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A method for predicting remaining oil distribution in waterflood development oilfields 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: 2020-10-27
CHINA UNIV OF PETROLEUM (EAST CHINA) +1
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  • A method for predicting remaining oil distribution in waterflood development oilfields based on deep learning
  • A method for predicting remaining oil distribution in waterflood development oilfields based on deep learning
  • A method for predicting remaining oil distribution in waterflood development oilfields 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.

Description

technical field [0001] The invention belongs to the field of oil and gas field development, and in particular relates to a method for predicting the distribution of remaining oil in water drive development oilfields based on deep learning. Background technique [0002] With the development of water flooding, most oilfields in China have entered the "double high" stage of high water cut and high recovery. It is very important to know the remaining oil to provide an important basis for secondary and tertiary oil recovery. There are many methods to study the distribution of remaining oil, mainly using reservoir numerical simulation, cross-well tracer method, physical simulation, well test analysis, well logging method and other methods. In recent years, although some scholars have begun to use neural network technology in the development of oil and gas fields, there are very few related studies at home and abroad, and no examples of using deep learning methods to predict the d...

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Application Information

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Patent Type & Authority Patents(China)
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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