Controls of low injectivity caused by interaction of reservoir and clogging processes in a sedimentary geothermal aquifer (Mezőberény, Hungary)
Bibliographic record
Abstract
Abstract Low injectivity is often experienced in geothermal doublets installed in sandstone reservoirs. This even led to a shutdown of the Mezőberény (Hungary) geothermal site. An on-site campaign was carried out in January 2021 to prepare a stimulation aiming to enhance the transmissivity of the sedimentary reservoir and the near-wellbore zone of this site. Previous studies have concluded that insufficient injectivity may be linked to a high skin effect in the near well-bore zone and pore clogging in combination with the low net sandstone content of the fluvio-deltaic reservoir. A chemical soft stimulation based on the injection of hydrochloric acid (HCl) was successfully used to unclog and recover the well injectivity. Despite such empirical evidence, the geochemical mechanisms leading to both, detrimental formation of clogging and the HCl-driven transmissivity restoration, have not yet been elucidated. This work presents the results of a novel analysis aiming at (a) predicting the dominant type of clogging forming in the near-well bore zone; (b) quantifying the drop in hydraulic conductivity as clogging occurs; and (c) supporting the optimization of the HCl dosage during the chemical soft stimulation. The study is supported by new experimental datasets never presented before from the Mezőberény site and a geochemical model set-up simulating the main mechanisms involved in the clogging and unclogging processes. It is concluded that the biofilm formation was the dominant, while the precipitation of calcite and amorphous ferrihydrite—later reduced to magnetite by microbes—was the secondary clogging mechanism: In the long-term (yearly scale) simulating the hydraulic conductivity showed a decline with forming scales; therefore, biofilm was presumably responsible for the experienced rapid (1 month) clogging. When modelling the chemical stimulation, the estimated amount of precipitated minerals was dissolved already with 2.5 mol of HCl per liter of water (~ 10 m/m%). Therefore, the 20 m/m% of HCl chosen during the field campaign might had a beneficial effect dissolving the potentially higher amount of scaling and/or the carbonate minerals of the matrix near the wellbore. Overall, it is concluded that the chemical and the microbial analyses together with the geochemical model were critical to tailor the remediation attempts and to propose further development or reconstruction of the surface system before going into operation to prevent recurrent impairments. Our findings highlight the importance of interactions of various clogging mechanisms with each other as well as with the reservoir processes and provide approaches to tackle the issue of injectivity drop by characterizing and quantifying their effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".