The Importance of Initial Reservoir Pressure for Tight Gas Completions and Long-Term Production Forecasting
Bibliographic record
Abstract
Abstract Flow and buildup testing of gas wells in Alberta is common practice for the determination of reservoir pressure, permeability and wellbore skin. Post-frac welltesting is still regarded as the preferred welltesting method, due in part to current government regulations for initial testing requirements. With the maturing of the Western Canadian Sedimentary Basin (WCSB) in recent years, the development of unconventional gas reservoirs has significantly increased. Deep tight gas reservoirs have become an important part of unconventional gas resources and this paper will address several issues and pitfalls related to the welltesting of tight gas. Initial reservoir pressure is probably one of the most important ‘data points’ for reservoir engineering and completion design and yet one of the most abused and assumed parameters. Field examples will highlight major errors that are likely to occur when post-frac flow and buildup tests are analyzed for tight gas reservoirs without precise knowledge of the initial reservoir pressure. This paper will demonstrate that errors of reservoir pressure as small as 2% can lead to significant overestimates of the well 's recoverable reserves. Introduction The strong commodity prices in the energy sector experienced in recent years have resulted in an increased activity level for the development of unconventional tight gas reservoirs. Natural gas price is the major factor that will determine the economic viability of tight gas reservoirs. However, there are additional factors that should be taken into consideration, such as original-gas-in-place, completion efficiency, well spacing and the ultimate recoverable reserves. Unlike conventional gas reservoirs where proven reservoir engineering techniques are easily applied, the predictability of deep tight gas reservoirs is much more difficult to achieve. Too often the post-frac welltest results from wells completed in tight gas reservoirs are used to establish initial pressure, completion effectiveness and reservoir permeability for production forecasting for economic decisions. The flow behaviour of hydraulically fractured tight gas reservoirs is very complex and significant errors in the determination of matrix permeability and fracture parameters often occur. These errors can have a significant impact on predicting gas production and total cumulative gas recovery. Discussion With the maturing of the WCSB and high natural gas prices, the activity levels in the oil and gas industry have increased to record levels. So has an increased number of tight gas well completions. Generally speaking, performance evaluation of tight gas wells reveals a trend where the production performance of the wells is much less than what was initially predicted. These errors are considered the result of overestimating reservoir parameters such as the reservoir flow capacity (kh). Short references will be made to some of the factors determining the success of developing a tight gas resource:Original-gas-in-place (OGIP) is usually determined by the volumetric method and requires knowledge of reservoir pressure, mapping and petrophysical parameters. One equation used for estimating OGIP is:Equation (1) (Available In Full Paper)Hydraulic fracturing is a necessary step in the completion process in tight gas reservoirs in order to achieve commercial production.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".