Production Fractionation and Efficiency Indicators from Phase Snapshots
Bibliographic record
Abstract
Petroleum produced from low permeability shales is different to the dispersed in-situ fluids from which it is derived. Whereas in-situ fluids consist of hydrocarbons, resins and asphaltenes in proportions governed by organic matter type, maturity and retention behaviour, the produced fluids are highly enriched in hydrocarbons and low polarity non-hydrocarbons, and show an enhanced GOR. Here, we study the effects of fractionation during production from Permian and Cretaceous shales using laboratory experiments, PVT-modeling and a regional PVT database. Our goal was to develop methodologies for predicting yields and compositions of produced fluids ahead of drilling. Target wells with known fluid properties were used for calibration. Shales from neighbouring wells of slightly lower maturity were mildly matured to that of the calibration well using MSSV pyrolysis, and a PhaseSnapShot of the resultant fluid made using PVTsim. The first example, from the late oil window Eagle Ford, demonstrates that both kerogen and bitumen are important petroleum precursors, and that in-situ compositions are largely determined by the most recently generated charge, rather than by cumulative addition during maturation. The PVT model, calibrated to the engineering report of the target well and its environs, reveals that a high proportion of the in-place C7+ fluids remain in the rock matrix relative to gas during production. The second example, taken from a gas and condensate fairway in the Permian Basin, shows that the predicted bulk composition of recently generated petroleum is facies dependent. PVT fluid calibrations have low Psat and low cricondentherms. These characteristics are reproduced by experiment, but only for those zones containing low contents of high molecular weight liquids. Any contributions to produced fluids from other zones is associated with massive retention of high molecular weight organics. The third example concerns volatile oil production from wells in the Permian Basin. The MSSV products generated by adjacent lower maturity shales exhibited phase envelopes with higher cricondentherms than that of the calibration, this being attributable to a molecular weight difference in heavy components. Adjusting the MW from 249 (measured) to 222 (produced oil PVT value) in the PVTsim model aligned the cricondentherms. This tuning step corresponds to the preferential retention of heavy polar compounds in the rock matrix during production. In a second step, 20% of the tuned MSSV-generated liquids are considered to be retained in the rock, thereby raising Psat. The result is an excellent match between the doubly tuned predicted phase envelope and that of the produced fluid. The preferential retention of polar compounds is also in line with this tuning step. In summary, fractionation is part and parcel of production from shales. Up to 80% liquids retention relative to gas has been demonstrated. Production efficiency assessments are readily inferred from these data. The extent to which fractionation occurs varies a lot, and has here been assessed by combining experimental rock geochemistry with PVT modeling (PhaseSnapShots), and using PVT reports on produced fluids for calibration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".