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Record W2896348576 · doi:10.2118/191621-18rptc-ms

Adaptive Option in Geological Modeling of Petroleum Reservoirs

2018· article· en· W2896348576 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.

Bibliographic record

VenueSPE Russian Petroleum Technology Conference · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Data Processing Techniques
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsFuzzy logicGridInterpolation (computer graphics)Basis (linear algebra)Computer scienceObject (grammar)Function (biology)Data miningGeologyArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Abstract The adaptive geological model differs from the deterministic one in that it takes into account the uncertainty of the initial data, has a degree of detail corresponding to the level of accuracy of the geological information, and reproduces the main regularities of the structure of the oil and gas object under consideration, primarily for the purpose of predicting its geology in undeveloped zones. At the first stage, adaptive geological models of separate layers identified by the results of detailed correlation of the considered object are constructed independently from each other, and then the single-layer models are summed up into a multilayer adaptive geological model of the object as a whole. With the adaptive approach, traditional methods of interpolation of geological and production data are not applied. The basis of adaptive geological modeling is the seismic data. The essence is that the seismic data vector is available both at the wells and at the points of the inter-well spacing, so any geological parameter can be calculated from this vector using a special fuzzy-logic function. At the same time, such function cannot be the same for the whole model polygon, so an additional so-called fuzzy-grid is developed. A grid with large cells, which contain local fuzzy-logic functions. Since fuzzy-logical functions are formed on the basis of geological and production information of the considered object, they can differ substantially for other objects. Thus, the mathematical apparatus of a geological model automatically modifies to the specific initial data, and therefore it is called adaptive. The calculated parameters of the adaptive geological model cells through which the wells pass are not necessarily the same as the actual well data. The reason is that these parameters are calculated using functions in which the initial data of neighboring wells participate. Deviations of calculated results from actual data characterize the degree of "defectiveness" of geological and production information. Thus, the adaptive geological model in comparison with the deterministic one gives a more meaningful result both about the geological structure of the considered object and the degree of reliability of its representation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.259
Teacher spread0.235 · 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