Predicting Sorption-Induced Strain and Permeability Increase With Depletion for Coalbed-Methane Reservoirs
Bibliographic record
Abstract
Summary It is well known that absolute permeability changes occur in coalbed methane (CBM) reservoirs during primary depletion or enhanced recovery/CO2 sequestration operations. Sorption-induced strain in CBM reservoirs, also known as matrix shrinkage or swelling, may dominate permeability changes at low pressures, as is the case for CBM wells undergoing primary depletion in the Fruitland coal fairway of the San Juan basin. Several analytical models have been developed to predict changes in coal permeability as a function of stress and sorption. Most models, however, utilize an empirical method for estimating sorption-induced strain. Recently, a theoretical model for sorption-induced strain was developed and applied to single-component adsorption/strain experimental data. The new model was developed from basic thermodynamic principles and is more predictive than the empirically based approaches. In this paper, the theoretical model is expanded to incorporate multicomponent adsorption models that are more rigorous, and sometimes more accurate, than the commonly applied extended Langmuir (EL) equation. This improves predictions of multicomponent gas sorption-induced strain, as demonstrated by comparison to experimental data. The new sorption-induced strain model is then used to calculate the sorptionstrain component of the popular Palmer and Mansoori (P&M) equation, which, in turn, can be used to model permeability changes during both primary (single- or multicomponent gas) and enhanced recovery operations. Finally, the coupled sorption-strain/permeability model, incorporated into an analytical simulator, is used to predict and match permeability growth in a producing CBM well in the Fruitland coal fairway, which has a binary (CH4 + CO2) sorbed/produced gas composition. Matches to field-derived permeability growth using the new model are accurate but nonunique because of the lack of available data, particularly rock mechanical properties. Given the availability of rock mechanics and adsorption isotherm data, the rigorous thermodynamic basis of the new model should allow for more accurate predictions of coalbed permeability changes, but further testing is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".