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Record W2075934634 · doi:10.2118/138155-ms

Application of the "Continuous Estimation of Ultimate Recovery" Methodology to Estimate Reserves in Unconventional Reservoirs

2010· article· en· W2075934634 on OpenAlexfundno aff
S. M. Currie, D. Ilk, Thomas Alwin Blasingame, Dave Symmons

Bibliographic record

VenueCanadian Unconventional Resources and International Petroleum Conference · 2010
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
FundersShell Canada
KeywordsExtrapolationLimit (mathematics)EconometricsTransient (computer programming)Flow (mathematics)Work (physics)EstimationBoundary (topology)Exponential functionProxy (statistics)Production (economics)MathematicsStatisticsComputer scienceEconomicsMathematical analysisEngineering

Abstract

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Abstract The estimation of reserves in unconventional reservoirs using rate-time decline relations is both challenging and often non-unique due to the very long transient flow periods exhibited by the production data. The misuse of rate-time relations during the transient flow regime can result in significant overestimation of reserves. Consequently the development of a systematic methodology which uses rate-time production data analysis and also deals with the uncertainty associated with reserve estimates should be beneficial. This work presents the application of the recently introduced "Continuous Estimation of Ultimate Recovery" or "Continuous EUR" methodology (Currie et al. 2010) to estimate reserves in unconventional reservoirs by providing a variety of tight and shale gas examples. The "Continuous EUR" methodology is a procedure that employs several rate-time models to produce a profile of EUR versus production time. Upper and lower limits of ultimate recovery are established thereby reducing the uncertainty in reserves estimation prior to the onset of boundary-dominated flow. We integrate both traditional and new rate-time relations to provide the upper limit for EUR. We show that the rate-time relations that better represent the transient flow regimes (i.e., the power law exponential rate decline relation) provide a more accurate upper limit for EUR compared to tradition rate decline relations (i.e., Arps' hyperbolic relation). We also use a straight line extrapolation technique and the power law exponential relation to produce forecasts of rate-time data that are influenced by boundary-dominated flow. The EUR estimates from these relations are used to establish a lower limit for reserves. The difference between the upper and lower limit of reserves decreases with time and converge to the "true" value of reserves. The proposed methodology is applicable for all reservoir systems where production data is continuously acquired. In this work, the proposed methodology is extremely useful for estimating time-dependent reserves in unconventional reservoirs. We successfully demonstrate that the "Continuous EUR" methodology is a valuable tool for reducing the uncertainty in estimating reserves for unconventional gas reservoirs. Introduction Unconventional reservoir systems have recently become a topic of increased interest because of their potential for significant hydrocarbon production and reserves potential. These reservoir systems offer unique challenges as they are difficult to characterize and produce using conventional methods due to their low permeability nature. In particular, unconventional gas reservoir (i.e., tight and shale gas reservoirs) require enhanced drilling and completion techniques (i.e., horizontal drilling and hydraulic fracturing) to establish production at economic rates. In addition, new approaches for reservoir characterization and production analysis have been developed to better describe reservoir behavior and predict long-term well performance.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.024
GPT teacher head0.297
Teacher spread0.273 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2010
Admission routes1
Has abstractyes

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