Chelating Agent for Uniform Filter Cake Removal in Horizontal and Multilateral Wells: Laboratory Analysis and Formation Damage Diagnosis
Bibliographic record
Abstract
Abstract An impermeable mud cake layer, created on the formation face while drilling may be favorable for drilling operations but detrimental to well productivity. In vertical high pressure wells the layer is cleaned out while flowing back the well at adequate pressure drawdown. On the other hand, low pressure differential at the sand face in horizontal wells makes well cleanup treatments a necessity. Not all filter cake components are acid soluble. Commonly used inorganic acids and oxidizers are very reactive and cause uneven filter cake removal, which can affect the well's performance. As a result, there was a need to evaluate slow-reacting chemicals that can produce delayed uniform filter cake removal in horizontal wells. These treatment chemicals vary from the nonreactive acid-free microemulsion fluid systems to the weak organic acids, acid precursors, enzymes and chelating agents. The objective of this paper is to evaluate two chelating agent based treatments, NTA and EDTA, as filter cake removal treatments for a sandstone reservoir utilizing oil based mud (OBM) drill-in fluid (DIF) in Saudi Arabia. Coreflooding experiments were run under reservoir conditions to evaluate fluid-rock interaction. In addition, fluid-fluid compatibility was conducted between chelating agent and drill-in fluids base brines and between the chelating agent and formation fluid using high temperature, high pressure (HTHP) see-through cell. Solubility and static fluid-loss tests were conducted to evaluate the filter cake removal efficiency. Experimental results indicated that the NTA-based treatment was effective in removing up to 91% of the filter cake uniformly after soaking treatment for 24 hours. Fluid-fluid compatibility tests showed that the NTA-based chelating agent, when mixed with reservoir fluids, was free from precipitation or emulsion. On the contrary, mixing the chelating agent with the DIF carrier brine resulted in severe precipitation of insoluble permanently damaging byproducts. The laboratory study also observed that pH has a direct proportional relationship with the amount of precipitation. EDTA-based treatment results showed a removal efficiency of 93% after soaking treatment for 90 hours. Fluid-fluid compatibility tests showed that the EDTA-based treatment, when mixed with OBM DIF was free from precipitation or emulsion. On the contrary, mixing the chelating agent with the reservoir formation water resulted in severe precipitation of insoluble permanently damaging byproducts.
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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 | 0.000 | 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) | 0.000 | 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".