Heavy Oil Production Enhancement by Viscosity Reduction
Bibliographic record
Abstract
Abstract This paper presents an evaluation of different chemical agents that can reduce dramatically the apparent viscosity of a heavy crude oil or a thick emulsion. The focus of this study is on methods to improve the production of heavy oils and very viscous emulsions such as are found in California, Canada, and Venezuela. This study identified several surfactant-demulsifier formulations that can reduce the viscosity of such heavy fluids by as much as 3 orders of magnitude. If efficient chemical solutions are applied downhole to reduce produced fluids viscosity this offers an economical means to reduce the energy required to move the oil between the well to the surface facilities, thereby improving well productivity and reducing lifting costs. It is especially suited for wells that are producing fluids at colder temperatures (less than 150 °F) that have extreme fluid viscosities (from 10,000 to 100,000 cp); these may be reduced to 100 – 500 cp by gentle mixing with aqueous-based chemical treatment solutions. Wells with high hydraulic pressures, poor pump efficiencies, or excessive pressure losses in the facility gathering systems are good candidates for these treatments. Reducing these extreme viscosities will have benefits such as lowering the power consumption to lift the produced fluids and reduce system pressures. Chemical costs for such chemical treatments are less than a dollar a barrel of oil, and can be even less than $0.50 per barrel of heavy oil. These same or similar chemical systems also may be beneficial for longer distance transportation of heavy oils, as pretreatments for cyclic steam treatments, or as additives in the stimulation fluids applied in heavy oil wells. This laboratory investigation employed a unique novel viscometer that will measure accurately the effective dynamic viscosity of multi-phase liquids (emulsions) from several centiposes to thousands of centipoise. This instrument was developed to overcome the limitations of conventional laboratory viscometers to measure unstable emulsions that may separate during the measurement process.
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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".