Dr. Xiangdong Peng Profile

Dr. Xiangdong Peng

at Northeast Petroleum Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 15 July 2022 Paper

Porosity prediction of carbonate reservoir based on neural network

Liyan Yang, Xiangdong Peng

Proceedings Volume 12258, 1225803 (2022) https://doi.org/10.1117/12.2640460

KEYWORDS: Neural networks, Statistical modeling, Error analysis, Data modeling, Carbonates, Statistical analysis, Superposition, Reliability, Neurons, Network security

Read Abstract +

Porosity, as one of the important reservoir physical parameters, plays an important role in reservoir evaluation. Considering the actual work needs, finding a low-cost and efficient method to obtain high-precision porosity has become an important topic of reservoir evaluation. Due to the complex nonlinear mapping relationship and timing characteristics between logging parameters and porosity, a model of deep learning is proposed to predict the porosity of carbonate reservoir according to the existing logging data. Firstly, on the basis of core analysis and geological and logging data, data preprocessing is carried out for carbonate reservoir logging data, including core depth homing, logging data standardization and normalization. The second step is to establish the prediction model of reservoir parameters by using proper learning samples. The third step is to evaluate the predicted effect of porosity model and modify the model by using superposition diagram method and error statistics method. The calculation demerit of the final model is compared with the traditional results.The comparison results in the last step show that the prediction results of reservoir parameters by neural network are more accurate than those by traditional methods.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years