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Evaluating Conceptual Plays

2008· book-chapter· en· W3103226380 on OpenAlexaboutno aff
P.J. Lee

Bibliographic record

VenueOxford University Press eBooks · 2008
Typebook-chapter
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Resource (disambiguation)Process (computing)Conceptual modelComputer scienceConceptual frameworkData scienceGeographyRisk analysis (engineering)GeologyBusinessEpistemology

Abstract

fetched live from OpenAlex

A conceptual play has not yet been proved through exploration and can only be postulated from geological information. An immature play contains several discoveries, but not enough for discovery process models (described in Chapter 3) to be applied. The amount of data available for evaluating a conceptual play can be highly variable. Therefore, the evaluation methods used are related to the amount and types of data available, some of which are listed in Table 5.1. Detailed descriptions of these methods are beyond the scope of this book. However, an overview of these and other methods will be presented in Chapter 7. This chapter deals with the application of numerical methods to conceptual or immature plays. For immature plays, discoveries can be used to validate the estimates obtained. In this chapter, the Beaverhill Lake play and a play from the East Coast of Canada are examined. A play consists of a number of pools and/or prospects that may or may not contain hydrocarbons. Therefore, associated with each prospect is an exploration risk that measures the probability of a prospect being a pool. Estimating exploration risk in petroleum resource evaluation is important. Methods for quantifying exploration risks are described later. Geological factors that determine the accumulation of hydrocarbons include the presence of closure and of reservoir facies, as well as adequate seal, porosity, timing, source, migration, preservation, and recovery. For a specific play, only a few of these factors are recognized as critical to the amount of final accumulation. Consequently, if a prospect located within a sandstone play, for example, were tested, it might prove unsuccessful for any of the following reasons: lack of closure, unfavorable reservoir facies, lack of adequate source or migration path, and/or absence of cap rock. The frequency of occurrence of a geological factor can be measured from marginal probabilities. For example, if the marginal probability for the presence-of-closure factor is 0.9, there is a 90% chance that prospects drilled will have adequate closure. For a prospect to be a pool, the simultaneous presence of all the geological factors in the prospect is necessary. This requirement leads us to exploration risk analysis.

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)0.013
metaresearch head score (Gemma)0.088
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.028
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.088
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0010.003
Scholarly communication0.0080.012
Open science0.0020.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0280.003

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 head0.076
GPT teacher head0.266
Teacher spread0.191 · 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 designTheoretical or conceptual
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".

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Citations0
Published2008
Admission routes1
Has abstractyes

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