Flow Units: From Conventional to Tight Gas to Shale Gas to Tight Oil to Shale Oil Reservoirs
Bibliographic record
Abstract
Abstract 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 (or delivery) speed provides a continuum between conventional, tight and shale gas reservoirs (Aguilera, 2010). This work extends the previous observation to tight oil and shale oil reservoirs. The link between the various fluids is provided by the word ‘petroleum’ in ‘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 have been extracted from published geologic and petroleum engineering literature. The paper introduces a new unrestricted transition flow period in tight reservoirs that is recognized by a straight line with a slope of -0.75 on log-log coordinates. This straight line occurs as a transition between 2 linear flow periods. Process speed is the ratio of permeability and porosity. The approximate boundary between viscous and diffusion dominated flow in gas reservoirs is estimated with Knudsen number which can be calculated from pore throat radius (a function of process speed). Viscous flow is present, for example, when the architecture of the rock is dominated by megaports, macroports, mesoports and sometimes microports (port = pore throat). Diffusion flow on the other hand is observed at the nanoport scale, which can occur in both tight and shale reservoirs. The process speed concept has been used successfully in conventional petroleum reservoirs for several decades and in tight and shale gas reservoirs during the past 3–4 years. The concept is extended in this paper to tight oil and shale oil reservoirs, and hence to the complete petroleum system, with the support of core and drill-cuttings data. The approach permits estimating volumes of petroleum-in-place, differentiating between viscous and diffusion dominated flow in gas reservoirs and the contribution of each flow mechanism with the use of a unified diffusion-viscous flow model. This is valuable, for example, in those cases where the formation to be developed is composed of alternating stacked layers of tight and shale reservoirs, or where there are lateral variations due to facies changes. It is concluded that there is significant practical potential in the use of process speed as part of the flow unit characterization and production performance prediction in unconventional petroleum reservoirs.
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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 | 0.002 | 0.002 |
| 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".