Steam Distillation Studies for the Kern River Field
Bibliographic record
Abstract
Summary The interactions of heavy oil and injected steam in the mature steamflood at the Kern River Field have been extensively studied to gain insight into the effect of steam on compositional changes of oil during the recovery process and to provide input for compositional thermal simulation. Steam distillation behavior of this 13°API California oil between 300 and 467°F under a variety of process conditions, along with extensive analysis of distilled hydrocarbons were incorporated to give a more in-depth description of what is happening to the oil and what changes are occurring in the distillates or produced oil. This information was further integrated with analysis of the field distillate, "casing blow," to infer what is happening in the field. The results show that steam distillation is temperature dependent and more important than originally thought. The data developed in this study are a basis for improvement of numerical thermal models with potential for better designed steamfloods and reservoir management. The results may also impact certain logging techniques used in steamfloods and possible heavy oil upgrading techniques. Kern River oil is more than 10% distillable at 300°F and 15% distillable at 400°F in dynamic laboratory steam distillation tests at steam throughputs of four times the initial volume of oil. Distillate physical properties of density, viscosity, molecular weight, and hydrocarbon composition of the distillates changed significantly. Distillate properties increased in value with increasing steam throughput, and at higher temperatures. This information is important in the tuning of equations of state, including hydrocarbon-water interaction parameters for compositional thermal simulation. Analysis of the field distillate, "casing blow," showed properties similar to laboratory distillates at low steam throughputs. The observation of a light field distillate production in a mature steamflood compared to laboratory measurements implies that the casing system temperature is a major controlling factor in "casing blow" composition and quantity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 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".