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Record W2471095416 · doi:10.3968/8502

Lower Napo Shale Formation in the Oriente Basin: Analysis of Reservoir Formation Conditions and Prediction of Favorable Areas

2016· article· en· W2471095416 on OpenAlexvenueno aff
Hong Zhang, Jing Li

Bibliographic record

VenueAdvances in petroleum exploration and development · 2016
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil shaleStructural basinGeologySource rockGeochemistryShale oilShale gasSichuan basinPetroleum engineeringGeomorphologyPaleontology

Abstract

fetched live from OpenAlex

Considering the current situation of Ecuador being rich in oil and poor in gas sources, we evaluate the exploration priority of the shale gas potential of the primary oil- and gas-bearing basin in the country—the Oriente Basin. Richness criteria for shale gas reservoirs are proposed according to the evaluation. On the basis of the main lithological characteristics, geochemical factors, depth, distributional stability, and data availability, the lower shale rock of the Napo Formation in the Oriente Basin is selected from five potential source rocks as the shale rock that shows the greatest potential for shale gas. The reservoir formation conditions of the Napo Formation are studied. The thickness of the lower Napo shale rock is obtained by studying the 38 wells in the basin. Then, the depths of the lower Napo shale rocks are mapped by interpreting 29 seismic lines in the basin. Next, the distribution of the geochemical factors of the basin is derived by analyzing the outcome of the predecessors. Finally, the areas with favorably rich shale gas are predicted by studying the different reservoir formation conditions. The northwest (blocks 11 and 18), east (blocks 12, 67, 18, 31, and 63), and west areas (blocks 29, 20, 22, 28, and 70) of the basin are prospective areas that are rich in shale gas (oil). This study provides guidelines for the exploration and development of the shale gas in the Oriente Basin.

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 score0.216
Threshold uncertainty score0.277

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 studies0.0000.000
Scholarly communication0.0000.002
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 head0.015
GPT teacher head0.236
Teacher spread0.221 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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