DNA-based tracers for fractured reservoir characterization
Abstract/Contents
- Abstract
- A thorough understanding of the subsurface fracture network is crucial for the effective exploitation and management of geothermal energy, unconventional hydrocarbon resources, groundwater reservoirs, etc. While conventional tracer technology is a useful tool to characterize the complex network of flowpaths in geologic reservoirs, tracers are limited in unique variations and hence insufficient for characterizing reservoirs with a large number of wells. In addition, conventional tracer testing only provides a "snapshot" of the flowpath properties which may be inadequate for reservoirs that are subjected to changes. This research sought to resolve the limitations of conventional tracer testing by exploring novel, DNA-based tracer candidates. DNA's infinite number of unique sequences and hence great degree of specificity makes it a promising tracer candidate for improved subsurface characterization. We first investigated the use of uniquely designed, synthetic DNA fragments as injected tracers. The method to measure target-specific DNA tracer concentration is described. The effect of DNA sequence, fragment length and porous medium on DNA transport was studied to provide guidance to potential field applications and data interpretation. It was found that DNA transport was not affected by DNA sequence (i.e. the arrangement of nucleotides). The length of DNA fragments does not affect the shape of the tracer return curve, but does affect tracer mass recovery. Shorter DNA appeared to be more prone to adsorption, while longer DNA appeared to be more prone to size exclusion effect. We then extended the concept of DNA-based tracers towards the genomic DNA of fluid-associated microorganisms that naturally colonize a geologic reservoir. Instead of targeting just a few microbes, we proposed taking advantage of the entire microbial community population in a reservoir fluid sample as unique signatures pinpointing the origins of fluids. We tested this method at a mesoscale enhanced geothermal system (EGS) testbed at Sanford Underground Research Facility (SURF) by sampling indigenous fluids produced from separate fractures and analyzing their microbial community structure via high-throughput 16S rRNA gene amplicon sequencing. We found that hydraulically isolated fractures at our field site hosted distinct microbial community populations, demonstrating substantial microbial heterogeneity across fractures. However, locally within a fracture, the microbial community were relatively homogenized, serving as a unique natural tracer or "fingerprint" of the fracture. We demonstrated at our field site that sampling indigenous fluids from an undisturbed, newly developed reservoir could help us identify natural interwell connectivity when more than one well were drilled into the same natural fracture. Finally, building upon the idea of reservoir indigenous microbial populations as natural tracers, we investigated the potential of this novel data source in an actively circulating, dynamic reservoir. Again using the EGS testbed at SURF, we sampled the produced fluids from the reservoir that underwent long-term flow circulation. Sampling was conducted regularly in a 5-month time series and the microbial populations in the fluids were sequenced. We found that although the whole circulating reservoir were connected hydraulically, the difference in relative connectivity among fractures still allowed different flowing fractures to have different microbial community signatures. The long-term microbial monitoring at our site identified the switch of production zone of a borehole likely due to major changes in the fracture network. Changes in fracture network were also observed from microbial time-series data after a week-long injection halt, likely due to the reopened hydraulic fracture not restoring to its initial state. We thereby demonstrated that long-term microbial community monitoring in an active reservoir may effectively enable the direct observation of fracture network evolution. Such information is difficult to achieve via other reservoir diagnostic methods
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Zhang, Yuran |
|---|---|
| Degree supervisor | Dekas, Anne |
| Degree supervisor | Horne, Roland N |
| Thesis advisor | Dekas, Anne |
| Thesis advisor | Horne, Roland N |
| Thesis advisor | Kovscek, Anthony R. (Anthony Robert) |
| Degree committee member | Kovscek, Anthony R. (Anthony Robert) |
| Associated with | Stanford University, Department of Energy Resources Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Yuran Zhang |
|---|---|
| Note | Submitted to the Department of Energy Resources Engineering |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Yuran Zhang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...