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Record W2028367390 · doi:10.2118/00-05-ge

Lessons Learned-How to Do It Successfully

2000· article· en· W2028367390 on OpenAlexaboutno aff
Lorne Kelly

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsProject teamProcess (computing)Set (abstract data type)Project managementClass (philosophy)Project planningKey (lock)EngineeringEngineering managementProcess managementComputer scienceSystems engineering

Abstract

fetched live from OpenAlex

Much has been learned during the successful completion of the first major natural gas project in the Atlantic region. How to capture the lessons learned from the project holds many lessons in itself. Background A Lessons Learned Report was published to document the key lessons from a world class natural gas project; the first of its kind for Atlantic Canada. Because the project itself was very successful, capturing information on how the project team met and overcame the many challenges the project faced will be of benefit to those who undertake future projects of this size and complexity. Much can be learned from studying the lessons learned process (how the data was gathered, categorized, interpreted, prioritized, and summarized). This article describes the lessons learned process and what we have learned from it. It does not focus on what specific lessons were learned from the first major natural gas project in the Atlantic region. Rather, it deals with how the project team went about gathering the lessons and how the team members successfully set out the nuggets of what was learned for those who will manage and participate in future projects of a similar size and complexity. The Lessons Learned Process Objective and Approach A lessons learned team was formed to capture the key lessons learned during all phases of the project (front end planning and approvals, engineering design, construction, and commissioning), so that these lessons could be carried forward to guide others who will manage and participate in other similar East Coast developments or major capital projects. Task Team The lessons learned task team was comprised of representatives from the various organizational functions of the project. The team was supported by experienced facilitation and technical documentation consultants. Terms of Reference The terms of reference set out some boundary conditions or limitations. The terms specified that:The task team was to take direction from a steering.The task team was only assigned part-time and the members were not to be significantly diverted from their primary duties on the project.Part-time facilitation and documentation support was to be made available.The data was to be gathered before most of the project staff was demobilized.Data was not to be limited to only that from senior management, but was to include a representative sample of on-site supervisors.Data gathering was to include some individual and some group interviews with key project staff.A representative sample of contractors, suppliers, and regulators were also to be interviewed. Data Gathering Data was gathered by the task team through over ninety interviews with individuals and groups from all aspects of the project. Interviewees were asked to identify three to five things that they believed contributed most significantly to the project's success, and three to five things that they thought might have been done better. All interviewees were guaranteed anonymity and interview results were maintained in a confidential database. Interviewees were given the opportunity to validate their input by reviewing and approving or correcting the interview summary sent to them later.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.220
Teacher spread0.208 · 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 designNot applicable
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
Published2000
Admission routes1
Has abstractyes

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