Organic Weighting Hydraulic Fracturing Fluid: Complex Interactions between Formate Salts, Hydroxy Carboxylate Acid, and Guar
Bibliographic record
Abstract
Summary Hydraulic fracturing has extended to both deep-terrestrial and deep-sea reservoirs because hydrocarbons in shallow subsurface are depleting. However, the density of common inorganic weighting agents may not give sufficient column pressure, which may compromise the efficiency of hydraulic fracturing fluids (HFF) and present potential risks to facilities and the environment. Here, we investigated hydroxypropyl guar (HPG)-based HFF (HPG-HFF) using potassium formate (PF) as a weighting agent with and without a hydroxy carboxylate acid (citric acid, abbreviation FW was used througout this study) as an additional dispersion stabilizer. Analyses included stability investigations, macro- and microrheology assessments, Fourier transform infraredspectroscopy (FTIR) analyses, molecular dynamic simulations, and screening of crosslinking points. Our results showed that increased concentrations of PF substantially reduced the stability and viscosity of HPG solutions, but adding citric acid mitigated these drawbacks. Molecular dynamic modeling suggested that formate acid ions formed hydrogen bonds with HPG and water, resulting in reduced hydrophilicity and coiling of the HPG molecular chain. When citric acid was added, less formate ions surrounded the HPG molecule, and the forming FW ions primarily interacted with the HPG molecule through hydrogen bonding. Besides, the hydroxyl group of the citric acid may improve the hydrophilicity of the whole complex. Thus, the original nature of the HPG molecular chain could be compensated. Atomic force screening showed more crosslinking points with stronger intensity and an even distribution in the HPG-PF-citric acid gel system, compared to that in the HPG-PF gel system (without citric acid). Furthermore, thermal stability tests showed that the proposed PF-citric acid-HPG-HFF system could resist temperatures up to 120°C. Our study demonstrates the potential application of formate-based weighting agents, highlighting the effects of hydrogen bonding in complex HFF. This benchtop study provides a foundation for future research to understand the application of formate-FW-based weighting HPG-HFF in downhole high temperature conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".