Investigation on the Impact of Voidage Replacement Ratio and Other Parameters on the Performances of Polymer Flood in Heavy Oil Based on Field Data
Bibliographic record
Abstract
Abstract Polymer flooding has now become a relatively well accepted method to increase production and recovery in heavy oil fields. Numerous pilots have taken place these last few years and field expansions are currently ongoing in several fields such as Pelican Lake (Canada), Marmul (Oman), Bohai Bay (China), Diadema (Argentina) and Patos Marinza (Albania). As a result of these recent developments, field data has now become available in large quantity and can be used to provide guidance on the impact of various parameters on expected flood performances. For instance, a comparison of primary, secondary and tertiary polymer flood performances based on the analysis of several polymer flood patterns in Pelican Lake was presented in 2016 (Delamaide, 2016). The present paper proposes to go further and to investigate the impact of parameters such as pore volume injected, well length, well spacing or Voidage Replacement Ratio (VRR) on polymer flood performances, based on data from fields in Canada and other parts of the world. The performances of over 70 patterns belonging to several heavy oil polymer floods were analyzed and the impact of VRR, well spacing, well length and other parameters on recovery was evaluated. The calculations were performed using actual reservoir and production data whenever possible and published data in other cases. Despite a large scatter in the data due to the wide range of reservoir conditions investigated, it is possible to distinguish interesting trends. For instance, higher VRR corresponds to lower recovery and recovery is fairly well correlated to injected pore volumes. This paper will provide guidance to engineers designing polymer floods in heavy oil fields, allowing to adjust some of the design parameters to improve field response. In addition, the results can also be used to benchmark reservoir simulation results which can often be too optimistic or to compare performances of pilot projects in other fields.
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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) | 0.000 | 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".