Rock physical elastic parameter forward modeling method, forward modeling device and electronic equipment

A technology of petrophysical and elastic parameters, applied in the field of oil and gas geophysical exploration, can solve the problems that cannot meet the needs of high-precision seismic interpretation, and achieve the effect of improving the accuracy of forward modeling

Pending Publication Date: 2022-05-03
CHINA PETROLEUM & CHEM CORP +1
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiency of the conventional Xu-White rock physics model, which cannot meet the current high-precision seismic interpretation requirements, the present invention proposes a forward modeling method of rock physics elastic parameters based on deep learning

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Rock physical elastic parameter forward modeling method, forward modeling device and electronic equipment
  • Rock physical elastic parameter forward modeling method, forward modeling device and electronic equipment
  • Rock physical elastic parameter forward modeling method, forward modeling device and electronic equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0081] In this embodiment, a deep-water drilling well in a sandstone block in southern China is taken as an example, and the method provided by the present invention is used to carry out petrophysical forward modeling.

[0082] figure 2 A technical flow chart of petrophysical forward modeling according to an embodiment of the present invention is shown.

[0083] image 3 The structural diagram of the deep feedforward neural network is shown. The deep feedforward neural network includes an input layer, an output layer, and two or more hidden layers.

[0084] Figure 4 The depth domain logging data of the drilling well in the example is shown. It can be seen from the figure that the diameter expansion phenomenon occurs in the deep section of the well. Due to the chemical characteristics, the wellbore becomes seriously unstable during deepwater drilling. For this reason, in the embodiment, the deep neural network training is carried out using the well section data with stabl...

Embodiment 2

[0095] Such as Figure 11 As shown, this embodiment provides a forward modeling device for rock physical elastic parameters based on deep learning, including:

[0096] The acquisition unit acquires the depth domain logging data of the research area and forms the training sample set of the deep feedforward neural network;

[0097] The training unit constructs a rock physics forward modeling model based on a deep feed-forward neural network, uses the training sample set for training, and obtains a nonlinear mapping relationship between rock parameters and elastic parameters;

[0098] The forward modeling unit performs rock physical forward modeling of elastic parameters on the target well section based on the rock physical forward modeling model.

[0099] The acquisition unit, the training unit and the forward modeling unit are sequentially connected by communication, the acquisition unit provides the training sample set to the training unit, and the petrophysical forward model...

Embodiment 3

[0101] This embodiment provides an electronic device comprising: a memory storing executable instructions; a processor running the executable instructions in the memory to realize the above-mentioned forward modeling of rock physical elastic parameters based on deep learning method.

[0102] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0103] The memory is used to store non-transitory computer readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache). The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, and the like.

[0104] The processor may be a central processing unit (CPU) or other form of proces...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a rock physical elastic parameter forward modeling method and device and electronic equipment, and the method comprises the steps: obtaining depth domain logging data of a research region, and forming a training sample set of a depth feedforward neural network; constructing a rock physical forward model based on a deep feedforward neural network, and training by using the training sample set to obtain a nonlinear mapping relationship between rock parameters and elastic parameters; and based on the rock physical forward modeling model, rock physical forward modeling of elastic parameters is carried out on a target well section. According to the method, a conventional Xu-White rock physical forward modeling model is replaced with the deep feed-forward neural network model, the internal relation between the rock parameters and the elastic parameters can be fully excavated, and therefore the nonlinear mapping relation between the rock parameters and the elastic parameters is established, and the forward modeling precision of the elastic parameters is improved.

Description

technical field [0001] The invention belongs to the field of oil and gas geophysical exploration, and in particular relates to a petrophysical forward modeling method, a forward modeling device and electronic equipment for improving the prediction accuracy of elastic parameters. Background technique [0002] The three elastic parameters of compressional wave velocity, shear wave velocity and density are bridges connecting various physical properties of rocks and seismic wave exploration. The physical quantity reflecting the fluid properties can be obtained by using the elastic three parameters, thereby reducing the multi-solution of seismic amplitude interpretation, and has important applications in AVO analysis of seismic exploration data, pre-stack inversion, and lithology, physical and fluid identification of reservoirs, etc. . However, in actual production, due to various reasons, the three parameters of elasticity are missing and incomplete, which seriously affects the...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G01V1/30G01V1/40
CPCG01V1/306G01V1/40G01V2210/6242
Inventor 谢玮胡华锋马灵伟姚铭钟晗雷朝阳
Owner CHINA PETROLEUM & CHEM CORP
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap