Application of Conventional Forecasting Methods to Waterfloods with Horizontal Wells in Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract Using horizontal wells for primary production of heavy oil reservoirs is common in Canada but it is less frequent to employ them for waterflood. As a result, very few papers have been published on this topic. Similarly, numerous publications are available on the use of conventional forecasting methods to evaluate waterflood performances, but very few if any have focused on waterfloods with horizontal wells in heavy oil reservoirs. This is what this paper proposes to do. The production performances of over twenty horizontal wells from five Canadian heavy oil pools where waterflood has been implemented using horizontal wells have been studied. The pools are thin and bottom water is present in some of them; oil viscosity ranges from a few hundred to a few thousand centipoises. Conventional waterflood forecasting methods such as Arps, Yang and logarithm of Water-Oil Ratio (WOR) vs. Cumulative oil production were used and compared. However, the focus of the paper is not only the comparison of the various forecasting methods but also the evaluation of the performances of horizontal well waterfloods in these high oil viscosities. The Arps method appears difficult to use, especially when there are strong variations in injection rates. By comparison, the Yang and the WOR vs. Cumulative production methods appear more stable. The forecast in cumulative production can vary widely between these methods. Ultimate recovery is expected to vary from a few percent OOIP to over 20%OOIP. This paper will present the performances of several horizontal waterfloods in heavy oil reservoirs in Canada and compare several waterflood analysis methods. Very few if any paper has been published on this topic thus the information provided will be of interest to engineers who are considering using horizontal wells for waterflood as a follow-up to primary production in heavy oil reservoirs.
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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 | 0.001 | 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".