MétaCan
Menu
Back to cohort
Record W2041378062 · doi:10.2118/168968-ms

Simulation of Liquid-Rich Shale Gas Reservoirs with Heavy Hydrocarbon Fraction Desorption

2014· article· en· W2041378062 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSPE Unconventional Resources Conference · 2014
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersAlberta Innovates - Technology FuturesShell Canada
KeywordsOil shaleHydrocarbonPetroleum engineeringDesorptionOil shale gasAdsorptionNatural gasChemistryPorosityVolume (thermodynamics)Organic matterFossil fuelFraction (chemistry)Unconventional oilGeologyChromatographyOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.235
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it