A prediction method for remaining oil distribution of a water flooding development oil field based on deep learning

A technology of deep learning and prediction methods, which is applied in neural learning methods, prediction, and fluid mining, etc., and can solve problems such as no application of deep learning methods.

Active Publication Date: 2019-06-28
CHINA UNIV OF PETROLEUM (EAST CHINA)
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  • A prediction method for remaining oil distribution of a water flooding development oil field based on deep learning
  • A prediction method for remaining oil distribution of a water flooding development oil field based on deep learning
  • A prediction method for remaining oil distribution of a water flooding development oil field based on deep learning

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[0064] The implementation of the present invention will be described in detail below with examples, so as to fully understand and implement the implementation process of how the present invention uses technical means to solve technical problems and achieve technical effects.

[0065] The invention discloses a method for predicting the distribution of remaining oil in water drive development oilfields based on deep learning, such as figure 2 shown, including the following steps:

[0066] S1. Select a small layer in a certain block as an example, and collect its reservoir structure and well location distribution map (see Figure 5 ), well location distribution, development time, injection-production parameters oil-water viscosity, reservoir porosity and permeability, reservoir thickness, relative permeability curve, reservoir oil-bearing area, reservoir boundary conditions, and generate a learning sample library;

[0067] S2. Grid the reservoir, and each unit body corresponds ...

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Abstract

The invention discloses a prediction method for remaining oil distribution of a water flooding development oil field based on deep learning. The prediction method comprises the following steps: collecting and arranging data; performing gridding on the reservoir; preprocessing the data; establishing an SVM classification model for judging whether the unit bodies are exposed to water or not; establishing a neural network model for residual oil distribution prediction; training and parameter adjustment of an SVM classification model and a neural network model; and selecting a target block for model verification by taking the prediction accuracy and the prediction time consumption as evaluation indexes. The residual oil distribution can be quickly and accurately predicted by utilizing the field data of the oil field.

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/02G06N3/04G06N3/08E21B43/20
Inventor 谷建伟王依科周梅刘巍田同辉郑家朋
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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