Evaluation of Quintuple Porosity in Shale Petroleum Reservoirs
Bibliographic record
Abstract
Abstract The use of a quintuple porosity system for calculation of original petroleum in place (OPIP) in shales is important as neglecting some of the porosities can result in pessimistic values of OPIP and production rates. Based on the concept of Total Petroleum System (Magoon and Beaumont, 1999) the word ‘Petroleum’ includes (1) thermal and biological hydrocarbon gas, (2) condensates, (3) crude oils and (4) natural bitumen. In the case of natural gas, the gas is trapped and stored in shale in different ways: (1) gas adsorbed in the kerogen material, (2) free gas trapped in nonorganic inter-particle (matrix) porosity, (3) free gas trapped in microfracture and slot porosity, (4) free gas stored in hydraulic fractures created during the stimulation of the shale reservoir, and (5) free gas trapped in a pore network developed within the organic matter or kerogen. An additional storage element is provided by gas dissolved in kerogen. The governing equations that describe the gas mass balance in the quintuple porosity model are presented in detail. The series and parallel gas transport approaches discussed previously in the literature are shown to be special cases of the new general gas transport formulation developed in this study. The effects of permeability stress-dependency are taken into account. Real data from Devonian gas shales are used to illustrate the effect of free gas, adsorbed gas and dissolved gas in a material balance crossplot of P/Z vs. cumulative gas production. Historically, the large contribution of organic porosity, natural fractures and hydraulic fractures that can contribute a significant amount of free petroleum in place has not been taken into account. And many of the laboratory experiments used for determining data utilized in computations of OPIP and production rates have been carried out in crushed samples, which by their very nature do not generally preserve natural fractures, slots and all the porosity present in the organic matter. This leads to pessimistic values of OPIP and rates. This helps to explain the larger than anticipated rates and recoveries of natural gas from some of these formations, for example Devonian shales, which have been producing for several decades. Although the quintuple porosity characterization mentioned above indicates very heterogeneous systems, the production performance is less heterogeneous than that of carbonates, sandstones and naturally fractured tight reservoirs. This surprising result is demonstrated with the use of actual production data from various petroleum reservoirs around the world. The subject matter is significant because of the large volume of petroleum resources in shales throughout the world, which probably are underestimated because of not considering in a single model all types of porosity discussed 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".