Polymer Flooding Pilot Learning Curve: 5+ Years Experience to Reduce Cost per Incremental Oil Barrel
Bibliographic record
Abstract
Abstract This paper deals with the learning curve of a 5+ years polymer flooding pilot conducted in a mature water-flood that includes several works related to injector and producer wells, reservoir management, etc. The scope of this paper is to describe the learning curve during the last five years rather than the reservoir response of the polymer flooding technique; focus is on the aspects related to reduce cost per incremental oil barrel for a possible extension to other waterflooded areas of the field. Diadema Oil Field is located in the San Jorge Gulf Basin in the Southern portion of Argentina. The field is operated by CAPSA, an Argentinean oil producer company; it has 480 producer and 270 injector wells. The company has been developing waterflooding during more than 18 years (today this technique represents 82% of oil production in the field) and produces about 1,600 m3/d of oil and 40,000 m3/d of gross production (96% water cut) with 38,400 m3/d of water injection. The reservoir being polymer-flooded is characterized by high permeability (500 md average), high heterogeneity (10 to 5000 md), high porosity (30%), very stratified sand-stone layers (4 to 12 m of net thickness) with poor lateral continuity (fluvial origin) and 20 °API oil (100 cp at reservoir conditions). Diadema's Polymer Flooding Pilot started in October 2007 on 5 water injectors (it includes 13 injectors today) with an injected rate of 1000 m3/d (today, 2000 m3/d). Polymer solution is made using produced water (15000 ppm brine) and 1500 ppm of HPAM polymer reaching 15/20 cp of fluid injection viscosity. Oil rate production from the original "central" producers (wells that are aided with 100% of polymer injection) has increased 100% at the same time as average reduction in water cut is about 15%. The main aspects presented in this work are: depth profile modification with cross-linked gel injected along with polymer, use of "curlers" to regulate injection in multiple wells with one injection pump without shearing the polymer and an improved technology on producer wells with PCP to decrease shutting time and number of pulling interventions. The plan for the next years is to extend this project to other areas by using the acquired knowledge and to improve different aspects, such as water quality and optimization of polymer plant operation, among them. These improvements will allow the company to reduce operative cost per incremental oil barrel.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".