Chemical Sand Consolidation - Developing a Strategic Capability Across a Wide Portfolio
Bibliographic record
Abstract
Abstract Mechanical sand control completions are often used to optimise hydrocarbon production from weak formations. In a few wells this primary sand control method has sub-optimal performance. There is substantial value in remediating some of these wells, but developing this capability is not a quick and simple task. Recompleting a well could be feasible, but is often cost prohibitive. Mechanical solutions could be applied to stem sand production if the sand producing interval is known; however, in some cases chemical sand consolidation is the most effective approach. Chemical sand consolidation works by pumping chemicals downhole to strengthen the formation and stop sanding. In most cases reported in the industry, chemical consolidation has been used in short production intervals (<100m). Our approach was to develop a laboratory programme to test various industry chemicals and to achieve a good understanding of how these can be applied. Candidate wells were matched to chemicals to identify which systems (may be more than one) would be the best fit. The strategy was to initially trial the technology in low-rate onshore wells (typically <1 mmscf/d), before moving progressively to more challenging wells (up to 2,500 bopd). The wells were split into 3 groups based on their complexity: (1) proppant flow back remediation, (2) <100m producing interval and (3) >100m intervals. Each has unique challenges, but this approach facilitated a progressive learning curve. In proppant flow back remediation 7 field applications were conducted with 100% success rate. Longer intervals were successfully treated over time. Matrix consolidation presented a bigger challenge - 10 field trials have been carried out with a mix of successes and failures. A key learning is that adequate placement of the chemicals is critical. Chemical sand consolidation would fulfil its potential only when the chemicals can be reliably delivered to the target sand-producing zone – (often unknown), and remain static to allow sufficient curing time. Some treatments have been bullheaded (i.e pumped down the production tubing); others have been placed with coiled tubing. Operator has successfully developed an organisational capability whereby this technology can be part of the toolkit and - where appropriate - can be applied with a reasonably high chance of success to add value. Wells have been treated in the Lower 48 states in the USA, Canada, Alaska, Azerbaijan and Egypt and other wells are constantly being evaluated. Well types include oil and gas producers, onshore and offshore, with reservoir temperatures from 29 °C to 135°C. This technology is therefore being used across a wide well portfolio.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".