Reservoir scheduling method based on optimal convolution two-dimensionalization

Abstract

A reservoir scheduling method based on optimal convolution two-dimensionalization. The method comprises a step 1 of performing convolution processing on the input data based on a multi-objective optimization algorithm; a step 2 of constructing a dynamic scheduling model based on a convolutional neural network; and a step 3 of generating an evaluation model and an adjustment scheme, including a training section and a real-time scheduling section. The method, in combination with a deep neural network algorithm and a weight sharing technology, trains a deep artificial neural network to understandthe implicit knowledge in a scheduling scene through a large amount of scheduling historical data provided by a big data system, researches the time-space correlation of the input reservoir dynamic refinement comprehensive scheduling data, reduces the number of weights in each layer in the model construction by a neuron link method of sharing weight, increases the depth of the model so that the network fully recognizes the reservoir dynamic refinement comprehensive scheduling, finds out a deeper structure in the reservoir dynamic refinement comprehensive scheduling process, and finally completes a process of constructing a dynamic scheduling model with fast response and high accuracy.

Description

technical field [0001] The invention belongs to a scheduling method for storage capacity of a reservoir. Background technique [0002] For the reservoir scheduling problem, the mainstream method abstracts the actual problem by establishing a mathematical model and combines the optimization heuristic algorithm to solve it, which can obtain higher solution accuracy when the problem scale is small. However, in the context of big data, the explosive growth of production parameters in the production environment and strict scheduling time indicators put forward further requirements for scheduling methods. At present, it seems that the traditional scheduling algorithm cannot quickly respond to scheduling problems while processing massive scheduling data. Contents of the invention [0003] The present invention overcomes the above-mentioned shortcomings of the prior art, and proposes a two-dimensional optimal convolution-based reservoir scheduling method. [0004] Aiming at the ...

AI Technical Summary

Problems solved by technology

At present, it seems that the traditional scheduling algorithm cannot quickly respond to scheduling problems while processing massive scheduling data.

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