MétaCan
Menu
Back to cohort
Record W1995136273 · doi:10.1115/ipc2014-33521

Access and Logistics Challenges in Mountain Terrain Pipeline Projects

2014· article· en· W1995136273 on OpenAlexaboutno aff
Mark G. McDougall, Ken Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline transportPipeline (software)TerrainPort (circuit theory)Transport engineeringTonnageSupply chainPetroleum industryEngineeringBusinessCivil engineeringGeographyGeology

Abstract

fetched live from OpenAlex

Oil and gas production in Canada’s west has led to the need for a significant increase in pipeline capacity to reach export markets. Current proposals from major oil and gas transportation companies include numerous large diameter pipelines across the Rocky Mountains to port locations on the coast of British Columbia (BC), Canada. The large scale of these projects and the rugged terrain they cross lead to numerous challenges not typically faced with conventional cross-country pipelines across the plains. The logistics and access challenges faced by these mountain pipeline projects require significant pre-planning and assessment, to determine the timing, cost, regulatory and environmental impacts. The logistics of pipeline construction projects mainly encompasses the transportation of pipe and pipeline materials, construction equipment and supplies, and personnel from point of manufacture or point of supply to the right-of-way (ROW) or construction area. These logistics movement revolve around the available types of access routes and seasonal constraints. Pipeline contractors and logistics companies have vast experience in moving this type of large equipment, however regulatory constraints and environmental restrictions in some locations will lead to significant pre-planning, permitting and additional time and cost for material movement. In addition, seasonal constraints limit available transportation windows. The types of access vary greatly in mountain pipeline projects. In BC, the majority of off-highway roads and bridges were originally constructed for the forestry industry, which transports logs downhill whereas the pipeline industry transports large equipment and pipeline materials in both directions and specifically hauls pipe uphill. The capacity, current state and location of these off-highway roads must be assessed very early in the process to determine viability and/or potential options for construction access. Regulatory requirements, environmental restrictions, season of use restrictions and road design must all be considered when examining the use of or upgrade of existing access roads and bridges. These same restrictions are even more critical to the construction of new access roads and bridges. The logistics and access challenges facing the construction of large diameter mountain pipelines in Western Canada can be managed with proper and timely planning. The cost of the logistics and access required for construction of these proposed pipeline projects will typically be greater than for traditional pipelines, but the key constraint is the considerable time requirement to construct the required new access and pre-position the appropriate material to meet the construction schedule. The entire project team, including design engineers, construction and logistics planners, and material suppliers must be involved in the planning stages to ensure a cohesive strategy and schedule. This paper will present the typical challenges faced in access and logistics for large diameter mountain pipelines, and a process for developing a comprehensive plan for their execution.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0060.004
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0190.002

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 head0.032
GPT teacher head0.249
Teacher spread0.216 · 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 designQualitative
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
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicGeotechnical Engineering and Underground StructuresFrench-language works237,207