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Record W2229217538 · doi:10.2118/01-11-04

Field Data Demonstrate Thermal Effects Important in Gas Well Pressure Buildup Tests

2001· article· en· W2229217538 on OpenAlexaboutno aff
Robert Hawkes, Zhaoyang Su, D. Leeech

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTransient (computer programming)Joule–Thomson effectThermalMechanicsPetroleum engineeringCabin pressurizationWellboreThermodynamicsGeologyEngineeringMechanical engineeringPhysics

Abstract

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Abstract Thermal effects become an important factor in gas well pressure buildup tests with a surface shut-in. Welltest data from Western Canada demonstrate that, due to the PVT relationship, temperature changes of wellbore fluids can cause thermal-storage effects that can be misinterpreted as complex reservoir characteristics by unexplained pressure transient response or nonunique pressure response. Understanding the duration of thermalffects can improve the interpretation of buildup tests to allow enough time for observation of true reservoir pressure transient responses. Temperature effects can be significant and extended, depending on many in situ and imposed factors. Thermal effects can be important, even when pressure recorders are placed at the midpoint of the producing interval, due to Joule-Thompson effects. Field observations demonstrate that Joule-Thompson cooling exists in many gas wells. Cooling effects were observed to extend 50 m in the formation, suggesting that significantly long time periods are required for formation fluids to reach thermal equilibrium after shut-in. Implications of not understanding the thermal effects in a buildup test analysis can result in "heterogeneities" being interpreted when there are none and skin calculations indicating an improved wellbore condition when the well has only been perforated. It is the purpose of this paper to show how temperature data diagnostics can be used to aid the welltest engineer in distinguishing between general wellbore effects and reservoir behaviour in the pressure transient data. In this paper, we have adopted the acronym PTTA for (P)ressure (T)emperature (T)ransient (A)nalysis. Introduction Use of pressure transient tests has become an established practice to determine reservoir parameters, potential reserves and near wellbore conditions. It is widely recognized that wellbore and near wellbore effects can distort and mask true reservoir responses, specially in the early stages of a buildup test. Both analytical and practical efforts have been developed to quantify and evaluate these effects by using concepts of skin, wellbore storage and phase redistribution(1–5). In addition, wellbore dynamics beyond wellbore storage and phase redistribution, such as liquid influx/efflux, wellbore (and near wellbore) clean-up, plugging, recorder effects, etc., have been discussed in the past decade(6–8). However, theoretical and numerical analyses of thermal effects have only been addressed recently(9–12). Implicit assumptions that require well test data to reflect information from an isothermal condition in both wellbore and reservoir are generally made in interpretation models. Such assumptions hold only if the pressure and temperature recorders are placed at the middle of the perforation interval, and neither Joule- Thompson cooling or heating occurs. Although various aspects of heat transfer between a wellbore and the formation have been studied, most of them are steady-state models, which assume that fluid properties and flow rate are not functions of time in theellbore(9). Thermal effects are more pronounced in gas wells because gas properties are strong functions of pressure and temperature. Observations demonstrate that gas well pressure buildup tests often exhibit complex reservoir pressure behaviours. These models, such as narrow channels, double porosity, or multiple nearby boundaries, often did not agree with the actual reservoir system and, consequently, resulted in incorrect interpretation of well and formation parameters(12).

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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".

Quick stats

Citations9
Published2001
Admission routes1
Has abstractyes

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Same venueJournal of Canadian Petroleum TechnologySame topicHydraulic Fracturing and Reservoir AnalysisFrench-language works237,207