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Record W2174237385 · doi:10.4043/23850-ms

Best Practice in Arctic Development Concept Selection - How to Avoid the Traps

2012· article· en· W2174237385 on OpenAlex
Chris Mole, Mike Paulin, Amy Sturge

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

VenueOTC Arctic Technology Conference · 2012
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsIntecsea (Canada)
Fundersnot available
KeywordsComputer scienceArcticSelection (genetic algorithm)Process (computing)Key (lock)Set (abstract data type)The arcticField (mathematics)Development planOperations researchRisk analysis (engineering)Process managementEnvironmental resource managementEnvironmental scienceEngineeringGeologyBusinessCivil engineeringArtificial intelligenceOceanography

Abstract

fetched live from OpenAlex

Abstract The process of selecting a field development concept following a discoveryinvolves a complex iterative interaction between its key elements ofsubsurface, drilling and completions, surface facilities, and commercial andregulatory considerations. The objective being to understand how differentrisks and uncertainties impact each scenario, leading to the final selection ofthe tsingle concept that best balances the key elements and extracts maximumvalue for all stakeholders. A recommended procedure for Arctic concept selection has been developed, using a building-block approach matched with a practical and systematic methodfor understanding the key drivers and uncertainties in a project. In order tocomplete this type of anyalysis, experienced professionals are equipped with atoolkit and set of processes that allow a disciplined approach to only doingwork that is focused on each decision that has to be made. This then leads tothe development of a decision based plan to extract the maximum value from anyopportunity. Also discussed are some of the common traps that can befall a conceptselection study such as: solving the wrong problem due to an inadequate projectframe; utilizing incorrect, invalid or out of date data; having inadequatesystems and tools in place to maintain focus and alignment; an inability toarticulate key insights; a lack of team integration; and finally the dangers ofan activity based workscope instead of a decision based one. Introduction Recent studies have estimated that 25% of the world's petroleum reserves arelocated in Arctic waters. Current energy demand has promoted renewed interestin the exploration and field development of offshore hydrocarbon basins inArctic and ice covered waters of the northern hemisphere. With the oilindustry's continued quest for oil and gas in frontier offshore locations, several developments have taken place in regions characterized by seasonal icecover and the presence of icebergs including the US Beaufort, North Caspian, Sakhalin Island, and the east coast of Canada. These new frontier developmentshave a higher cost per barrel than traditional developments. With thisknowledge, it becomes even more important to make good robust decisions whenanalyzing projects to invest in and maximize the value of capital employed. Additionally frontier basins have a lack of benchmark data to give confidencein development costs and schedules - the old " norms" simply don't apply inthese cases.

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)0.001
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 score0.684
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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