these dimensions can improve operational
sustainability for O&G companies by monitoring carbon emissions, optimizing energy efficiency,
reducing waste, and predicting emission intensities throughout their supply chain. When
incorporating AI technology, the industry will most likely benefit from taking proactive steps to
address cybersecurity challenges, adapt to changing regulations, and ensure data quality. (Amy
Chronis, 2024)
The primary consideration in selecting Artificial Intelligence is the possibilities it brings in the
reduction of mistakes as it encourages more precise outputs. Automation fastens the decisionmaking process considering previous data and a more precise group of algorithms. Efficient
articulation helps reduce the mistakes to irreducible minimums.
Conclusion:
Artificial intelligence provides the most advantageous situation because of its involvement in the
process of managing risks. It helps in fastening decision-making using available data and this
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makes the process more reliable since automation increases accuracy- it offers sufficient
evaluation and feedback to the researchers (Duggal, 2024).
1.2. Problem Statement
•
Common gap in previous research with respect to my proposed title. The knowledge
gap is widely known as the areas where an organization lacks the necessary skills,
expertise, or information needed to perform the risk management analysis effectively.
Limited knowledge may prevent better outcomes, creativity, and the general success of
an institution. It has been extensively studied and examined that currently risk
management strategies in mega projects of oil and gas using AI have not been
conducted yet.
•
Another problem exists in the appropriate procedure and methods to employ in this
research which will increase accuracy and data reliability. Inadequacy in this sector
could jeopardize precision and evaluation, narrow the study scope, or prevent the
growth of effective theories of solutions. However, with the vast amount of information
available, technology offers the most appropriate steps to reducing the space between
the known and the unknown. Its presence eases the process of identifying areas with
limited methodological practices and avails the necessary resources to bridge such
gaps. Therefore, AI utilization would be the first of its kind and can be utilized to
suggest rectifying all previous methodological approaches against the risk management
strategy in mega projects of oil and gas using AI.
•
The coined schools of thought lack the required prowess to mitigate theoretical
limitations in this field. Most existing resources barely use Artificial Intelligence, hence
their inclusion in this research becomes difficult. Therefore, this research is appropriate
in revealing the vitality of AI in this industry.
•
Most researchers found out that the population possessed some shortcomings because
some of them were undeserving and rarely researched. It is one of the most common
limitations in the population where there is gender, age, and race might have
insufficient or biased representation while looking for evidence. Since AI is a very new
approach to the resolution of the issues faced in risk management strategies, the
population gap can easily be avoided via Social Learning which is a social learning
activity through which people earn from those around them. Social learning mediums
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help create better networks with knowledgeable people from whom knowledge can be
disbursed. For instance, an individual could understand technology through social
learning platforms where they meet people with more advanced expertise in that field.
1.3.Research Question
•
What will be the benefits of utilizing AI in risk management and who will get the benefits
from applying it?
•
In AI, we can feed it with the expected risk and challenges for any product starting from
planning to the handing over of the product to the customer. AI will analyze all the data
identify the gaps in each phase and give the outcome of whether the plan is as expected
and appropriate or not.
•
What The organization will gain from implementing the AI as well as the end user.
1.4.Research Objectives
•
To enhance the performance of the organization regarding risk management as well as
increase customer satisfaction by delivering high-quality products.
•
To identify the negative indicators in the conventional risk management process.
1.5.Significance of Study
•
Using AI in analyzing the expected risk will help the organizations as well as the personnel
in avoiding any harm to the people, or facility and will provide the end user with the
required product on time with the expected quality. Also, will avoid the amount of losses
regarding material, time, effort, and human
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References
Amy Chronis, K. H. A. M., 2024. Deloitte Research Center for Energy & Industrials. 2024 oil and
gas industry outlook, 1(https://www2.deloitte.com/us/en/insights/industry/oil-and-gas/oil-andgas-industry-outlook.html), p. 1.
Cheng Cheng, Z. W. M.-M. L. &. X.-H. R., 2018. Risk measurement of international oil and gas
projects. Risk measurement, 23 November, pp. https://link.springer.com/article/10.1007/s12182018-0279-1.
Choubey, S. &. K. G. P., 2021. Artificial intelligence techniques and their application in oil and
gas industry.. 54(5), 3665-3683 ed. s.l.:Artificial Intelligence Review.
Duggal, N., 2024. Advantages and Disadvantages of Artificial Intelligence [AI]. Advantages and
Disadvantages of Artificial Intelligence [AI], 1(https://www.simplilearn.com/advantages-anddisadvantages-of-artificial-intelligencearticle#:~:text=One%20of%20the%20biggest%20benefits,can%20be%20reduced%20to%20null.
), p. 1.
Kalejaiye, Y., 2023. Project Risk Management for Oil and Gas Industry. Risk Management, 12
April,
pp.
https://www.linkedin.com/pulse/project-risk-management-oil-gas-industry-neural-
yemi-kalejaiye/.
Layth Kraidi, R. S. W. M. F. B., 2019. Analyzing the critical risk factors associated with oil and
gas
pipeline
projects.
Analyzing
the
critical
risk
factors,
24
March,
p.
https://www.sciencedirect.com/science/article/abs/pii/S1874548217301208.
Samimi, A., 2020. Risk management in oil and gas refineries.. 3(2), 140-146. ed. s.l.:Progress in
Chemical and Biochemical Research,.
Srivastava, S., 2024. How AI Is Revolutionizing the Oil and Gas Industry. AI for Oil and Gas, 13
February,
pp.
https://appinventiv.com/blog/artificial-intelligence-in-oil-and-gas-
industry/#:~:text=The%20application%20of%20machine%20learning%20and%20AI%20in%20t
he%20oil,and%20train%20their%20personnel%20accordingly.
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