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Record W4246487483 · doi:10.2118/2007-053

Underground Gas Storage in Partially Depleted Gas Reservoir

2007· article· en· W4246487483 on OpenAlexaff
M. Soroush, Nasser Alizadeh

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCitationNatural gas fieldComputer scienceLibrary sciencePetroleum engineeringEngineeringNatural gasWaste management

Abstract

fetched live from OpenAlex

Abstract Main objective of this study is dealing with a real case study of an Iranian gas condensate reservoir for the purpose of underground gas storage. Doing such a study in this reservoir will aid developing this technology and also demonstrates new concept of underground gas storage in partially depleted gas reservoirs. After gathering some data about the reservoir and preparing a geological model for the field, a simulation plan considered for this field. Static model converted to a dynamic one by assigning reservoir fluid data. Finally, compositional model of the reservoir prepared and verified to be accurate through a history matching process. After being sure about accuracy of the model and validating it, different scenarios for underground gas storage developed. Depletion and gas storage scenarios made for the field and results obtained. Gas storage in partially depleted gas reservoir considered in scenarios for developing this field too. After comparing different scenarios some practical results achieved and best scenario for developing this field chosen. Introduction An underground gas storage system can be defined as a combination of a constant supply with a variable demand for economic advantages[1]. In other words, it helps to combine low-demand summer season and high-demand winter season. The whole process is injecting natural gas or rarely other gases into subsurface reservoirs in the periods that demands fall bellow the gas supply. When demands exceed the supply, the gas will be withdrawn from the reservoir. Fluctuating demands due to temperature and climate make it an economic process that is necessary in many cases for efficient use of the pipelines. It also helps to have an effective delivery during peak demand seasons. This process can also help producing oil or condensate and can be considered as an IOR method too. By increasing demand of gas in many areas of the world, developing storage plans and effective use of existing storage sources is a priority for engineering and economic advantages. Figure 1 illustrates natural gas supply and demand relation and clarifies importance of having some gas stored in low demand periods in order to use in high demand periods. Figure 1- Natural gas supply and demand. (Source from [2]) (Available in full paper) Depleted gas reservoirs called to be the best choice of underground gas storage and almost all early projects were developed in depleted gas reservoirs. There are of course other types of underground gas storage that are storing gas in aquifers and in caverns and salt structures. Peak load natural gas consumption has become a problem in big cities in Iran, especially in Capital. Underground gas storage technology seems to be a helpful key to overcome this trouble. Reservoir Summary A gas condensate reservoir is chosen for the purpose of underground gas storage that is located in central Iran. This field discovered in 1955 and its production started in 1959. It has a structure that is a northwest-southeast trending anticline approximately 25 kilometers long and about five kilometers wide. The structure is investigated by surface and seismic surveys and eight wells drilled on the field.

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 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 score0.807
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.231
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2007
Admission routes1
Has abstractyes

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