ABSTRACT
Hydrocarbon production in the petroleum industry is often constrained by reservoir heterogeneity, deliverability and capacity of surface facilities, also optimization technique in the petroleum industry requires execution of several iterative runs by comparing various solutions until an optimum or satisfactory solution is found.
In this study a comparative economic analysis to aid the optimization of petroleum production was done. Two key variables, tubing sizes and choke sizes were considered, their sensitivity to production was also determined.
A prudent approach to optimizing petroleum production is by statistical and sensitivity analysis, specifically, Nodal Analysis and @Risk software were used in this work. The nodal analysis procedure consists of selecting a division point or node in the well, the system at that point was analyzed differently to optimize performance in the most economical manner, an integral analysis of the entire production system was also considered. Using @Risk software (Monte Carlo simulation), risk analysis of the objective function was done. Monte Carlo simulation sampling is a traditional technique for using random or pseudo-random numbers to sample from a probability distribution.
Substantial findings of this study shows that the tubing size of 1.90-inch had an optimal rate of production for deeper reservoir conditions used in this research using the Nodal analysis technique, also Monte Carlo simulation proves that the price of oil has the highest impact on profit for the probabilistic period of 5, 10, and 15 years followed by the rate of production while the cost of tubing has the least effect.
CHAPTER ONE:
1.0 INTRODUCTION
1.1 BACKGROUND OF THE STUDY
Hydrocarbon production in the petroleum industry are often constrained by reservoir heterogeneity, deliverability and capacity of surface facilities. As optimization algorithms and reservoir simulation techniques continue to develop and computing power continues to increase, upstream oil and gas facilities previously assumed not to be candidates for advanced control or optimization have being given new considerations (Clay et al., 1998).
An optimization technique is a procedure which is executed iteratively by comparing various solutions till an optimum or a satisfactory solution is found.
However, Wang (2003) addressed some problems associated with optimizing the production rates, lift gas rates, and well connections to flow lines subject to multiple flow rate and pressure constraints to achieve certain short-term operational goals. This problem is being faced in many mature fields and is an important element to consider in planning the development of a new field.
Nonlinear Optimization, also known as nonlinear programming has proven itself as a useful technique to reduce costs and to support other objectives, especially in the refinery industry whereas linear optimization is a method applicable for the solution of problems in which the objective function and the constraints appear as linear functions of the decision variables. The constraint equations may be in the form of equalities or inequalities. Furthermore, it had been used to determine the most efficient way of achieving optimal outcome for example, to maximize profit or to minimize cost in a given mathematical model. It can be applied to numerous fields like business or economics situations, and also in solving engineering problems. It is useful in modeling diverse types of problems in planning, routing, scheduling, assignment and design.
Carroll (1990) applied a multivariate optimization techniques to a field produced by a single well. The model used in his research includes a single oil well field. However, only the separator model was compositional, and no engineering parameters were allowed to vary with time. He used two types of optimization routines that is, Gradient methods and Polytope methods. However, Ravindran (1992) applying the same technique but allowed for gas-lift and engineering parameters to vary with time. Again, Fujii in 1993 improved the technique by allowing a network of wells connected at the surface, he also studied the utility of genetic algorithms for petroleum engineering optimization.
Regarding some paper view, the application of optimization techniques to solve problems in the upstream sector of petroleum Exploration and Production has been surprisingly limited not taking into account the enormous important of the E&P activities to the hydrocarbon enterprise and to the global energy systems and the economy as a whole.
Over time, development in the petroleum industry resulted in optimization methods improving in its ability to handle various problems. In optimization of a design, the design objective could be to minimize the cost of production or to maximize the efficiency of production.
In this work methodology used include Statistical and Sensitivity Methods:
o Nodal Analysis
o @Risk Software (Monte Carlo simulation)
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