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Record W2060651206 · doi:10.2118/155737-ms

Probabilistic Forecasting of Unconventional Resources Using Rate Transient Analysis: Case Studies

2012· article· en· W2060651206 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsUnconventional oilProbabilistic logicTransient (computer programming)Computer scienceFlow (mathematics)Petroleum engineeringTight oilBoundary (topology)Tight gasOperations researchEconometricsGeologyFossil fuelMathematicsEngineeringHydraulic fracturingArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Reliable, early determination of long term production and ultimate recovery in oil and gas reservoirs is of utmost importance to E&P companies, reserves auditors and investors. In conventional reservoirs, the EUR can be reliably estimated once the drainage volume (hydrocarbon pore volume) has been established. This can be done using Rate Transient Analysis (RTA) if the presence of boundary dominated flow can be observed in the data. Unfortunately this approach is not easily applied to tight, fractured reservoirs because of the complexity of these reservoirs (which leads to non-unique reservoir characteristics) and the presence of persistent transient flow (which leads to non-unique estimations of ultimate recovery). In some instances, boundary dominated flow may not be observed until several years have elapsed during the producing life of the well. In recent years, there have been numerous contributions to the science of well performance-based methods for estimation of ultimate recovery of unconventional resources. Wattenbarger et al. (1998) and Brown et al. (2009) propose analytical techniques while Ilk et al. (2008), Valko and Lee (2010) and Duong (2011) have each proposed new empirical decline curve equations. While each of these methods has value, they do not specifically address the problem that underpins all unconventional well analysis, uncertainty. In 2011, the authors proposed the use of probabilistic rate transient analysis to help quantify this uncertainty. This approach acknowledges the non-uniqueness inherent in the RTA model inputs and allows for the systematic investigation of an allowable parameter space based on acceptable ranges of inputs such as the conductivity, spacing, complexity, length and height of the fractures and the matrix permeability. The result is the full set of possible production forecasts (in as much as the model can be said to capture the physics of the problem) from which the "most likely" production profile can be extracted and that help define the uncertainty in long-term recovery for the play. In this paper, we will further explore probabilistic rate transient analysis by presenting a case study from the Montney play in Canada. The primary objective of this work is to illustrate that a probabilistic approach can be practical, reliable and systematic, offering a viable alternative (or complement) to the standard deterministic techniques.

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.

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)0.001
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 score0.136
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.109
GPT teacher head0.332
Teacher spread0.223 · 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