Life Cycle Management - The Myth Becomes Reality - A Case Study With Burlington Resources Canada Ltd.
Bibliographic record
Abstract
Abstract This paper is the second in a series of papers covering the spectrum of Life Cycle Management. The first paper provided an approach for the execution of a Life Cycle Management Plan. The approach was based upon a combination of Workflow, Integration and Notification (WIN). Current "best of breed" applications and business processes would be retained and the WIN approach used to "bridge the gaps" in bothbusiness processes and software, the result being proactive communication in the organization and effective management reporting to drive business decisions. This second paper is a case study with Burlington Resources Canada Ltd (BRC). It reviews the solution being implemented around the "Commencement of Production" (from licence / permit through to on-production) in the Life Cycle of aWell. BRC's existing business processes (with some optimization), were employed. BRC already had a solidinfrastructure of "silo" applications. Both a project team and a process owner were identified at the beginning of the implementation, these being key to the success of the project.An examination of the associated value, benefits and issues for this phase of Life Cycle Management is included as well as future expectations for the next phases. Introduction This paper is the second in a series of papers covering the spectrum of Life Cycle Management. The first paper provided an approach for the execution of a Life Cycle Management Plan. The approach was based upon a combination of Workflow, Integration and Notification (WIN). Current "best of breed" applications and business processes would be retained and the WIN approach used to "bridge the gaps" in both business processes and software, the result being proactive communication in the organization and effective management reporting to drive business decisions. This second paper is a case study with Burlington Resources Canada Ltd.(BRC). It reviews the solution being implemented around the "Commencement of Production" (from licence / permit through to on-production) in the Life Cycle of a Well. BRC's existing business processes (with some optimization), were employed. BRC already had a solid infrastructure of "silo" applications. Both a project team and a process owner were identified at the beginning of the implementation, these being key to the success of the project. An examination of the associated value, benefits and issues for this phase of Life Cycle Management is included. Business Challenges Burlington Resources Canada Ltd. ("BRC") is a division of Burlington Resources in Houston. BRC has on staff many highly skilled employees who do their jobs very effectively and efficiently. The Company is organized according to functional departments, such as Geology, Land, Drilling, etc. Each of these departments performs their duties very well. However, the maturing of the Western Canadian Sedimentary Basin and the focus on costs and productivity have introduced newchallenges. High volume expectations, maintaining competitive advantage, and targeted drilling programs in excess of 800 wells per year lead BRC to realize their well life cycle data and processes needed to be managed more efficiently. One of the key areas of improvement surrounded the decision making and communication of well tie-in decisions and ultimately bringing wells on production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".