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Record W4255891487 · doi:10.2118/2006-171

The Importance of Initial Reservoir Pressure for Tight-Gas Completions and Long-Term Production Forecasting

2006· article· en· W4255891487 on OpenAlexaffabout
F. Hategan, R.V. Hawkes

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsARC Resources (Canada)
Fundersnot available
KeywordsTight gasTerm (time)Production (economics)Petroleum engineeringEnvironmental scienceGeologyHydraulic fracturingEconomicsPhysicsMicroeconomics

Abstract

fetched live from OpenAlex

Abstract Conventional flow and buildup testing of gas wells in Alberta is common practice for 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 in recent years, the development of unconventional gas reservoirs has significantly increased. Deep tight gas reservoirs have become an important part of the unconventional gas resource and the paper will address several issues and pitfalls related to welltesting of tight gas. We are now seeing a large number of disciplines involved with completions. This diverse group is necessary due to the complexity surrounding tight gas reservoirs. Initial reservoir pressure is probably one of the most important "data points" for a completion 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. Furthermore, the 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 record high commodity prices in the energy sector experienced in recent months has 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 the 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 economical decisions. The hydrodynamics of tight gas, hydraulically fractured reservoirs are very complex and significant errors in matrix permeability and fracture parameters evaluation are often occurring. These errors have a much greater impact on predictions for long term 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 has increased to record levels. So has increased the 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 initially was predicted. These errors are considered the result of over estimating reservoir parameters such as the reservoir flow capacity (kh). Short references will be made to some of the factors determining the success of developing tight gas resource:Original-gas-in-place (OGIP) usually is 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)

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 score0.039
Threshold uncertainty score0.978

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)0.0000.000

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 head0.040
GPT teacher head0.288
Teacher spread0.248 · 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

Citations4
Published2006
Admission routes2
Has abstractyes

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