Screening Criteria for Infill Drillingin Water Flood Operations
Bibliographic record
Abstract
Abstract Based on experience of drilling 10,000 vertical and 1,000 horizontal infill wells in various waterflood operations in the Western Canadian Sedimentary Basin over the last 15 years, certain conditions favorable to economically successful infill wells were identified. These included:Thickness> 6 m, porosity> 10% and near well oil saturation> 50% (prevailing water-cut<75%)Transmissibility (kh/ μ o) of the reservoir> 0.1darcy.metre/mPa.sReserves life index of ongoing waterflood (remaining reserves/ current yearly production) of over 10 yearsSome heterogeneity to enable production of adequate incremental reserves to make drilling infill wells worthwhileAppropriate completion practices leading to low or negative skin factors in various injectors/ producers Furthermore, most successful in-filled waterflood operations benefited from:Ability to handle large volumes of high water-cut effluentAbility to selectively isolate intervals contributing large amounts of water/ gas.Proactive monitoring and surveillance and timely intervention. It was appreciated that due to resource variability in any given reservoir, performance of individual infill wells would also be highly variable, leading to some economically unsuccessful infill wells in every operation. Therefore, a prudent way to measure economic success is to compare overall weighted averages for the infill program, rather than results of individual wells, some of which will most likely be economically unsuccessful. Thus, infill drilling is recommended only in prospects that allow multi-well infill locations, enabling computation of statistically meaningful risk weighted averages. Introduction Infill wells are drilled in waterflood situations mainly to increase the net asset value via draining additional reserves and/or to accelerate oil drainage. In the Western Canadian Sedimentary Basin (WCSB), about 10 000 vertical and 1 000 horizontal infill/ step-out wells were drilled in various waterflood projects between 1986 and 2003. Our intent here is to explore key lessons from data available in the public domain on various infill wells in waterflood1,2. While reviewing infill drilling activity, we keep the following aspects in mind:During initiation of a waterflood, ‘down-spacing’ or fieldwide infill drilling is often consideredDuring the life of a waterflood, infill drilling is considered for accelerating oil production, and for sweeping incremental reservoir volume. An extreme example would be completely changing flooding pattern following performance review, resulting in infill injectors/ producersA portion of infill wells are service wells or replacement wells for the older wells that are being converted to service wells/ suspended/ abandoned. This paper is a retrospective of overall success/ failure of infill wells of recent years with a view to distill lessons learnt that might assist in screening similar future prospects for infill drilling. Considerartion in Infill Drilling in Waterfloods For infill drilling to be justified, anticipated revenues from incremental oil drained and rate of its drainage should be sufficient to offset costs on a risk-weighted basis. Net revenues, besides oil prices, are dependent upon productivity and incremental reserves drained. Incremental reserves, in turn, depend upon heterogeneities/ channeling, etc causing poor volumetric sweep3.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".