Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

In-situ wetting angle measuring device and wetting angle determining method based on deep learning

A deep learning and wetting angle technology, applied in neural learning methods, measuring devices, scientific instruments, etc., can solve the problem that it is difficult to meet the needs of high-precision wetting angle measurement, wettability measurement results deviate from reality, and experimental results are inaccurate. and other problems, to achieve the effect of improving measurement accuracy and recognition accuracy, avoiding large calculation errors and high-precision recognition

Active Publication Date: 2021-06-29
CHINA UNIV OF PETROLEUM (BEIJING)
View PDF14 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The existing wetting angle measurement devices do not meet the actual needs of oilfield development / enhanced oil recovery research, ignoring the adsorption of crude oil components on the rock surface and the mass transfer between oil and water under reservoir conditions, which leads to the deviation of the measured wettability measurement results In fact, it has affected engineering practice to a certain extent
In addition, the current wetting angle determination is mainly obtained through the empirical formula of the fitting rule. Due to the influence of human factors (baseline determination) and the applicability of the formula, there are large errors in the experimental results, and it is difficult to meet the high-precision wetting angle. Determination of needs

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
  • In-situ wetting angle measuring device and wetting angle determining method based on deep learning
  • In-situ wetting angle measuring device and wetting angle determining method based on deep learning
  • In-situ wetting angle measuring device and wetting angle determining method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present specification in combination with the drawings in the embodiments of the present specification. Obviously, the described embodiments are only some of the embodiments of the present specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of this specification.

[0024] In the description of the embodiments of this specification, unless otherwise specified and limited, the terms "installation", "connection" and "connection" should be interpreted in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediary; connected. ...

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 embodiment of the invention provides an in-situ wetting angle measuring device and a wetting angle determining method based on deep learning. The in-situ wetting angle measuring device comprises oil-water pretreatment equipment and wetting angle measuring equipment, and the method is a wetting angle determining method based on deep learning. Particles with different particle sizes and properties are placed in the oil-water pretreatment equipment to simulate various porous media and used for simulating full contact reaction of crude oil, water and rock, so that crude oil components are adsorbed on the surface of the rock and the mass transfer effect between oil and water is fully generated to obtain an oil reservoir in-situ fluid, and the actual condition of an oil reservoir is simulated to the maximum extent. The wetting angle measurement result is more accurate; furthermore, the convolutional deep learning network is introduced into wetting liquid form capture and simulation, high-precision recognition of the wetting angle is achieved, the problem that the calculation error of the wetting angle is large due to human factors and formula applicability differences is avoided, and the measurement precision of the wetting angle is improved.

Description

technical field [0001] The embodiment of this specification relates to the technical field of oilfield development, in particular to an in-situ wetting angle measuring device and a method for determining the wetting angle based on deep learning. Background technique [0002] Wetting angle (contact angle) is the most important physical quantity to describe the wettability of liquid-solid interface, and it plays a wide role in surface chemistry, chemical production, material preparation, petrochemical industry and environmental protection. Taking oilfield development and enhanced oil recovery research as an example, as the main evaluation index of interface wettability, it is directly related to oil recovery. With the continuous advancement of oil and gas field development, how to accurately and truly evaluate the wettability of rock surfaces in reservoirs has become the key for petroleum engineers to choose development plans and evaluate the pros and cons of many methods of e...

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): G01N13/02G06N3/04G06N3/08
CPCG01N13/02G06N3/08G01N2013/0208G06N3/045
Inventor 刘月田柴汝宽让滕达王靖茹
Owner CHINA UNIV OF PETROLEUM (BEIJING)
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com