Assurance Increased for Drill Cuttings Re-Injection in the Panuke Field Canada: Case Study of Improved Design
Bibliographic record
Abstract
Abstract Disposal of oil-contaminated cuttings has become increasingly important from both economic and environmental perspectives. Re-injection through hydraulic fracturing can provide a zero discharge solution and eliminate future cleanup liabilities. The development of best practices and practical solutions for predicting fracture growth during slurry injection has accelerated the economic disposal of oily cuttings from drilling operations. One such case is for Panuke wells, in Nova Scotia, Canada. For sections deeper than 1290 m MD, these wells are drilled with oil based mud. In the past, drilling waste was injected into an existing well, Well PI-1. A total cuttings slurry volume of 96,000 bbls had been injected through casing into well PI-1 before an additional well was planned in early 1999. Well PP3C was identified as a candidate injection well through the 11¾" × 9 " casing annulus. In order to assure the containment of fractures arising from injection, investigations were conducted on the design of the injection process using a fully three-dimensional hydraulic fracturing simulator. This paper assesses the affects of formation layering, varying permeability and elastic modulus, injection rate, and other operational procedures on the injection fracture geometry and containment. Significant to the injection recommendations were lessons learned from a recent joint industry project on ‘drilling waste disposal’ (DEA #81). The ability to verify multiple fractures in DEA #81 from post laboratory test examination suggests that the analyses and design are adequate to assure safe containment of injected drill cuttings. The case study showed that interaction among the various factors can result in very complex fracture geometry and a true three-dimensional fracturing simulator must be used to assess fracture containment. The results provide insight into best practices for the containment of fractures, designs for successful re-injection, selection of candidate injection zones, and quality assurance.
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 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".