This course work intention is to conduct a research on the application of Enterprise Modelling as a Decision Support Systems Framework within the context of Organisation of Petroleum Exporting Countries (OPEC) and the impact on technology. OPEC is established in 1960 and its main objective is to ensure that the economy of their member counties is steady through petroleum industry. In this section I like to provide a summary about the articles and the five main themes I have selected from the generic framework of energy industry, for the Section II discussion which using those articles. The most chosen articles are not directly related to OPEC but oil industry.
Figure1.1. Chosen cycle and the five themes from Generic framework of energy Industry
One of the articles that I have found is "A decision support system for prioritizing oil and gas exploration activities" (Dyer et al, 1990).Oil is the capital of OPEC countries which is main factor of their economy. That article presents the research on usage of decision support system to explore oil and gas, and the analysis about its availability.
I have searched and found lots of articles
I have selected below articles
Article A talks about A,B and D
Using these articles I like to analyse the below five themes which I selected from generic framework of energy industry.
Section II
In section II of the review, you will give an in-depth explanation of how the content of the articles relates to technology within the context of OPEC.
Available Capital
Decision support systems have been used to model the oil and gas exploration process successfully through guiding by budgeting and man power decision. The identified problem in the exploration process was, though exploration budget was high, manpower wasn't enough to the expectation.
Dyer et al (1990) mentioned two approaches took for the current problem which was,
Develop a linear approximation to the value of information model that includes the same five variables.
Then estimate the factors in the linear model using attributes of plays familiar to the geologists and geophysicists.
The first approach is based on decision tree system as shown in figure 1. In this decision tree it consider about drill for oil without exploration and the exploration for oil or gas as main decisions at the first stage. Then in the Drill decision consider about probability of commercially successful. The second stage op explore stage is consider about the probability of favourable and support decision to continue the drill.
Figure2.1. Decision tree diagram for oil well exploration decision [Dyer et al. 1990]
Multi attribute decision system is a preference determination system which helps in deciding between alternatives at predetermine decision points. The second approach was to estimate the variables in the linear model using familiar attributes of plays. The Table1 shows the attributes grouped by six linear variables.
Table2.1: Relation of Attributes to Variables and the Attribute Weights [Dyer et al. 1990}
Value of Information Variables
Attributes
Attributes Weights
P(S)
Probability of a successful play
Concept
Progress
Data
Reserve
0.24
0.24
0.29
0.24
P(S/G)
Probability of a successful play given good exploration results
Manpower
Technology
0.47
0.53
R
Returns
Volume
Marketability
Information
Percent oil
Other company problem
Extra industry problem
0.50
0.13
0.12
0.04
0.09
0.12
C
Drilling Costs
Drilling costs
0.37
E
Exploration Costs
Drilling difficulty
Remoteness
0.33
0.30
D
Cost of delay
Land availability
Competitive advantage
Urgency
Delays
Lease terms
0.40
0.26
0.04
0.08
0.21
The liner regression model evaluated finally by Dyer at el (1990) was,
U (P(S)) = 0.24uc + 0.24up + 0.29ur + 0.24ud
A questioner made on the above model and implemented it on a personal computer. This Decision support system is support aggressive exploration activities on oil and gas, and also it helps on getting decisions of required man power for oil plays.
Renewable Energy
Renewable energy is one of the main topics currently discussing all over the world. Petroleum industry is very interesting about regenerating used oil because of its benefits such as protect environment and produce renewable resource. There are several technologies available to regenerate used oil but it has been difficult to choice a technology through one of them which is accomplished specific needs and preferences."The first prototype of spent oil regeneration (SPORE), a decision support tool, has been developed to help decision-makers to assess the available technologies and select the preferred used oil regeneration options. The analysis is based on technical, economical and environmental criteria"(Helifi et al , 2006).the core of this tool is multi-criteria decision analysis (MCDA) and the target stakeholders are developing countries.
The architecture of the SPORE tool is shown in figure2.2 which has three main tiers. Graphical user interface (GUI) is implemented by standard web application and rational data base management system (RDBMS) represents the Data ware house. Web based intelligent systems environment (WISE) is the middle tier which interconnects GUI and RDBMS using MCDM package which contain the implementation of most popular outranking type PROMETEE algorithm. "Outranking methods represent binary relations between alternatives, given the preference of the decision maker, the quality of the valuations of the alternatives and the nature of the problem" (Helifi et al , 2006).
Figure2.2. WISE based decision support tool architecture [Helifi et al , 2006]
The general algorithm used in SPORE is shown in the figure2.3.The different technologies and criteria's used in the SCOPE first prototype is presented in the table2.2.
Figure2.3. General Algorithm supported with SPORE [Helifi et al , 2006]
Tabel2.2 List of available technologies and criteria used for SPORE implementation [Helifi et al , 2006]
Development Stage
Technologies
Product
Criteria
Industrial application
Mohawk
Revivoil
Atomic vacuum distillation
Blowdec
Cyclon
Enviro-tech
Meinken
Prop
Acid/clay purification
Snamprogetti
Sotulub
Vaxon
KTI_Relube
Base oil
Yield of the main product
Quality of the product
Stream factor
Development stage
Operating cost
Estimated capital cost
PCB's removal
By-product quality
Solid waste existence
Prototype and pilot stage
Interline
Entra
Mrd-Kernsolvat-Extraktions
Base oil
Studies and patents
UOP Hylube
Base oil
After user go through certain step by selecting options by their preference the SPORE tool will generate the results and finally user has to make a decision to choose the best matching result considering the environment and other facts.
Repoussis et al (2002) proposed, A web based dessision support system for waste lube oils(WLO) collection and recyclling is different from the SPORE tool which developed by Helifi et al (2006).And also repossis et al (2002) look at this process widely, start to end of the process which are,
How much WLO must be available at the central accumulation point in order to allow for smooth production
How to effectively schedule the collection process performed by heterogeneous vehicle fleets, considering all operational constraints
How to continuously monitor collection operations and accumulation rates to ensure full WLO retrieval
Provide the appropriate decision making tools for the effective and efficient distribution of lubricant products to customers
Energy Supply
One of the key points in OPEC mission is regular supply of petroleum to consumers, to achieve that goal they should have good and suitable oil production management. Zhong et al (2008) proposed a "Decision Support System for Oil Production Based on SOA", which combination of technologies such as graphics and image processing, remote transmission control, data mining and etc. The advantages of this SOA based system is able to integrate with other information systems currently available, independent from the hardware and software of those systems. Figure.2.4 shows the Service Oriented Architecture (SOA) Framework based proposed oil production Decision support system.
Figure2.4. Based on SOA Framework of oil production Decision support system [Zhong et al 2008]
The user layer design using Model view Control (MVC) architecture, Object relational mapping technology is used to integrate with date layer and application service layer is executes the business logics.
The usage of this system is make decisions to expand the scale of production through reduce production costs and improve production efficiency. The proposed system is contain five major components which are,
Scene acquisition subsystem
Long-distance transmission controlling subsystem
Remote monitoring subsystem
Expert analysis and diagnosis subsystem
Decision supporting subsystem
The figure.2.5 shows the oil production DSS model structure.
Figure2.5. Oil production DSS model structure [Zhong et al 2008]
According to Zhong et al (2008) this system combines many advanced IT technologies with oil professional knowledge, it will be effectively applied to the entire oil production operation management system to standardize operation process, and it will reduce production costs and increase productivity.
Technology & Deployment
In this section I have combined two themes from generic frame work of energy industry which are more related, because it's easy to identify the technology that use in modelling decision support system and the deployment of it.
The previous section discusses about the usage of decision support system in aggressive exploration activities on oil and gas. The program is implemented on an IBM personal computer, which computed and summarized the given matrixes for attributes by use by use of a new matrix. Then it will generate an S-cure graph which helpful on ranking oil plays.
Section III
Conclusion
Section III should give a succinct (luhudu/sanshikptha) summary of (I and II) and present your own objective recommendations
