Steam flooding injection and production scheme determining method based on stochastic disturbance particle swarm optimization
A particle swarm algorithm and random perturbation technology, which is applied in the directions of calculation, production of fluid, and calculation model, etc., can solve the problems of non-optimal injection and production scheme, slow calculation speed, and many adjustment parameters, so as to improve the accuracy, simple operation, and improve the economic effect
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specific Embodiment approach 1
[0030] Specific embodiment one: the method for determining the injection-production scheme of steam flooding based on random disturbance particle swarm algorithm of the present invention, it comprises the following steps:
[0031] Step 1: Obtain the reservoir description, original formation conditions, underground fluid properties, underground rock properties, parameters related to injection wells and production wells of a heavy oil reservoir area, and establish a steam flooding analytical model for the oil reservoir area;
[0032] Step 2: set the parameters of the particle swarm optimization algorithm, the parameters include: particle population size N, particle dimension L, particle speed range of each dimension [V imin , V imax ], where i=1, 2, ..., L, the range of each dimension position of the particle [X imin , X imax ], where i=1, 2,..., L, inertia weight w, learning factor C 1 and C 2 , the disturbance step size step, the maximum number of iterations Iter_Max and t...
specific Embodiment approach 2
[0050] Embodiment 2: This embodiment differs from Embodiment 1 in that when the number of iterations per interval is m, 0<m<Iter_Max, re-initialize the position and velocity of the worst particle in the initial population.
[0051] Principle of the present invention: particle swarm algorithm is an evolutionary computing technology proposed by Dr. Kennedy and Dr. Eberhart, and its core idea is the simulation of biological social behavior. Assuming that there is only one piece of food in the food search area, all the birds do not know where the food is. Kennedy et al. believe that there is mutual exchange of information between birds. By estimating their own fitness value, they know how far away the current position is from the food. How far away, so searching the surrounding area of the bird closest to the food is the simplest and most effective way to find the food, and the group can be optimized through the collective cooperation among the birds. Particle swarm optimization...
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