Field Data Demonstrate Thermal Effects Important in Gas Well Pressure Buildup Tests
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".