Flow Units: From Conventional to Tight-Gas to Shale-Gas to Tight-Oil to Shale-Oil Reservoirs
Bibliographic record
Abstract
Summary Core data from various North American basins with the support of limited amounts of data from other basins around the world have shown in the past that process speed or delivery speed (the ratio of permeability to porosity) provides a continuum between conventional, tight-, and shale-gas reservoirs (Aguilera 2010a). This work shows that the previous observation can be extended to tight-oil and shale-oil reservoirs. The link between the various hydrocarbon fluids is provided by the word “petroleum” in the “total petroleum system (TPS),” which encompasses liquid and gas hydrocarbons found in conventional, tight, and shale reservoirs. Results of the present study lead to distinctive flow units for each type of reservoir that can be linked empirically to gas and oil rates and, under favorable conditions, to production decline. To make the work tractable, the bulk of the data used in this paper has been extracted from published geologic and petroleum-engineering literature. The paper introduces an unrestricted/transient/interlinear transition flow period in a triple-porosity model for evaluating the rate performance of multistage-hydraulically-fractured (MSHF) tight-oil reservoirs. Under ideal conditions, this flow period is recognized by a straight line with a slope of –1.0 on log-log coordinates. However, the slope can change (e.g., to –0.75), depending on reservoir characteristics, as shown with production data from the Cardium and Shaunavon formations in Canada. This interlinear flow period has not been reported previously in the literature because the standard assumption for MSHF reservoirs has been that of a pseudosteady-state transition between the linear flow periods. It is concluded that there is a significant practical potential in the use of process speed as part of the flow-unit characterization of unconventional petroleum reservoirs. There is also potential for the evaluation of production-decline rates by the use of the triple-porosity model presented in this study.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".