The Impact of Increase in Lateral Length on Production Performance of Horizontal Thermal Wells
Bibliographic record
Abstract
Abstract Drilling the horizontal wells was the beginning of unlocking the enormous potential of Western Canada oil-sands. However, several technical and operational challenges avoid the so-called extended reach Steam Assisted Gravity Drainage (SAGD) wells. The last few years drive to reduce Capital Expenditures (CAPEX) encouraged the development of key innovative tools to make long thermal wells a reality. SAGD pad development using maximum possible well-length has taken significant leaps in economical assessment of SAGD projects. The goal of this paper is investigating the impact of Flow Control Devices (FCDs) on the overall performance of the long wells in SAGD projects. In this paper, seven major thermal projects in Western Canada were investigated. Production history of all the wells is normalized by the variation of the geological condition, operational parameters, and well length. Following a convention in the industry, the wells with lateral length longer than 850 meters are labeled as "long" and those shorter than 850 meters are labeled as "short". Eventually, normalized oil production by long wells completed or retrofitted with FCDs is compared with those without FCDs to supply insight on the role of completion design on relative performance of drilling long wells. The comparison has been conducted with respect to different completion types such as Liner or Tubing Deployed FCDs (LDFCD or TDFCDs). On average, long wells produced 2% more normalized oil compared to short wells for all projects, while they produced on average 16% more normalized oil in the projects 1, 2, 6, and 7 in which long wells successfully drilled. The historical production performances show that FCDs are the key enablers and innovative strategy to drill longer wells. For successful long wells with FCDs, the normalized oil production is improved as high as 81%, and the improvement rate is 108% and 10% for LD and TD FCDs, respectively. Furthermore, the completion strategy of combining long wells with FCDs improved the normalized oil production about 33% compared to short wells. This study shows that switching from short wells to long wells in SAGD projects and completing them with FCDs is a synergic approach to increase the oil production (33%). The results of this paper confirmed that drilling long wells with FCDs is a win-win strategy resulted in more oil production compared to long/ short wells without FCDs; since CAPEX is reduced by long wells and oil production is increased by FCDs. The results also help completion and production engineers to get a better understanding of the contribution of FCDs in long lateral wells.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".