Utilization of Nodal Analysis Technique for Selecting the Optimum Tubing Size to Achieve an Optimum Production Rate in Naturally Flowing Well via Modeling
CHAPTER ONE
AIMS AND OBJECTIVE OF THE PROJECT
The aim of this project is to use the nodal analysis technique for selecting the optimum tubing size to achieve an optimum production rate in naturally flowing well via modeling. The objectives of this project include:
- To identify key variables that affect tubing sizes production optimization.
- To determine the sensitivity of the tubing sizes for production optimization.
- To evaluate the techniques required in the optimization of tubing sizes.
CHAPTER TWO
REVIEW OF RELATED LITERATURE
Introduction
This chapter presents the review of related literature on the utilization of nodal analysis technique for selecting the optimum size to achieve an optimum production rate in naturally flowing well via modeling. Views and opinions of other authors will be presented as follows.
OVERVIEW OF PETROLEUM PRODUCTION OPTIMIZATION TECHNIQUES
Optimization of production operations can is key in increasing production rates and reducing production cost. In the later part of the 1940s, a mathematical optimized field was introduced (Lenstra et. al., 1991). Regardless of its short history in the petroleum industries it has been developed into a more advanced field with deep specialization and great diversification. It also include numerical techniques such as linear and nonlinear optimization, integer programming, network flow theory and dynamic optimization and combinatorial optimization, stochastic programming, and so on.
THE PETROLEUM PRODUCTION OPTIMIZATION TECHNIQUES
In the oil and gas industries most commercial reservoir simulators and flow rate constraints on facilities are handle sequentially by ad-hoc rules. In addition to the reservoir simulators, the optimization of lift gas is separately done from well rates allocation. As regard to the nonlinear nature of the optimization problem and complex interaction, the results from such procedure can be unsatisfactory.
In September 2003, Wang et al. presented in one of their paper, a procedure for the simultaneous optimization of well rate, lift gas rates, and connections of wells subject to multiple pressure, flow rate and velocity constraints. When they performed the technique, it was successful but its limitation was in handling flow interactions among wells when allocating well rate and lift-gas rates.
In that regard, a new formulation for the problem of simultaneously optimization of the allocation of well rates and lift gas rates was extended using the research work of Wang et al., In that extension the optimization problem was solved by a sequential quadratic programming algorithm, which is a derivative-based nonlinear optimization algorithm. However the result obtained from the extended work of Wang et al. showed that the procedure is capable of handling flow interactions among wells and can also be applied to a variety of optimization problems of varying complexities and size.
CHAPTER THREE
METHODOLOGY
INTRODUCTION
In this research work, methodology used is qualitative. A qualitative methodology involved describing in details specific situation using research tools like interviews, surveys, and observations. In this type of approach the researchers tends to be inductive which means that they develop a theory or look for a pattern of meaning on the basis of the data that they collected. This involves moving from the specific to the general and is sometimes called a bottom-up approach.
STATISTICAL AND SENSITIVITY ANALYSIS
Sensitivity analysis is any systematic, common sense technique that is used to understand how risks are estimated and in particular, risk-based decisions are dependent on variability and uncertainty in the factors contributing to risk. In short, it identify what is “driving” the risk estimates. However, it is also used in both point estimate and probabilistic approaches to identify and rank important sources of variability as well as important sources of uncertainty.
CHAPTER FOUR
DATA FINDING AND ANALYSIS
INTRODUCTION
In this chapter, three reservoir conditions of tubing sizes and choke sizes where determining to give the optimal production and to satisfy the economics needed in order to optimize production and reduce cost.
In determining the tubing sizes and choke sizes that optimizes production, the following equations were used: Vogel and Gilbert Correlations in order to computing various parameters. These equations are:
CHAPTER FIVE
CONCLUSION AND RECOMMENDATIONS
INTRODUCTION
This chapter presents the conclusion and recommendations for further studies.
CONCLUSION
- This study has been able to address the problem of production optimization using four (4) production tubing sizes and a five (5) choke sizes.
- It was observed that at a depth of 4264 ft, the optimal tubing size was 2.375-inches with a rate of production (Q) of 572 stb/d while at a higher depth of 10232 and 14076 ft, the optimal tubing size were both 1.90-inch with the rate of production (Q) 560 and 619 stb/d respectively.
- Regards to choke size, it was observed from the results obtained that all reservoir conditions required similar chokes sizes of 60/64 of an inch for optimal production with all having approximately the same rate of production (Q).
- Results shows that the tubing size of 1.90-inches generate more revenue and profit regardless of the cost of tubing.
- Sensitivity study shows that the price of oil and rate of production greatly impact profit generation while the cost of tubing have a very little effect on profit.
RECOMMENDATIONS
- Alternatively, other function approximation methods like linear and nonlinear optimization could be interesting to implement with this optimization problem to improve accuracy.
- Perhaps the biggest improvement would be to demonstrate the use of different objective criteria in the optimization process. This would clearly demonstrate the inherent advantages of nonlinear optimization over exhaustive iteration.
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