BEFORE YOU READ THE ABSTRACT OR CHAPTER ONE OF THE PROJECT TOPIC BELOW, PLEASE READ THE INFORMATION BELOW.THANK YOU!
INFORMATION:
YOU CAN GET THE COMPLETE PROJECT OF THE TOPIC BELOW. THE FULL PROJECT COSTS N5,000 ONLY. THE FULL INFORMATION ON HOW TO PAY AND GET THE COMPLETE PROJECT IS AT THE BOTTOM OF THIS PAGE. OR YOU CAN CALL: 08068231953, 08168759420
WHATSAPP US ON 08137701720
PRODUCTION OPTIMIZATION AND PERFORMANCE BENCHMARKING TECHNIQUES FOR NEW INFILL WELLS USING IPR/VLP
ABSTRACT
This Master thesis develops a new solution method for optimizing oil production at the Troll C platform. The Troll West field where Troll C is located is an oil rim field. The characteristic gas cap above the thin oil layer makes the Gas-Oil-ratio strongly rate de- pendent during production. Today the gas handling capacity is the main constraint as the processing trains at the platform can process only a certain amount of gas. The pro- duction planning problem is today solved by applying state of the art software. However this software can not simultaneously solve all eight clusters at Troll C. Norsk Hydro ASA uses production data and experience to decide on an amount of gas that each cluster may produce, before optimizing each cluster separately.
To improve this current practice the authors considered the use of linearization techniques and Lagrangian Decomposition as a new methodological solution platform. The idea has been to develop a solution method that split the total amount of gas capacity in a more optimal way. This is done by relaxing the gas handling capacity constraint into the objective function and use a Lagrange multiplier to penalize the use of gas. The authors have developed a Mixed Integer Linear Programming (MILP) model, and based on this built an algorithm implemented in VBA for Excel, Xpress-IVE and GAP. The algorithm has the possibility to dynamically increase the resolution of the linearized model close to the area where the solution is believed to be. This formulation is completely scalable and can solve problem with any resolution, by accepting longer solution time. The solution method is based on Linear Problem (LP) methods, SOS2, Branch & Bound and Lagrangian decomposition. The algorithm is tested on a test case to evaluate the performance of the algorithm. The information obtained was evaluated and taken into consideration before testing it on actual data from two out of the nine clusters at Troll C. This data was provided by Norsk Hydro ASA. The solution was compared with today’s solution, and produces 7,7% less oil.
DInA – Dynamic Interpolation Algorithm
GAP – General Allocation Package
GOR – Gas/Oil-Ratio
GORM – Gas/Oil-Ratio Model
IC – Interpolation Coordinate
ILP – In ow Perfomance Ratio
LaDA – Lagrangian Decomposition Algortihm
LD – Lagrangian Decomposition
PDP – Production Data Portal
PI – Productivity Index
P-line – Production line
PPOP – Production Planning Optimization Problem
PROSPER – Production and Systems Performance analysis software
RBC – Real Base Case
RTO – Real Time Optimization
SLP – Sequential Linear Programming
SOS1 – Special Ordered Sets of type 1
SOS2 – Special Ordered Sets of type 2
SQP – Sequential Quadratic Programming
TBC – Test Base Case
T-line – Test line
TOA – Total Optimization Algorithm
TWGP – Troll West Gas Province
TWOP – Troll West Oil Province
VLP – Vertical Lift Performance
INTRODUCTION
Oil production planning optimization has been practiced for many years and di erent models have been developed to assist oil producers to more e ciently extract the oil from reservoirs. In collaboration with Norsk Hydro ASA this work have had focus on the operational production planning at the Troll C platform. The special properties of the Troll reservoir make the production planning di cult. The most critical production factor when producing at the Troll C is the gas capacity. The oil is located in thin zones with large gas caps above, and one have to take into consideration the strongly rate dependent Gas/Oil Ratio (GOR) due to the gas-handling capacity of the rst stage separator. Today Norsk Hydro ASA use sophisticated software and technology to optimize their production.
In the oil industry, optimization models and methods have been used extensively for pro- duction planning problems. Wang [53] provides a thorough overview of oil production planning optimization, where both linear and nonlinear optimization techniques for com- mon oil production planning problems are presented. The most common concepts and components of an oil production optimization problem are presented in an informative way by Bieker, Slupphaug and Johansen [5]. This article gives a good overview of the subject and includes short descriptions on elements such as; processing facilities, well model updating, reservoir model updating, production planning, reservoir planning and strategic planning. Methods for well routing, gas lift and gas/water injections optimiza- tion are also reviewed. For more Troll speci c optimization Hauge and Horn [21] can be recommended. Here the challenges of operating and maintaining the Troll subsea system are described.
Today’s practice at Troll C is to allocate a certain amount of the total gas handling capacity to each cluster manually. Production optimization is often trial and error and good communication between operators and production engineers onshore are vital for success. By studying production data and simulate production, this experienced personnel decides on how much gas each cluster is allowed to produce. When this decision has been taken sophisticated software suites are used to optimize oil production by consider- ing the gas handling constraint. However, these software suites are not able to handle the routing of wells between the di erent production lines which also is part of the problem. Norsk Hydro ASA therefore needs to use brute force to check every routing combination for each cluster.
This master thesis develops a new solution methodology for the production optimization at Troll C. It considers the possibilities of assigning the gas capacities more properly to each cluster by solving all clusters simultaneously. The developed model is also going to handle routing of wells. Before developing this solution method the authors have stud- ied relevant literature about petroleum production and operational research. A Mixed Integer Linear Programming (MILP) has been formulated and solution methods for this model are proposed. The solution method has been implemented in Xpress-IVE and Visual Basic for Excel (VBA). All non-linearity are handled through piecewise lineariza- tion and the use of Special Ordered Sets of type 2. The resolution on the linearization is dynamic. The model is further decomposed by use of Lagrangean Decomposition to relax the gas-handling capacity, where the Subgradient Optimization method is used to update the Lagrange multiplier. Beasley [4] provides a good description on Lagrangian Decomposition, while Jackson and Grossmann [27] uses Lagrangian Decomposition on a production planning problem from the process industry with good results.
The time horizon considered is one week. This implies that the reservoir conditions are treated as constant in this problem formulation. Further the production from each well is described by a piecewise linearized well performance curve generated in the General Allocation Package (GAP) developed by Petroleum Experts. GAP has also been used to simulate di erent combinations of owrates for oil, gas and water to construct a piecewise linearized multi-dimensional pressure drop function dependent on these rates. As this Master thesis is a rst step into this kind of solution methodology no gas lift wells or gas lift risers are considered.
The most di cult parts when building a production planning models is the modeling of the in ow from the reservoir to the wells and the pressure drop in the pipeline network. These subjects have been treated by several others. Handley-Schachler, McKie and Quintero [19] uses the concept of Special Ordered Sets of type 2 (SOS2) to linearize well performance curves when solving production optimization problem by Sequential Linear Programming. Their work also uses multi-dimensional SOS2 to model the pressure drop in the pipeline system, though not explained thoroughly. Litvak, Clark, Fairchild, Fossum, Macdonald and Wood [35] uses ow correlations together with Equations of State to calculate the pressure gradients. Kosmidis, Perkins and Pistikopolous [33] presents a mixed integer non linear model (MINLP) for daily well scheduling in petroleum elds using analytic ow relations when modeling multiphase ow and pressure drop relations. This model considers all of the issues in our Master thesis and more, except for the gas-handling constraint.
The developed algorithm’s performance is tested by solving some test cases generated by the authors and on data from a real case. The real case data is provided by Norsk Hydro ASA. The data consist of GAP les containing all information about the two clusters, including Norsk Hydro’s best solution. This data makes it possible to generate well performance curves and pressure drop functions in GAP.
The results are presented and discussed, both with respect to the performance of the MILP model and the potential value when planning the production at Troll C. The results show that the dynamic linearization technique works effectively at the test cases; however it could not be used during testing of real data due to a malfunction. The Lagrange multiplier adjustment method works very well for both cases. The results from the real case shows that gas capacity are divided differently than the manual solution used today, however it produces a bit less oil than the current solution.
All in all the different parts of the developed algorithm works as intended, however the assumption made about the pressure drop modeling seems to be a bit coarse. Therefore this part of the MILP model needs further development and testing before this tool can be used as an optimization tool at Troll C.
As part of this Master thesis the authors have studied a wide variety of literature on petroleum production planning and optimization.
The remainder of the report is made up of two major parts; the rst part describes the problem and the current practice. It starts out with a general description of the different levels of oil production planning optimization problems in Chapter 2. Chapter 3 describes the operational production planning problem considered at Troll C. Chapter 4 closes the first major part by describing the different models used to solve these types of problems and present the system topology and the mathematical model for the production planning at Troll C.
PURPOSE OF THE STUDY
The purpose of this work is to develop an alternative solution method to optimized production
OBJECTIVE OF THE STUDY
The objective of production system analysis can be summarized as:
SCOPE OF THE STUDY
The productivity of the well depends on an efficient use of the compressional energy available in the reservoir allowing the reservoir fluids to flow toward the production separator. This techniques uses IPR or VLP.
DEFINITION OF VERTICAL LIFT PERFORMANCE
This is a plot of the pressure traverse in the vertical/inclined tubing of a production system. It is a plot of the pressure losses vs. production rate in the system. Multiphase flow correlations are used in calculating the VLP.
VERTICAL LIFT PRESSURE LOSSES
FACTORS AFFECTING THE VERTICAL LIFT PERFORMANCE
HOW TO RECEIVE PROJECT MATERIAL(S)
After paying the appropriate amount (#5,000) into our bank Account below, send the following information to
08068231953 or 08168759420
(1) Your project topics
(2) Email Address
(3) Payment Name
(4) Teller Number
We will send your material(s) after we receive bank alert
BANK ACCOUNTS
Account Name: AMUTAH DANIEL CHUKWUDI
Account Number: 0046579864
Bank: GTBank.
OR
Account Name: AMUTAH DANIEL CHUKWUDI
Account Number: 3139283609
Bank: FIRST BANK
FOR MORE INFORMATION, CALL:
08068231953 or 08168759420
BEFORE YOU READ THE ABSTRACT OR CHAPTER ONE OF THE PROJECT TOPIC BELOW, PLEASE READ THE INFORMATION BELOW.THANK YOU!
INFORMATION:
YOU CAN GET THE COMPLETE PROJECT OF THE TOPIC BELOW. THE FULL PROJECT COSTS N5,000 ONLY. THE FULL INFORMATION ON HOW TO PAY AND GET THE COMPLETE PROJECT IS AT THE BOTTOM OF THIS PAGE. OR YOU CAN CALL: 08068231953, 08168759420
WHATSAPP US ON 08137701720
THE IMPACT OF PRODUCTION PLANNING AND CONTROL ON PRODUCTIVITY
CHAPTER ONE
INTRODUCTION
1.1 Background of the study
The Impact of Production Planning on Productivity in the Seven up Bottling Company Kaduna
Production is one of the most important function of management in every organization. The essence of production is to prepare or forecast into the future in terms of production in an organization. As such Baryoko (2002) opined that production planning is mainly concerned with the directing and controlling of production process of an organization in order foster optimum utilization of human and material resources use in production, which brings about increase productivity and consequently profit maximization.
Similarly Nwachukwu (2007) opined that production planning involves the establishment of the overall strategy and process design required for the realization of effective production in an organization.
As such, production planning’s particularly very important in a developing country where resources are relatively scarce. In Nigeria, where many of the manufacturing firms are inexperienced interms of technical know-how, production planning become very imperative in other to avoid waste of resources. This is so because production planning is a blue print for action. As such all manufacturing organization who are into production need to plan their production in order to gain an optimum utilization of their plant and raw materials use in production.
However, Ayo (2006) opined that failure to engage in production planning give rise to inefficiency and lack of direction. It make the organizations to constantly put off fire instead of preventing its occurrence. This implies that production planning entails the identification of what at business plans to do in terms of growth, development and survival or continuous existence. But inspite of this, over 48.6% of Nigeria indigenous organizations do not undertake a formal plan and do not have production budgets.
It is against this background that the researcher deemed it very imperative to investigate the subject matter of this research. The impact of production planning on productivity in seven-up bottling company Kaduna.
1.2 STATEMENT OF THE PROBLEM
Production planning as earlier indicated is a very crucial aspect of any management process. Hence, efficient production planning becomes very imperative for the sustainability of an effective production system that would be cost effective and profitable.
But inspite of the importance of production planning and its positive impact on productivity, it is observed by Ayo (2006) that about 48.6% of Nigerian indigenous organizations do not undertake a formal plan and do not have production budgets. As such doubt is expressed about production planning in most organization in Nigeria. Hence the need to investigate the impact of production planning on the productivity of seven-up bottling company Kaduna become very imperatives and the subject matter becomes an empirical problem worthy of investigation.
1.3 OBJECTIVES OF THE STUDY
The central objective of the study is to investigate the impact of production planning on productivity in seven-up bottling company Kaduna. Other sub-objectives are as follows:
To determine production planning activities of seven-up bottling company Kaduna plant.
To evaluate the effect of production system in manufacturing of seven up products?
To explore the meaning of production planning
To evaluate the relevance of production planning concept.
1.4 SIGNIFICANCE OF THE STUDY
This study examines the impact of production planning on seven-up bottling company Kaduna plant. The significance of the study of this magnitude cannot be overemphasized. The government corporation, corporate organization, small and medium enterprise involved in manufacturing will really find this study useful, especially as they utilize the findings of this study. The study will also add to the existing knowledge on production planning and would also be useful to student and researchers.
The government corporation and corporate organizations involved in manufacturing can use the finding of this research in policy formulation regarding production planning.
Small and medium enterprise may also find the findings of this study really significant as they utilize the finding as a guideline in formulating their production planning.
The study will also contribute to knowledge by being a good reference material to students, scholars, resources and individuals who may wish to undertake similar research. This research can be used as a spring board to undertake their own work.
1.5 RESEARCH QUESTIONS
i. What are the production planed activities in seven-up bottling company kaduna
ii. What is the effect of production system in the manufacturing of seven up products?
iii. What is the meaning of production planning
iv. Is Production planning relevant to productivity.
1.6 SCOPE OF THE STUDY
The study covers the effect of production planning in seven-up bottling company Kaduna plant a
1.7 LIMITATION OF THE STUDY
Every research activity has its constraints this particular one is however not an exception, certain factors limit the process of writing this research work, which include:
Inadequate research materials the research: Materials on this subject matter were very few. The much relied library is filled up with outdated books, which are of little relevance to current research. Moreover, the cost of new and updated materials are exorbitant which make them not readily affordable.
Lack of cooperation of subjects: Sourcing for information from source of respondents was not an easy task, particularly with regards to some information that are regarded as classified or highly confidential to the company, the degree of cooperation of staff of seven-up bottling company was minimal
Low return of questionnaires in the process of gathering data, it was discovered that not all questionnaires issued out were returned. Some of them were also wrongly filled which means a great deal of relevant information was held back, and which could have enhanced the quality of this study.
Inaccessibility to subjects: Another constraint encountered during the research is the inaccessibility to the inventory management, manager who was not always available to provide desired data.
1.8 DEFINITION OF TERM
Production: this refers to an outline process that is aimed at satisfying human needs and wants. That is the only process that deals with the transformation of raw materials into finished good.
Planning: is a set of managerial function which prepares forecast into the future and ensure that decision regarding the use of human and material resources involved in production is effectively managed
Production planning: This is a managerial function concern with planning directing and controlling the method to be used in manufacturing a product.
Schedule: This is prescribing of when and where each operation necessary for the manufacturing of a product is to be performed or the establishment of time at which to begin or complete each event or activity.
Raw materials: this are the basic inputs required for productions.
Work-in-progress: There are semi-manufactured product. Product that is uncompleted and skill in the process of production.
Finished good: product that has been completed and ready for consumption.
Lead time: This refers to the expected time of delivery.
HOW TO RECEIVE PROJECT MATERIAL(S)
After paying the appropriate amount (#5,000) into our bank Account below, send the following information to
08068231953 or 08168759420
(1) Your project topics
(2) Email Address
(3) Payment Name
(4) Teller Number
We will send your material(s) after we receive bank alert
BANK ACCOUNTS
Account Name: AMUTAH DANIEL CHUKWUDI
Account Number: 0046579864
Bank: GTBank.
OR
Account Name: AMUTAH DANIEL CHUKWUDI
Account Number: 3139283609
Bank: FIRST BANK
FOR MORE INFORMATION, CALL:
08068231953 or 08168759420
BEFORE YOU READ THE ABSTRACT OR CHAPTER ONE OF THE PROJECT TOPIC BELOW, PLEASE READ THE INFORMATION BELOW.THANK YOU!
INFORMATION:
YOU CAN GET THE COMPLETE PROJECT OF THE TOPIC BELOW. THE FULL PROJECT COSTS N5,000 ONLY. THE FULL INFORMATION ON HOW TO PAY AND GET THE COMPLETE PROJECT IS AT THE BOTTOM OF THIS PAGE. OR YOU CAN CALL: 08068231953, 08168759420
WHATSAPP US ON 08137701720
PRODUCTION OPTIMIZATION AND PERFORMANCE BENCHMARKING TECHNIQUES FOR NEW INFILL WELLS USING IPR/VLP
ABSTRACT
This Master thesis develops a new solution method for optimizing oil production at the Troll C platform. The Troll West field where Troll C is located is an oil rim field. The characteristic gas cap above the thin oil layer makes the Gas-Oil-ratio strongly rate de- pendent during production. Today the gas handling capacity is the main constraint as the processing trains at the platform can process only a certain amount of gas. The pro- duction planning problem is today solved by applying state of the art software. However this software can not simultaneously solve all eight clusters at Troll C. Norsk Hydro ASA uses production data and experience to decide on an amount of gas that each cluster may produce, before optimizing each cluster separately.
To improve this current practice the authors considered the use of linearization techniques and Lagrangian Decomposition as a new methodological solution platform. The idea has been to develop a solution method that split the total amount of gas capacity in a more optimal way. This is done by relaxing the gas handling capacity constraint into the objective function and use a Lagrange multiplier to penalize the use of gas. The authors have developed a Mixed Integer Linear Programming (MILP) model, and based on this built an algorithm implemented in VBA for Excel, Xpress-IVE and GAP. The algorithm has the possibility to dynamically increase the resolution of the linearized model close to the area where the solution is believed to be. This formulation is completely scalable and can solve problem with any resolution, by accepting longer solution time. The solution method is based on Linear Problem (LP) methods, SOS2, Branch & Bound and Lagrangian decomposition. The algorithm is tested on a test case to evaluate the performance of the algorithm. The information obtained was evaluated and taken into consideration before testing it on actual data from two out of the nine clusters at Troll C. This data was provided by Norsk Hydro ASA. The solution was compared with today’s solution, and produces 7,7% less oil.
DInA – Dynamic Interpolation Algorithm
GAP – General Allocation Package
GOR – Gas/Oil-Ratio
GORM – Gas/Oil-Ratio Model
IC – Interpolation Coordinate
ILP – In ow Perfomance Ratio
LaDA – Lagrangian Decomposition Algortihm
LD – Lagrangian Decomposition
PDP – Production Data Portal
PI – Productivity Index
P-line – Production line
PPOP – Production Planning Optimization Problem
PROSPER – Production and Systems Performance analysis software
RBC – Real Base Case
RTO – Real Time Optimization
SLP – Sequential Linear Programming
SOS1 – Special Ordered Sets of type 1
SOS2 – Special Ordered Sets of type 2
SQP – Sequential Quadratic Programming
TBC – Test Base Case
T-line – Test line
TOA – Total Optimization Algorithm
TWGP – Troll West Gas Province
TWOP – Troll West Oil Province
VLP – Vertical Lift Performance
INTRODUCTION
Oil production planning optimization has been practiced for many years and di erent models have been developed to assist oil producers to more e ciently extract the oil from reservoirs. In collaboration with Norsk Hydro ASA this work have had focus on the operational production planning at the Troll C platform. The special properties of the Troll reservoir make the production planning di cult. The most critical production factor when producing at the Troll C is the gas capacity. The oil is located in thin zones with large gas caps above, and one have to take into consideration the strongly rate dependent Gas/Oil Ratio (GOR) due to the gas-handling capacity of the rst stage separator. Today Norsk Hydro ASA use sophisticated software and technology to optimize their production.
In the oil industry, optimization models and methods have been used extensively for pro- duction planning problems. Wang [53] provides a thorough overview of oil production planning optimization, where both linear and nonlinear optimization techniques for com- mon oil production planning problems are presented. The most common concepts and components of an oil production optimization problem are presented in an informative way by Bieker, Slupphaug and Johansen [5]. This article gives a good overview of the subject and includes short descriptions on elements such as; processing facilities, well model updating, reservoir model updating, production planning, reservoir planning and strategic planning. Methods for well routing, gas lift and gas/water injections optimiza- tion are also reviewed. For more Troll speci c optimization Hauge and Horn [21] can be recommended. Here the challenges of operating and maintaining the Troll subsea system are described.
Today’s practice at Troll C is to allocate a certain amount of the total gas handling capacity to each cluster manually. Production optimization is often trial and error and good communication between operators and production engineers onshore are vital for success. By studying production data and simulate production, this experienced personnel decides on how much gas each cluster is allowed to produce. When this decision has been taken sophisticated software suites are used to optimize oil production by consider- ing the gas handling constraint. However, these software suites are not able to handle the routing of wells between the di erent production lines which also is part of the problem. Norsk Hydro ASA therefore needs to use brute force to check every routing combination for each cluster.
This master thesis develops a new solution methodology for the production optimization at Troll C. It considers the possibilities of assigning the gas capacities more properly to each cluster by solving all clusters simultaneously. The developed model is also going to handle routing of wells. Before developing this solution method the authors have stud- ied relevant literature about petroleum production and operational research. A Mixed Integer Linear Programming (MILP) has been formulated and solution methods for this model are proposed. The solution method has been implemented in Xpress-IVE and Visual Basic for Excel (VBA). All non-linearity are handled through piecewise lineariza- tion and the use of Special Ordered Sets of type 2. The resolution on the linearization is dynamic. The model is further decomposed by use of Lagrangean Decomposition to relax the gas-handling capacity, where the Subgradient Optimization method is used to update the Lagrange multiplier. Beasley [4] provides a good description on Lagrangian Decomposition, while Jackson and Grossmann [27] uses Lagrangian Decomposition on a production planning problem from the process industry with good results.
The time horizon considered is one week. This implies that the reservoir conditions are treated as constant in this problem formulation. Further the production from each well is described by a piecewise linearized well performance curve generated in the General Allocation Package (GAP) developed by Petroleum Experts. GAP has also been used to simulate di erent combinations of owrates for oil, gas and water to construct a piecewise linearized multi-dimensional pressure drop function dependent on these rates. As this Master thesis is a rst step into this kind of solution methodology no gas lift wells or gas lift risers are considered.
The most di cult parts when building a production planning models is the modeling of the in ow from the reservoir to the wells and the pressure drop in the pipeline network. These subjects have been treated by several others. Handley-Schachler, McKie and Quintero [19] uses the concept of Special Ordered Sets of type 2 (SOS2) to linearize well performance curves when solving production optimization problem by Sequential Linear Programming. Their work also uses multi-dimensional SOS2 to model the pressure drop in the pipeline system, though not explained thoroughly. Litvak, Clark, Fairchild, Fossum, Macdonald and Wood [35] uses ow correlations together with Equations of State to calculate the pressure gradients. Kosmidis, Perkins and Pistikopolous [33] presents a mixed integer non linear model (MINLP) for daily well scheduling in petroleum elds using analytic ow relations when modeling multiphase ow and pressure drop relations. This model considers all of the issues in our Master thesis and more, except for the gas-handling constraint.
The developed algorithm’s performance is tested by solving some test cases generated by the authors and on data from a real case. The real case data is provided by Norsk Hydro ASA. The data consist of GAP les containing all information about the two clusters, including Norsk Hydro’s best solution. This data makes it possible to generate well performance curves and pressure drop functions in GAP.
The results are presented and discussed, both with respect to the performance of the MILP model and the potential value when planning the production at Troll C. The results show that the dynamic linearization technique works effectively at the test cases; however it could not be used during testing of real data due to a malfunction. The Lagrange multiplier adjustment method works very well for both cases. The results from the real case shows that gas capacity are divided differently than the manual solution used today, however it produces a bit less oil than the current solution.
All in all the different parts of the developed algorithm works as intended, however the assumption made about the pressure drop modeling seems to be a bit coarse. Therefore this part of the MILP model needs further development and testing before this tool can be used as an optimization tool at Troll C.
As part of this Master thesis the authors have studied a wide variety of literature on petroleum production planning and optimization.
The remainder of the report is made up of two major parts; the rst part describes the problem and the current practice. It starts out with a general description of the different levels of oil production planning optimization problems in Chapter 2. Chapter 3 describes the operational production planning problem considered at Troll C. Chapter 4 closes the first major part by describing the different models used to solve these types of problems and present the system topology and the mathematical model for the production planning at Troll C.
PURPOSE OF THE STUDY
The purpose of this work is to develop an alternative solution method to optimized production
OBJECTIVE OF THE STUDY
The objective of production system analysis can be summarized as:
SCOPE OF THE STUDY
The productivity of the well depends on an efficient use of the compressional energy available in the reservoir allowing the reservoir fluids to flow toward the production separator. This techniques uses IPR or VLP.
DEFINITION OF VERTICAL LIFT PERFORMANCE
This is a plot of the pressure traverse in the vertical/inclined tubing of a production system. It is a plot of the pressure losses vs. production rate in the system. Multiphase flow correlations are used in calculating the VLP.
VERTICAL LIFT PRESSURE LOSSES
FACTORS AFFECTING THE VERTICAL LIFT PERFORMANCE
HOW TO RECEIVE PROJECT MATERIAL(S)
After paying the appropriate amount (#5,000) into our bank Account below, send the following information to
08068231953 or 08168759420
(1) Your project topics
(2) Email Address
(3) Payment Name
(4) Teller Number
We will send your material(s) after we receive bank alert
BANK ACCOUNTS
Account Name: AMUTAH DANIEL CHUKWUDI
Account Number: 0046579864
Bank: GTBank.
OR
Account Name: AMUTAH DANIEL CHUKWUDI
Account Number: 3139283609
Bank: FIRST BANK
FOR MORE INFORMATION, CALL:
08068231953 or 08168759420
BEFORE YOU READ THE ABSTRACT OR CHAPTER ONE OF THE PROJECT TOPIC BELOW, PLEASE READ THE INFORMATION BELOW.THANK YOU!
INFORMATION:
YOU CAN GET THE COMPLETE PROJECT OF THE TOPIC BELOW. THE FULL PROJECT COSTS N5,000 ONLY. THE FULL INFORMATION ON HOW TO PAY AND GET THE COMPLETE PROJECT IS AT THE BOTTOM OF THIS PAGE. OR YOU CAN CALL: 08068231953, 08168759420
WHATSAPP US ON 08137701720
THE IMPACT OF PRODUCTION PLANNING AND CONTROL ON PRODUCTIVITY
CHAPTER ONE
INTRODUCTION
1.1 Background of the study
The Impact of Production Planning on Productivity in the Seven up Bottling Company Kaduna
Production is one of the most important function of management in every organization. The essence of production is to prepare or forecast into the future in terms of production in an organization. As such Baryoko (2002) opined that production planning is mainly concerned with the directing and controlling of production process of an organization in order foster optimum utilization of human and material resources use in production, which brings about increase productivity and consequently profit maximization.
Similarly Nwachukwu (2007) opined that production planning involves the establishment of the overall strategy and process design required for the realization of effective production in an organization.
As such, production planning’s particularly very important in a developing country where resources are relatively scarce. In Nigeria, where many of the manufacturing firms are inexperienced interms of technical know-how, production planning become very imperative in other to avoid waste of resources. This is so because production planning is a blue print for action. As such all manufacturing organization who are into production need to plan their production in order to gain an optimum utilization of their plant and raw materials use in production.
However, Ayo (2006) opined that failure to engage in production planning give rise to inefficiency and lack of direction. It make the organizations to constantly put off fire instead of preventing its occurrence. This implies that production planning entails the identification of what at business plans to do in terms of growth, development and survival or continuous existence. But inspite of this, over 48.6% of Nigeria indigenous organizations do not undertake a formal plan and do not have production budgets.
It is against this background that the researcher deemed it very imperative to investigate the subject matter of this research. The impact of production planning on productivity in seven-up bottling company Kaduna.
1.2 STATEMENT OF THE PROBLEM
Production planning as earlier indicated is a very crucial aspect of any management process. Hence, efficient production planning becomes very imperative for the sustainability of an effective production system that would be cost effective and profitable.
But inspite of the importance of production planning and its positive impact on productivity, it is observed by Ayo (2006) that about 48.6% of Nigerian indigenous organizations do not undertake a formal plan and do not have production budgets. As such doubt is expressed about production planning in most organization in Nigeria. Hence the need to investigate the impact of production planning on the productivity of seven-up bottling company Kaduna become very imperatives and the subject matter becomes an empirical problem worthy of investigation.
1.3 OBJECTIVES OF THE STUDY
The central objective of the study is to investigate the impact of production planning on productivity in seven-up bottling company Kaduna. Other sub-objectives are as follows:
To determine production planning activities of seven-up bottling company Kaduna plant.
To evaluate the effect of production system in manufacturing of seven up products?
To explore the meaning of production planning
To evaluate the relevance of production planning concept.
1.4 SIGNIFICANCE OF THE STUDY
This study examines the impact of production planning on seven-up bottling company Kaduna plant. The significance of the study of this magnitude cannot be overemphasized. The government corporation, corporate organization, small and medium enterprise involved in manufacturing will really find this study useful, especially as they utilize the findings of this study. The study will also add to the existing knowledge on production planning and would also be useful to student and researchers.
The government corporation and corporate organizations involved in manufacturing can use the finding of this research in policy formulation regarding production planning.
Small and medium enterprise may also find the findings of this study really significant as they utilize the finding as a guideline in formulating their production planning.
The study will also contribute to knowledge by being a good reference material to students, scholars, resources and individuals who may wish to undertake similar research. This research can be used as a spring board to undertake their own work.
1.5 RESEARCH QUESTIONS
i. What are the production planed activities in seven-up bottling company kaduna
ii. What is the effect of production system in the manufacturing of seven up products?
iii. What is the meaning of production planning
iv. Is Production planning relevant to productivity.
1.6 SCOPE OF THE STUDY
The study covers the effect of production planning in seven-up bottling company Kaduna plant a
1.7 LIMITATION OF THE STUDY
Every research activity has its constraints this particular one is however not an exception, certain factors limit the process of writing this research work, which include:
Inadequate research materials the research: Materials on this subject matter were very few. The much relied library is filled up with outdated books, which are of little relevance to current research. Moreover, the cost of new and updated materials are exorbitant which make them not readily affordable.
Lack of cooperation of subjects: Sourcing for information from source of respondents was not an easy task, particularly with regards to some information that are regarded as classified or highly confidential to the company, the degree of cooperation of staff of seven-up bottling company was minimal
Low return of questionnaires in the process of gathering data, it was discovered that not all questionnaires issued out were returned. Some of them were also wrongly filled which means a great deal of relevant information was held back, and which could have enhanced the quality of this study.
Inaccessibility to subjects: Another constraint encountered during the research is the inaccessibility to the inventory management, manager who was not always available to provide desired data.
1.8 DEFINITION OF TERM
Production: this refers to an outline process that is aimed at satisfying human needs and wants. That is the only process that deals with the transformation of raw materials into finished good.
Planning: is a set of managerial function which prepares forecast into the future and ensure that decision regarding the use of human and material resources involved in production is effectively managed
Production planning: This is a managerial function concern with planning directing and controlling the method to be used in manufacturing a product.
Schedule: This is prescribing of when and where each operation necessary for the manufacturing of a product is to be performed or the establishment of time at which to begin or complete each event or activity.
Raw materials: this are the basic inputs required for productions.
Work-in-progress: There are semi-manufactured product. Product that is uncompleted and skill in the process of production.
Finished good: product that has been completed and ready for consumption.
Lead time: This refers to the expected time of delivery.
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