Shale Gas-in-Place Calculations Part II — Multi-component Gas Adsorption Effects
Bibliographic record
Abstract
Abstract Recent studies have shown that shale gas industry is incorrectly determining gas-in-place volumes in reservoirs with a large sorption capacity by not properly accounting for the volume occupied by the adsorbed phase. Scanning electron microscopy has discovered nanopores in organic-rich shale with sizes typically in 3–100 nm range; adsorption data show presence of smaller pores and micropores (< 2 nm) as part of the predicted pore size distributions. At pore diameters of this scale the adsorption potential is high and thus the fractional pore volume occupied by adsorbed gas is often substantial. Hence a portion of the total pore volume would be occupied by the adsorbed gas and not available for the free gas molecules. In SPE 131772 a volumetric method, which accounts for the pore volumes occupied by the adsorbed and free gases, has been proposed based on single-component Langmuir adsorption model. In SPE 141416 we recognized the importance and impact of multi-component gas adsorption potential and adsorbed gas density when calculating gas-in-place estimates. We combined the widely used yet thermodynamically inconsistent Extended Langmuir model with volumetrics and free gas composition to formulate a new gas-in-place equation that accounts for the pore space taken up by a multi-component adsorbed gas phase. This paper extends the discussions on the adsorption layer effect of multi-component natural gases. The approach is based on thermodynamically-consistent ideal adsorbed solution (IAS) model to accurately predict adsorbed gas storage capacity for gas mixtures. We expanded on our previous work, where we calculated single-component adsorbed-phase density using molecular modeling and Monte Carlo simulation methods, and propose a new equation-of-state-based analytical approach to predict the adsorbed-phase density of a mixture. In concert, the model improves accuracy of the gas-in-place equations needed to account for the pore space taken up by a multi-component adsorbed phase. The new method yields total gas-in-place predictions, which suggest that an adjustment is necessary in volume calculations, especially for gas shales with high C2+ composition and high in total organic content. The new method is therefore recommended for shale gas-in-place calculations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".