“Squimulation”—Simultaneous Well-Stimulation and Scale-Squeeze Treatments in Deepwater West Africa
Bibliographic record
Abstract
Summary In a deepwater west African field, the relatively small number of high-cost, highly productive wells, coupled with a high barium sulfate scaling tendency (upon waterflood breakthrough of injected seawater) requires effective scale management along with removal of near-wellbore damage in order to achieve high hydrocarbon recovery. The nature of the well-completion strategy in the field (frac packs for sand control) had resulted in some wells with higher than expected skin values owing to drilling-fluid losses, residual fracture gel, fluid loss agents, and fines mobilization within the frac packs. The paper will present how the challenges of managing impaired completions and inorganic scale forced innovation in terms of when to apply both stimulation and scale-inhibitor packages to deepwater wells. This paper will outline a novel process for non-conventional batch chemical applications where bullhead stimulation treatments have been displaced deep into the formation (>20 ft) using a scale inhibitor overflush. Not only does this benefit the stimulation by displacing the spent acid and reagents away from the immediate wellbore area, but the combined treatment provides a cost savings with a single mobilization for the combined treatment. The paper will describe the laboratory testing that was performed to qualify the treatments. The four field treatments that were performed demonstrate how these coupled applications have proven very effective at both well stimulation/skin reduction and scale-inhibitor placement before and after seawater breakthrough. The term “squimulation” is used by the local operations team to describe this simultaneous squeeze-and-stimulation process. Many similar fields are currently being developed in the Campos basin (Gulf of Mexico) and west Africa, and this paper presents a good example of best-practice sharing from another oil basin.
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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) | 0.002 | 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".