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Record W2036797002 · doi:10.2118/2009-189

Analytical Solution for Constant Pressure Production from Hydrate-Capped Gas Reservoir

2009· article· en· W2036797002 on OpenAlexafffund
S. Hamed Tabatabaie, M. Pooladi‐Darvish

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of Calgary
FundersNatural Resources CanadaNational Science Council
KeywordsConstant (computer programming)HydrateClathrate hydratePetroleum engineeringProduction (economics)Materials scienceThermodynamicsProcess engineeringGeologyChemistryComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract The objective of this study is to develop an analytical model to calculate hydrate recovery and rate of gas generation when a hydrate-capped gas reservoir is produced at a constant bottomhole pressure. A similar model has been developed in the past for constant rate production. Such models are useful, when large number of sensitivity studies are necessary to evaluate the uncertainty in reservoir parameters and only limited specific properties of a hydrate reservoir are available. In this work, a tank-type material balance equation is considered. This is combined with the solution for the temperature of the hydrate cap to obtain the rate of gas generation during the period of constant bottomhole pressure production from a hydrate-capped gas reservoir. A numerical solution was used to validate the assumptions made to develop the analytical model; this numerical model relaxes some of these assumptions. The role of hydrate in improving the productivity and extending the life of hydrate-capped gas reservoir is demonstrated. The results show that the overlying hydrates can have a significant effect on improving the productivity of the underlying gas reservoir and increasing the reserve. Scope of the Study In this study we develop an analytical model to calculate hydrate recovery and rate of gas generation when gas is produced at a constant bottomhole pressure and decomposition occurs throughout the hydrate cap. For this purpose, a volumetric material balance equation is developed. The solution for the temperature of the hydrate cap is combined with the tank-type material balance equation and is solved for the rate of gas generation during the period of constant bottomhole pressure production from a hydrate-capped gas reservoir. In the following, after presenting the analytical modeling of the process, we perform a sensitivity study to investigate the effect of various reservoir parameters on reservoir performance, including the importance of porosity, thickness of hydrate zone, thermal conductivity and well bottomhole pressure. Analytical Model In order to obtain an analytical model to predict the recoverability of hydrate during constant bottomhole pressure production from a hydrate-capped gas reservoir, we assumed decomposition of hydrate occurs simultaneously throughout the hydrate layer. Four equations are used to develop the analytical model for predicting the behavior of the hydrate-capped gas reservoir during the period of constant bottomhole pressure production:Material balance equationEnergy balance equationHydrate decomposition modelInflow performance equation In the following, first the above mentioned equations are explained, and then the mathematical modeling of the process leading to the analytical model is presented. 1) Material balance equation: It is assumed that the pressure and temperature within the reservoir are instantaneously uniform; a depletion type material balance model can be used to predict the average reservoir pressure of the hydrate-capped gas reservoir. The simplified form of this equation for a volumetric hydrate-capped gas reservoir can be written as (Gerami et al., 2006): Equation (1) (Available in full paper) In the above equation, Gp and Gg are cumulative gas production and generation, respectively, and Gf is the initial free gas-inplace.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.996

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)0.0050.000

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 head0.021
GPT teacher head0.239
Teacher spread0.218 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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".

Quick stats

Citations3
Published2009
Admission routes2
Has abstractyes

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