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Record W2899945766 · doi:10.2118/192967-ms

Impact of Total Organic Carbon on Adsorption Capacity, In-Place Hydrocarbons, and Ultimate Recovery: A Case Study of the Duvernay Formation in Alberta, Canada

2018· article· en· W2899945766 on OpenAlexafffundabout
Gaurav Sharma, Henry Galvis-Portilla

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsAdsorptionTotal organic carbonPetrophysicsKerogenOil shaleCarbonateChemistryOrganic matterPorosityMethaneMaturity (psychological)Volume (thermodynamics)Carbon fibersEnvironmental chemistryHydrocarbonMineralogyQuartzLangmuirGeologyChemical engineeringSource rockOrganic chemistryMaterials scienceThermodynamics

Abstract

fetched live from OpenAlex

Abstract Unconventional reservoirs, especially shale gas reservoirs, exhibit dual porosity (free fluid porosity and adsorbed fluid porosity). The adsorbed volume is a function of total organic carbon (TOC) and thus, higher organic contents are assumed to be directly related to higher hydrocarbons in place. However, this case study tried to evaluate this concept and found that with higher TOC, though gas in place increases the recoverable hydrocarbons reduces due to the low contribution from adsorbed heavier components. We thoroughly evaluate the impact of organic contents on adsorbed hydrocarbons and further compare with the petrophysical properties and production behaviors; herein using information from the Devonian aged Duvernay Formation in Western Canada. First, multi-well analysis of core and log-derived TOC revealed that variations in organic contents are a function of the stratigraphy and thermal maturity, particularly increases in carbonate contents seems to correlate with lower organic contents, whereas increases in quartz and clays correlate with higher organic contents. Then, adsorption capacities were analyzed as a function of variations in the TOC. Finally, comparisons of hydrocarbons in-place and production contribution of the adsorbed volume is analyzed for different average TOC wells. It is observed that TOC impacts relative adsorption of methane which further impacts the fluid characteristics (gas wells have higher average TOC as compared to the oil wells). This observation becomes relevant as we could partially understand well performance from fundamental understandings of the variations in organic contents. Results of Langmuir isotherms indicate a significant increase in adsorption of heavier components compared to the increment in adsorption of methane components with higher TOC. This observation is further analyzed for production data of the multi-fractured horizontal wells which suggested the following: 1) desorption in the oil flowing wells increases as the saturation of the oil phase decreases, or in other words when the relative permeability of the gas increases. 2) In the gas flowing wells, desorption does not follow the trend of the relative permeability, while based on Langmuir pressure initial contribution is significant which declines as reservoir pressure drops. Further, for the gas flowing well, the production forecast from calibrated production model (with measured produced volumes) shows that post-production of 10 years, recovery is 3.66% in which contribution from desorption is about 17.6%. This observation in the production analyses highlights how with different adsorption capacities of heavier components, adsorption contribution in the production varies. Finally, post this study it is found that TOC plays a vital role in adsorption capacity, gas in place and in the production performance. The relation of the TOC with fluid characterization and recoverable reserves is complex and should be analyzed with the variation in adsorption and desorption capacity of lighter and heavier components.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2018
Admission routes3
Has abstractyes

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