Air Injection and Waterflood Performance Comparison of Two Adjacent Units in the Buffalo Field
Bibliographic record
Abstract
Summary Buffalo field covers a large area on the southwestern flank of the Williston basin, in the northwest corner of South Dakota. In 1987, 8,000 acres of the field were divided into two units to initiate improved-oil-recovery (IOR) operations with two different methods: air injection and waterflooding. After collecting 19 years of production history, a technical and economic comparison has been made between the two projects to determine the relative success of both units. The technical performance was evaluated in terms of incremental oil recovery, ultimate recovery, and incremental recovery per volumes of fluid injected. Ultimate primary recovery was estimated using conventional decline-curve analysis on individual wells. Ultimate recovery was estimated by extrapolation of the current performance of the units, assuming the same actual development scheme and operating strategies. The economic comparison was performed in terms of net present value (NPV), incremental rate of return, and payout time. A sensitivity analysis on some of the key drivers of the project economics—specifically, oil price, operating cost, and capital investment—was also performed. Throughout the years, the west Buffalo Red River unit (WBRRU) under high-pressure air injection (HPAI) has technically outperformed its "twin," west Buffalo "B" Red River unit (WBBRRU), which is under waterflooding. Nevertheless, the waterflood project has shown greater economic benefit, which results primarily from the low oil prices (less than USD 20/bbl) experienced during most of their operating lives. This case study shows that for an air-injection project to be successful not only technically but also economically, a sufficiently high oil price (i.e., greater than USD 25/bbl) is needed, mainly because of the high operating costs and capital investment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".