A Novel Stimulation Approach for Scale Control in Marrat Carbonate Reservoir - Case Studies from Joint Operations, PZ Kuwait
Bibliographic record
Abstract
Abstract Carbonate scaling is one of the common problems that occur in wells producing high amount of water. The tendency of scaling escalates in mature fields. This problem becomes critical in sub-hydrostatic wells with Electrical Submersible Pumps (ESP). In such cases, the scale not only reduces the flow of fluids into the wellbore, but also causes frequent failures in downhole equipment, eventually stopping production leading to well workover. Frequent ESP failures can increase the operating costs to unacceptable levels which may eventually lead to field abandonment. Joint Operations (Chevron and KGOC) in Partitioned Zone (PZ) faced severe scaling problems in Humma field producing from Marrat Carbonate reservoir. A thick layer of calcium carbonate scale was observed in the completion string during the workover. As a result of this scale, ESP repair and replacement frequencies increased abnormally. Also, the ESP amperage charts showed erratic behavior due to solids interference inside the pump resulting in pump failures. A combined scale control and stimulation treatment was applied in three wells in Humma field in Joint Operations to slow down scaling tendency in the formation and tubular. These wells are producing up to 1523 BWPD averaging 28% water cut. The treatment provided effective placement of scale inhibitor in the formation while controlling any increase in water production because of stimulation. As a result, the workover frequency due to pump failures was reduced. Not only did the production improve, the amount of deferred oil was also significantly reduced resulting in direct oil gain and significant savings in operating costs. This paper describes the lab analyses, treatment design and execution procedure, adopted for the implementation of this technique as well as the recommendations and lessons learned from the field experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".