Comparative Economics Of Petroleum Production Optimization Techniques

Project and Seminar Material for Petroleum Engineering

Comparative Economics Of Petroleum Production Optimization Techniques


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:

  1. Nodal Analysis
  2. @Risk Software (Monte Carlo simulation)

These methods above will be used to identify key variables, the most sensitive variables of production, and evaluate optimization techniques.


1.2 Statement of Problem

In previous works done by many researchers, they investigated many optimization techniques for tackling the reservoir problem which involve much simpler reservoir representations than those employed by the simulators. Saputelli et al., (2005) even argued that multivariable optimization has not fully penetrated the hydrocarbon industry sector of the E&P activities. This research will emphasis mainly on the comparative economic analysis of petroleum production optimization techniques since it was not taken into consideration by the many researchers who discuss the petroleum production optimization techniques.


1.3 Aim and Objectives of the Study

The aim of this research is to do a comparative economics Analysis of petroleum production optimization techniques in optimizing the tubing and choke sizes, using the idea of economic analysis while the objectives include:

  1. To identify key variables that affects tubing and choke sizes production optimization.
  2. To determine the sensitivity of tubing and choke sizes for production optimization.
  3. To evaluate the techniques required in the optimization of tubing and choke sizes.
  4. To perform economic analysis on the optimal tubing and choke sizes selected.

1.4 Organization of the Thesis

This thesis work consisted of five chapters as well as the references and appendices. Chapter one comprised the introductory part which unveiled the study and gave the background, statement of the problems, purpose and objectives of the thesis, and the organization of the thesis.

The second chapter presented literatures review that comprised of previous work done by other researchers on the subject matter, while chapter three focuses on the methodology.

Chapter four consisted of data presentation, interpretation, and discussion of the findings and chapter five concludes and gives recommendation followed by references and appendices.


Chapter Five


5.0 Conclusion and Recommendation

5.1 Conclusions

  1. This study has been able to address the problem of production optimization using four (4) production tubing sizes and a five (5) choke sizes.
  2. 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.
  3. 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).
  4. Results shows that the tubing size of 1.90-inches generate more revenue and profit regardless of the cost of tubing.
  5. 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.

5.2 Recommendations

  1. Alternatively, other function approximation methods like linear and nonlinear optimization could be interesting to implement with this optimization problem to improve accuracy.
  2. 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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