Author
Listed:
- Haitong Yang
(School of Energy Resources, China University of Geosciences (Beijing), Beijing 100190, China
School of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China)
- Lei Wang
(Geology Research Institute, Greatwall Drilling Company, China National Petroleum Corporation (CNPC), Beijing 100101, China)
- Xiaolong Qiang
(The Second Gas Production Plant of PetroChina Changqing Oilfield Company, Yulin 719000, China)
- Zhengcheng Ren
(The Second Gas Production Plant of PetroChina Changqing Oilfield Company, Yulin 719000, China)
- Hongbo Wang
(The Second Gas Production Plant of PetroChina Changqing Oilfield Company, Yulin 719000, China)
- Yongbo Wang
(The Second Gas Production Plant of PetroChina Changqing Oilfield Company, Yulin 719000, China)
- Shuoliang Wang
(School of Energy Resources, China University of Geosciences (Beijing), Beijing 100190, China)
Abstract
Production profiling technology is an important method for monitoring the dynamics of oil and gas reservoirs which can effectively improve the efficiency of oil recovery. Production profiling is a technique in which a test instrument is lowered from the tubing to the bottom of the well to measure flow, temperature, pressure, and density in a multi-layer section of a producing well. Normal production profiling process needs to stop production, operate complex, consume long time and high cost. Furthermore, the profile cannot be continuously monitored for a long time. To address these limitations, this paper proposes a production profiling interpretation method based on reservoir primitive microbial DNA sequencing. The microbial stratigraphic baseline with high-resolution features is obtained by sampling and DNA sequencing of produced fluid and cuttings from different wells. Specifically, the random forest algorithm is preferred and improved by comparing the accuracy, precision, recall, F1-score, and running time of three clustering methods: Naïve-Bayes classifier, random forest classifier, and back-propagation classifier. Constructing PSO-random forest model is based on stratigraphic records and produced fluid bacteria features. The computational accuracy and efficiency of this method allows it to describe the production profile for each formation. Moreover, this test process does not need to stop production with simple operation and does not pollute the formation. Meanwhile, by sampling fluid production at different stages, it can achieve the purpose of long-term effective dynamic monitoring of the reservoir.
Suggested Citation
Haitong Yang & Lei Wang & Xiaolong Qiang & Zhengcheng Ren & Hongbo Wang & Yongbo Wang & Shuoliang Wang, 2022.
"Research on Production Profiling Interpretation Technology Based on Microbial DNA Sequencing Diagnostics of Unconventional Reservoirs,"
Energies, MDPI, vol. 16(1), pages 1-23, December.
Handle:
RePEc:gam:jeners:v:16:y:2022:i:1:p:358-:d:1018185
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:358-:d:1018185. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.