Offshore Field Application of a Low Corrosive Fluid Designed for De-Scaling of Well with ESP Completion
Bibliographic record
Abstract
Abstract The applications of both inorganic and organic acids are to dissolve scales, remove filter cakes and increase the permeability of carbonate and sandstone formations, during processes like de-scaling, completion and stimulation. Often in sandstone mineral acid treatments have hidden costs from logistics, handling and failure to meet performance targets. The use of a novel low corrosive chelating agent, glutamic acid N, N-diacetic acid (GLDA), instead of hydrochloric acid, was evaluated to remove calcium carbonate and iron scale depositions found in Well A's downhole electrical submersible pump system and near wellbore area at elevated temperatures and pressures. Besides scaling issues, high H2S concentration, up to 300ppm, were also detected in this well. Extensive lab studies were conducted to determine the chemical's compatibility with the ESP components, dissolution effectiveness of the collected downhole samples and corrosion properties on carbon steel tubulars in the presence of hydrogen sulphide gas. The results proved that the chemical was able to dissolve more than 95% of calcite and iron scales without damaging the cables and elastomers of the ESP after 24 hrs soaking at reservoir temperature. The offshore treatment application did not require chemical specialist on-board or additional additives such as corrosion inhibitors, intensifiers, iron control agents, etc. Nor did it require a large footprint to place the chemical on the platform. The chemical was bullheaded easily through annulus and tubing without having to pull out the pump. The cost savings of this rigless intervention is estimated to be USD 2mil. The success of this treatment was realized by additional gain of 112 bopd and the H2S concentration was reduced to 10 ppm. The field application data, along with the lab data, confirms that the GLDA based solution is an effective de-scaling and stimulation fluid that is gentle to various types of well completions, safe to handle, and cost effective to use. This paper highlights the successful use of the GLDA in contributing to the much improved well productivity focusing on the lab studies, operational considerations and production performances of Well A.
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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".