Polymer-Flooding-Pilot Learning Curve: Five-Plus Years' Experience To Reduce Cost per Incremental Barrel of Oil
Bibliographic record
Abstract
Summary This paper deals with the learning curve of a five-plus-year polymer-flooding pilot conducted in a mature waterflood that includes, for example, several works related to injector and producer wells and reservoir management. The scope of this paper is to describe the learning curve during the last 5 years rather than the reservoir response of the polymer-flooding technique; focus is on the aspects related to reduce cost per incremental barrel of oil for a possible extension to other waterflooded areas of the field. Diadema oil field is 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 (interwell spacing is 250 m on average). The company has developed waterflooding over more than 18 years (today, this technique represents 82% of oil production in the field) and produces approximately 1600 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 that is polymer-flooded is characterized by high permeability (average of 500 md), high heterogeneity (10 to 5,000 md), high porosity (30%), very stratified sandstone 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 five water injectors (it includes 13 injectors today) with an injected rate of 1000 m3/d (today, 2000 m3/d). Polymer solution is made with produced water (15,000 ppm brine) and 1,500 ppm of hydrolyzed polyacrylamide polymer reaching 15- to 20-cp fluid-injection viscosity. Oil-production rate 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 approximately 15%. The main aspects presented in this work are depth profile modification with crosslinked 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 progressing-cavity pumps to decrease shut-in time and number of pump failures. The plan for the future is to extend this project to other areas with the acquired knowledge and to improve different aspects, such as water quality and optimization of polymer plant operation. These improvements will allow the company to reduce operating costs per incremental barrel of oil.
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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 | 0.001 |
| 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".