Mangala Polymer Flood Performance: Connecting the Dots Through In-Situ Polymer Sampling
Bibliographic record
Abstract
Summary The Mangala field contains medium-gravity viscous crude oil. Notably, it is the largest polymer flood in India and 34% of the stock tank oil initially in place (STOIIP) has been produced in 11 years. Mangala was put on full field polymer flood in 2015, 6 years after the start of field production on waterflood in 2009. Polymer flood added nearly 93 million barrels above the anticipated waterflood recovery in 6 years. Reservoir simulation models could replicate the initial Mangala polymer flood performance. However, the performance of the lower layers of Mangala (FM-3 and FM-4) continued to progressively deviate from modeling estimates. Importantly, the observed polymer breakthrough deviated significantly from predictions. As the polymer flood matured, the trend of field water cut with time indicated that in-situ polymer viscosity was equivalent to only 50 to 60% of the surface polymer viscosity. For better predictions and corrective actions, it was necessary to understand the nature of degradation, the progressively deteriorating field performance, especially of the lower layers, and the deviation of polymer breakthrough trends from predictions. Carefully designed in-situ polymer sampling, laboratory studies, and reservoir modeling studies helped connect the dots to understand the field performance. There are several excellent publications on accelerated aging studies and some on polymer sampling. This paper offers an opportunity to directly compare experimental results with field data. The procedures used and lessons learned during field sampling can be useful for other operators for management of polymer floods.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".