An Experimental Study of Single Component Adsorption/Desorption Isotherms
Bibliographic record
Abstract
Abstract Within tight reservoirs, gas is stored both as free gas contained in the pores and adsorbed gas on the rock matrix. Adsorbed gas exhibits liquid-like densities resulting in significantly more gas being stored on the rock surface. By having accurate adsorption/desorption data of injected and reservoir gases, one can acquire a better understanding of the true original gas in place, as well as how to maximize production through optimal enhanced gas recovery (EGR) techniques. The aim of this research is to measure the adsorption/desorption isotherms of single-component gases on activated carbon in a series of pressure steps up to 1500 psi. The experiments are conducted at varying temperatures to establish a wide array of isotherms. Temperatures are maintained through the use of a water bath. The obtained isothermal pressure data is modeled using the Gibbs sorption isotherm and the Langmuir mathematical model, the most popular and simplistic approach. Furthermore, by plotting pressure divided by adsorption capacity as a function of pressure, Langmuir parameters are determined. From the experiments, isothermal pressure data was able to be modeled using the Gibbs sorption isotherm and the Langmuir isotherm and Langmuir parameters were determined and compared. It was observed that decreasing temperature and increasing hydrocarbon molecular weight were the main contributing factors to higher sorption capacities of the single component gases. It is important to quantify both adsorption and desorption processes because in EGR techniques such as cyclic solvent injection (CSI) injected gas is competitively adsorbing onto the rock, causing the adsorbed reservoir gas to be displaced, desorb, and subsequently be produced. Due to the aforementioned irreversibilities, by using adsorption metrics to quantify the amount of gas desorbed within the reservoir, gas production may be overestimated. To date, most adsorption/desorption experimental work has been conducted on methane, carbon dioxide, and nitrogen. This research aims to expand on previous literature by performing adsorption/desorption experiments on higher chain hydrocarbons, such as ethane and propane. By doing so, CSI EGR schemes can be more meticulously modeled as the inclusion of higher chain hydrocarbons allows for the model sorption inputs to be more representative of typical unconventional reservoir gas. This in turn will allow for more accurate production forecasting, helping minimize the financial risk of costly EGR projects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".