Increasing Lazy Gas Well Production: A Field Wide Case History in Northern Alberta
Bibliographic record
Abstract
Abstract This paper summarizes methods, chemistries, and application results for a range of liquid foaming agents on a collection of wells applied at a single property in Northern Alberta. The paper goes on to summarize the philosophy, design criteria for selection of remediation chemistries, and methodologies used to counter the production decline occurring as a result of water production. Gas flow rates from these low pressure gas wells were being dramatically affected by production of free or condensed water. If allowed to collect to sufficient levels, the produced fluid killed production altogether. The shallow (500 to 750 meters) wells on this lease are particularly sensitive to fluid production as reservoir pressure was very low, approximately 700 kPa (102 psi), and production of only 0.5 m3 (3.15 bbl) of water per day could kill a well. Additional challenges were caused by high solids production and scale deposition. Additionally, further loading up the wells and lowering production as well as asphaltene precipitation, formed a very adherent and dense solid precipitate with sand produced by the well. Initially wells were cleaned with a combination batch asphaltene solvent. Subsequently, a very pervasive foam forming chemical was used to unload solids. Continuous injection of 2 liters per day of a liquid based foaming chemical into one particular well increased gas production by 3 e3m3 (0.1 mmcf) per day (a 50% increase). Other wells were successfully treated to remove water and solids that were blocking the perforated interval. Significant value was added by increasing production field wide by 33% with an insignificant associated chemical cost. The results and conclusions of this work are applicable to the shale gas and CBM fields being produced throughout North America and enable best practice chemical well enhancement to be applied.
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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) | 0.002 | 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 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".