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The main objective in oil production system using gas lift technique is to obtain the optimum gas injection rate which yields the maximum oil production rate. Relationship between gas injection rate and oil production rate is described by a continuous gas lift performance curve (GLPC). Obtaining the optimum gas injection rate is important because excessive gas injection will reduce production rate, and also increase the operation cost. In this paper, we discuss a mathematical model for gas lift technique and the characteristics of the GLPC for a production well, for which one phase (liquid) is flowing in the reservoir, and two phases (liquid and gas) in the tubing. It is shown that in certain physical condition the GLPC exists and is unique. Numerical computations indicate unimodal properties of the GLPC. It is also constructed here a numerical scheme based on genetic algorithm to compute the optimum oil production.

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Intermittent gas lift is commonly known as one of artificial lift technique that is implemented into wells with low bottom-hole pressure. The injection process will be in cycles with spesisified period. Previously, intermittent gas lift is modeled using nodal analysis method, which is intersection between inflow performance relationship (IPR) and tubing performance relationship (TPR) and mechanistic method. Both solutions required a hard-working numerical approach to solve the model. In this paper, a new-simple-effective analytical approach is introduced to solve a set of equations that was derived from Transport Reynolds Theorem. The work is slightly different from the Liao and Bordalo model. A complex equation that represents movement of the gas-column, the slug, and the film from surface to sub-surface is presented. Gas column is formed during the injection time and the injected gas will push the oil to the surface. The dynamics of the gas column greatly affect the success of the process of oil production with gas injection method intermittently. The gas-column pressure, surface injection pressure and rate, gas column density, and surface gas injection density are successfully obtained using this approach. The gas-column pressure in the tubing is strongly influenced by the surface gas injection rate. By controlling the surface gas injection rate then the column-gas pressure in the tubing will be stable. The stable gas-column pressure leads to the stable oil production. This research is genuinely believed to be the first analytical approach of determining intermittent gas lift parameters. The results are give huge advantageous and applicable either for the educational as well as industrial purposes.

Comparing the quality of basic reservoir rock properties is a common practice to locate new infills or development wells for optimizing an oil field development using a reservoir simulation. The conventional technique employs a manual trial and error process to find new well locations, which proves to be time-consuming, especially, for a large field. Concerning this practical matter, an alternative in the form of a robust technique was introduced in order that time and efforts could be reduced in finding best new well locations capable of producing the highest oil recovery. The objective of the research was to apply Genetic Algorithm (GA) in determining wells locations using reservoir simulation to avoid the manual conventional trial and error method. GA involved the basic rock properties, i.e., porosity, permeability, and oil saturation, of each grid block obtained from a reservoir simulation model, which was applied into a newly generated fitness function formulated through translating the common engineering practice in the reservoir simulation into a mathematical equation and then into a computer program. The maximum of the fitness value indicated a final searching of the best grid location for a new well location. In order to evaluate the performance of the generated GA program, two fields that had different production profile characteristics, namely the X and Y fields, were applied to validate the proposed method. The proposed GA method proved to be a robust and accurate method to find the best new well locations for field development. The key success of this proposed GA method is in the formulation of the objective function.