Research on Production Profiling Interpretation Technology Based on Microbial DNA Sequencing Diagnostics of Unconventional Reservoirs

Haitong Yang, Lei Wang, Xiaolong Qiang, Zhengcheng Ren, Hongbo Wang, Yongbo Wang and Shuoliang Wang ()
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Haitong Yang: School of Energy Resources, China University of Geosciences (Beijing), Beijing 100190, 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

Energies, 2022, vol. 16, issue 1, 1-23

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.

Keywords: DNA extraction; DNA sequencing; production profiling; dynamic monitoring; clustering algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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