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Quantification Method of Physical Parameters Based on Neural Network Algorithm

A neural network algorithm and physical parameter technology, applied in the field of seismic data reservoir prediction, can solve problems such as weak convergence ability, incorrect relationship establishment, large deviation of prediction results, etc.

Active Publication Date: 2016-06-29
BC P INC CHINA NAT PETROLEUM CORP +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

One is that when classifying, different or different samples are often forcibly determined as the same class due to lack of understanding and many influencing factors, resulting in incorrect relationship establishment, which leads to large deviations in prediction results and incorrect classification; Due to the inaccurate grasp of the model during establishment, problems such as weak convergence ability, large classification error, multiple experiments, difficulty in grasping the effect, and failure to get approval have occurred; third, the prediction results have greatly weakened its physical meaning and weakened the rich continuous changes. Earthquake and geological information

Method used

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  • Quantification Method of Physical Parameters Based on Neural Network Algorithm
  • Quantification Method of Physical Parameters Based on Neural Network Algorithm
  • Quantification Method of Physical Parameters Based on Neural Network Algorithm

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Embodiment 1

[0029] A physical parameter quantification method based on a neural network algorithm, which extracts attributes related to physical parameters from seismic data and adds them to the input of the neural network; adds known physical parameters to the output of the neural network, and then according to the neural network The algorithm establishes the relationship between output and input, and applies this relationship to all seismic data to realize the quantitative prediction of physical parameters.

[0030] Described method specifically comprises the steps:

[0031] a. Collect the physical parameters of wells drilled in various regions or geological structures, select the category of parameters to be predicted (such as porosity, permeability), and determine the value of this parameter;

[0032] b. Normalize the known physical parameters, the formula is: vv=(v-vmin) / (vmax-vmin);

[0033] (V is the size of the parameter itself, vv is the normalized size, vmin and vmax are the mi...

Embodiment 2

[0041] figure 1 It is a schematic diagram of a commonly used BP network structure. The network consists of an input layer, one or several intermediate layers, and an output layer. Each layer contains multiple nodes, and the nodes between layers are connected to each other to form a network. The known information X=(x1, x2,...,xn) is input from the input terminal, and reaches the output layer through network operations, and the output value Y=(y1, y2,...,ym) is obtained. In pattern recognition, the output value expresses the classification result, such as 1 for the first category, 0.5 for the second category, 0 for the third category, etc.

[0042] As a quantitative prediction of physical parameters, each node at the output end has the same type of parameters, but the values ​​​​are different, such as y1=10, y2=4, y3=0, etc.

[0043] For example, suppose there are 3 wells in a certain area or geological structure, the porosity of well 1 is 13, the porosity of well 2 is 6, and ...

Embodiment 3

[0045] This embodiment is a quantitative prediction of gas field permeability.

[0046] 1. Add the following permeability parameters of known well reservoir intervals to the output of the neural network.

[0047]

[0048] 2. Extract multiple attribute parameters for each channel of the seismic data reservoir section, and input them to the input terminal of the neural network.

[0049] 3. Establish the network relationship (weight) between input and output.

[0050] 4. Apply weights to all seismic data to obtain prediction results.

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Abstract

The invention discloses a physical property parameter quantification method based on a neural network algorithm. The method comprises the steps of: extracting properties, related to physical property parameters, from seismic data, and adding the properties to an input terminal of a neural network; and adding known physical property parameters to an output terminal of the neural network, establishing a relationship between output and input according to the neural network algorithm, and applying the relationship to all the seismic data so as to realize quantified prediction on the physical property parameters. According to the method, the known physical property parameters and seismic property parameters are combined so as to realize the quantified prediction on the physical property parameters, the predicted results are quantified data, and various physical property parameter values of reservoirs can be obtained.

Description

technical field [0001] The invention relates to a method for quantifying physical property parameters based on a neural network algorithm, and belongs to the field of seismic data reservoir prediction for oil and gas field exploration and development. Background technique [0002] Oil and gas exploration and development often need to know the reservoir conditions, that is, what physical and geological characteristics the reservoir has, and these characteristics are expressed by various parameters. In particular, it is necessary to know the size of the parameter value in order to finely describe the reservoir and obtain accurate reservoir information, thereby providing necessary and effective quantitative basis for exploration and development research, evaluation and engineering operations. This requires the development from the usual qualitative research of reservoirs to quantitative research, and quantitative prediction needs to be carried out in the early stage. [0003] ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01V1/28
Inventor 吴大奎戴勇邓常念胡天文
Owner BC P INC CHINA NAT PETROLEUM CORP