Well Logging Reconstruction Based on Bidirectional GRU
Pages 525 - 528
Abstract
As a sign of porosity and permeability, spontaneous-potential (SP) well logging plays an important role in reservoir division and evaluation. Complete and effective SP logging is helpful to obtain high resolution sandstone-mudstone section and is a reliable basis for oil-gas reservoir interpretation. However, in the actual mining process, distortion or incomplete of SP logging commonly exists due to instrument damage, borehole collapse and other reasons. Moreover, SP re-logging is expensive and difficult to implement. In this paper, we develop an alternative SP logging reconstruction technique based on Bidirectional gated recurrent unit (BiGRU) neural network. By considering the relationship between current logging data and historical and future logging data, and the nonlinear mapping relationship between different logging curves, the intelligent and effective completion of missing logging is realized. Extensive experiments are carried out in the Jiyang Depression, Shengli Oilfield to verify the effectiveness of the proposed method. The results show that BiGRU has higher reconstruction accuracy than gated recurrent unit (GRU), support vector regression (SVR) and linear regression (LR), which provides a new development direction for logging reconstruction.
References
[1]
BATEMAN R M. Openhole log analysis and formation evaluation[M]. 2nd ed. Richardson, Texas: Society of Petroleum Engineers, 2012.
[2]
ASQUITH G B, KRYGOWSKI D, GIBSONCR. Basic well log analysis[M]. Tulsa: American Association of Petroleum Geologists, 2004.
[3]
ESKANDARI H, REZAEE M, MOHAMMADNIA M. Application of multiple regression and artificial neural network techniques to predict shear wave velocity from wireline log data for a carbonate reservoir South-West Iran[J]. CSEG Recorder, 2004, 29(7): 40-48.
[4]
ZERROUKI A A, AIFA T, BADDARI K. Prediction of natural fracture porosity from well log data by means of fuzzy ranking and an artificial neural network in Hassi Messaoud oil field, Algeria[J]. Journal of Petroleum Science and Engineering, 2014, 115: 78-89.
[5]
SINGH U K. Fuzzy inference system for identification of geological stratigraphy off Prydz Bay, East Antarctica[J]. Journal of Applied Geophysics, 2011, 75(4): 687-698.
[6]
WANG G, CARR T R, JU Y, Identifying organic-rich Marcellus Shale lithofacies by support vector machine classifier in the Appalachian basin[J]. Computers & Geosciences, 2014, 64: 52-60.
[7]
SILVA A A, NETO I A L, MISS GIA R M, Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information[J]. Journal of Applied Geophysics, 2015, 117: 118-125
[8]
Zerrouki A A, AFa T, Baddari K. Prediction of natural fracture porosity from well log data by means of fuzzy ranking and an artificial neuralnetwork in Hassi Messaoud oil field, Algeria[J]. Journal of Petroleum Science and Engineering, 2014,115:78-89.
[9]
Long W, Chai D, Aminzadeh F. Pseudo density log generation using artificial neural network[C]//SPE Western Regional Meeting. OnePetro, 2016.
[10]
SALEHI M M, RAHMATI M, KARIMNEZHAD M, Estimation of the non records logs from existing logs using artificial neural networks[J]. Egyptian Journal of Petroleum, 2016, 26(4): 957-968.
[11]
Zhang D, Yuntian C, Jin M. Synthetic well logs generation via Recurrent Neural Networks[J]. Petroleum Exploration and Development, 2018, 45(4): 629-639.
[12]
Zhang Dongxiao, Chen Yuntian, and Meng Jin. Synthetic well logs generation via Recurrent Neural Networks[J]. Petroleum Exploration & Development, 2018,45(4): 69-79.
[13]
Cho K, Van Merriënboer B, Gulcehre C, Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. arXiv preprint arXiv:1406.1078, 2014.
[14]
Jun W, Jun-xing C, Jia-chun Y. Log reconstruction based on gated recurrent unit recurrent neural network[C]//SEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, 5-7 November 2019. Society of Exploration Geophysicists, 2020: 91-94.
- Well Logging Reconstruction Based on Bidirectional GRU
Recommendations
Intelligent technology for well logging analysis
Intelligent information processing IIWell logging analysis plays an essential role in petroleum exploration and exploitation. It is used to identify the pay zones of gas or oil in the reservoir formations. This paper applies intelligent technology for well logging analysis, particular ...
Comments
Information & Contributors
Information
Published In

June 2022
905 pages
ISBN:9781450397179
DOI:10.1145/3548608
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 October 2022
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICCIR 2022
ICCIR 2022: 2022 2nd International Conference on Control and Intelligent Robot
June 24 - 26, 2022
Nanjing, China
Acceptance Rates
Overall Acceptance Rate 131 of 239 submissions, 55%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 38Total Downloads
- Downloads (Last 12 months)14
- Downloads (Last 6 weeks)4
Reflects downloads up to 25 Feb 2025
Other Metrics
Citations
Cited By
View allView Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format