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Improved Firefly Algorithm to Optimize GRNN's Steam Flood Development Effect Prediction Method

A technology of firefly algorithm and development effect, which is applied in the field of steam flooding development effect prediction, can solve the problems of high cost and low accuracy, and achieve fast and accurate prediction, fast training speed, and the effect of optimization and adjustment

Active Publication Date: 2022-02-25
NORTHEAST GASOLINEEUM UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to provide an improved firefly algorithm optimization GRNN steam flooding development effect prediction method, this improved firefly algorithm optimization GRNN steam flooding development effect prediction method is used to solve the low accuracy and high cost of the existing steam flooding development effect prediction method The problem

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  • Improved Firefly Algorithm to Optimize GRNN's Steam Flood Development Effect Prediction Method
  • Improved Firefly Algorithm to Optimize GRNN's Steam Flood Development Effect Prediction Method
  • Improved Firefly Algorithm to Optimize GRNN's Steam Flood Development Effect Prediction Method

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[0058] Below in conjunction with accompanying drawing, the present invention will be further described:

[0059] refer to figure 1 , this improved firefly algorithm optimizes the GRNN steam flooding development effect prediction method, the specific steps are as follows:

[0060] Step 1: Collect on-site steam flooding development effect data as sample data, including original oil saturation, crude oil viscosity (mPa s), oil layer depth (m), permeability (mD), effective oil layer thickness (m), original temperature ( ℃), original pressure (MPa), well pattern area (m 2 ), steam injection rate (m 3 / d), steam injection dryness, steam injection pressure (MPa) a total of 11 main factors, select the cumulative oil-steam ratio as an index to measure the quality of steam flooding development effect;

[0061] Step 2: Since the collected data are often not in the same order of magnitude, in order to obtain a better prediction effect, it is necessary to map the collected data to [-1,1...

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Abstract

The present invention relates to an improved firefly algorithm to optimize the GRNN steam drive development effect prediction method, which is based on the steam drive development effect data collected on site, by introducing a chaos optimization operator into the standard firefly algorithm to initialize the population, and at the same time introducing a differential mutation operator to complete The firefly individual self-adaptive mutation increases the diversity of the population, speeds up the convergence speed, and improves the evolvability of the population. The improved firefly algorithm is used to optimize the smooth factor σ of the GRNN network. The optimization goal is the root mean square error of the fitting error. The optimal prediction model of steam flooding development effect realizes the prediction of steam flooding development effect. The present invention optimizes GRNN to predict the development effect of steam flooding by adopting the improved firefly algorithm, improves the speed and accuracy of prediction, can guide the compilation of on-site steam flooding development schemes, realizes the optimization and adjustment of steam flooding development schemes, and maximizes the economic benefits.

Description

technical field [0001] The invention relates to a method for predicting the development effect of steam flooding, in particular to a method for predicting the effect of steam flooding by improving the firefly algorithm and optimizing GRNN. Background technique [0002] As an efficient development method, steam flooding is the main development direction of heavy oil reservoir development, and realizing the prediction of steam flooding development effect is the key to the success of steam flooding. At present, the methods for predicting the development effect of steam flooding include empirical methods based on field data, indoor experimental simulation methods and numerical simulation methods. The empirical method is simple to operate, but the prediction accuracy is low; the indoor experimental method is not consistent with the field, and the prediction results are quite different from the actual situation. At present, there are some numerical simulation software that can be...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/11G06Q10/04G06Q50/02G06N3/00G06N3/04
CPCG06N3/006G06N3/0418G06Q10/04G06Q50/02Y02A10/40
Inventor 倪红梅王维刚刘永建刘金月时贵英韩玉祥
Owner NORTHEAST GASOLINEEUM UNIV
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