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Net reserves evaluation and sensitivity analysis of shale gas project under royalty & tax system in British Columbia, Canada

2019· article· en· W2919179790 on OpenAlexaboutno aff
Guifang Fa, Rui-e Yuan, Jun Lan, Qian Zou, Zhiyu Li

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2019
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsDirectional drillingOil shalePetroleum engineeringOperating expenseProduction (economics)Investment (military)Fossil fuelNatural resource economicsPetroleumEnvironmental economicsBusinessEconomicsDrillingEngineeringWaste managementFinanceGeology

Abstract

fetched live from OpenAlex

With the declination of production and increasing of fossil-fuel demand, petroleum companies strive to maximize production by conducting more advanced drilling operations, such as extended reach, horizontal and high-pressure/high-temperature (HP-HT) drilling and multi-stage hydraulic fracturing, which are expanding globally into unconventional resources drilling. Shale gas, which constitutes for a significant percentage of the natural gas resource base and offers tremendous potential for future reserve and production growth, was becoming the increasingly important asset for petroleum companies. This paper took a shale gas project in British Columbia (Canada) as an example, combining the characteristics of the shale gas reservoirs with royalty & tax policy of this region. The research on shale gas reserve evaluation and the principles of net reserve calculation under royalty & tax contract was carried out. The four main aspects such as technique, economy, commerce and engineering, were studied to analyze the influence on net reserves from the following factors: production, declining rate, development plan, oil price, Opex(operating costs), Capex(investment) and taxes, etc. Sensibility analysis was conducted by adopting the most weighted factors, such as oil prices, production, Opex and Capex. All the effort was to put forward the corresponding suggestions on optimizing development strategy, solve the current reserves evaluation problems of shale gas, and provide reference for the shale gas assets transaction, development and perfection of reserves value evaluation.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.615

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 head0.017
GPT teacher head0.230
Teacher spread0.213 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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