Research On Oil And Gas Allocation Based On Immune Particle Swarm Optimization Algorithm

After several decades of exploration and development, most oilfields of China have already entered a mid and late stage. The outputs of the oilfields decrease gradually. Other problems also come along. The reserves are insufficient. Newly increased resources have trouble taking the place of the old resources. It is difficult to maintain high exploitation rates, low water content and stable production. The costs of the crude oil increase rapidly. The exploiting enterprises are lack of capital and means of raising capital. A large proportion of the production facilities are not up-to-date. Facing the current resources and technology situations, how the oil enterprises determine the annual outputs and the future investments, and give scientific and reasonable distributions are important issues that the oil companies will do every year. Therefore, use optimization principles and then make scientific plans of productions and investments are important sustainable goals for each oilfield.In actual practice of the oil and gas allocation, in order to obtain the optimal plans, decision makers often need to consider many factors and multiple targets, however, some are incompatible and even contradictory. According to the operation situation of the oil companies, the author proposes that multiple objective programming can be used to solve this problem and establishes a multiple objective programming model, in which the total output is maximized, the total investment is minimized and the gross profit is maximized.For solving the model, the author put the Immune mechanism into the original particle swarm optimization algorithm, and designed a immune particle swarm optimization algorithm which is more suitable to solve the multi-objective optimization problem (Immune-Particle Swarm Optimization, I-PSO).At last, use an oil field as an example, and established its multi-objective allocation plan in 2009.Compared with the actual allocation of the oilfield, the experimental results show that this algorithm is effective to solve the multi-objective optimization problems.