Simulation of Liquid-Rich Shale Gas Reservoirs with Heavy Hydrocarbon Fraction Desorption
Bibliographic record
Abstract
Abstract Liquid-rich shale (LRS) gas reservoirs have gained increasing attention in recent years. The Eagleford Shale in the U.S. and the Duvernay Shale in Canada are examples of liquid-rich shale gas plays which are being exploited to produce more profitable liquid hydrocarbons with natural gas. These reservoirs may store liquid hydrocarbons in liquid or vapor state, as with gas condensate systems. Further, shale gas reservoirs may contain significant volumes of organic matter, which stores gas (and liquid hydrocarbons) in the adsorbed state. There have been several historical simulation studies investigating the impact of various reservoir and fluid properties on fluid production and recovery from LRS, however none have investigated the importance of desorption. Adsorption has previously been suggested to be an important storage mechanism in organic-rich shales, particularly for heavy hydrocarbon fractions. Matrix pore configuration and associated connectivity has also been previously demonstrated to be an important control on gas production from shale gas reservoirs, but the impact on condensate production has not been investigated for LRS. In this work, the impact of heavy hydrocarbon fraction desorption, pore configuration and connectivity, fluid composition and operating conditions (flowing bottomhole pressure) on LRS production is investigated using a commercial simulator. We use PVT data from previous studies to develop a compositional simulation model analog of an LRS reservoir. Hydrocarbon component adsorption amounts are modeled using the Langmuir isotherm. Different combinations of organic and inorganic matter and fracture porosity, and their connectivity, are also assigned in the simulation cases. Simulation sensitivities demonstrate that desorption can contribute significantly to condensate production, depending on fluid composition and pore connectivity. In cases where liquid-fraction adsorption contributes significantly to in-place volume, we recommend injection of light end gases to mitigate condensate blockage. This will be studied in detail in future work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".