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)