Effective HSES Management for Multi-national Onshore Oil Operations in North Africa - Fostering A Way of Thinking
Bibliographic record
Abstract
Abstract The purpose of this paper is to relate the experience gained with a project conducted recently to produce the Corporate Health, Safety, Environment and Social Management System “HSES MS” for TransGlobe Energy Corporation (TransGlobe), a Canadian based onshore oil company. The HSES MS was developed as a management tool to be applied to TransGlobe's activities worldwide, including both corporate headquarters in Canada, and operations in Egypt, as well as potential future operations in other countries. The system was developed to guide TransGlobe's HSES management for the next 5 years as a starting phase. The requirement for international application was a significant challenge, requiring that the system be flexible and facilitate a “way of thinking” that provides consistent results and effectiveness in various contexts, rather than a prescriptive or rigid approach outlining “this is how you will do it” that may not be appropriate or effective in multiple business contexts, cultures and jurisdictions. Work started in April 2013 with the objective of facilitating compliance with International best practice, applicable laws and regulations in Canada and Egypt, as well as compliance with International Finance Corporation (IFC) performance standards and guidelines. Key considerations in developing the HSES MS included gaining early and strong commitment from TransGlobe's senior management, fostering and maintaining close working relationships with personnel with a wide range of functions within the company, and in joint venture (JV) operating companies, incorporating and adapting best practices already employed by TransGlobe and JV partners, and incorporating emerging best practice with respect to stakeholder consultation and dispute resolution. The final product was rolled out to the company by the Chief Operating Officer in January 2014, and is helping to change the HSES culture within the company and associated JV companies. Fully understanding and incorporating the perspectives, and adopting or adapting current practices, of personnel across TransGlobe's administrative and operations functions, as well as joint venture companies, has been a significant challenge. The process of full integration and implementation is ongoing. This paper summarizes the thought process that was employed to develop the Management System, the main components included in the HSES MS, briefly describes roll out activities that were undertaken to bring the system out of the document and into reality, and discusses staff acceptance and reaction to it, with some examples of successes and lessons learned during development and first stages of implementation. The paper may be of interest to other oil and gas companies already with, or which intend, to expand operations in multiple jurisdictions, seeking to improve HSES management internally, to encourage continual improvement in joint venture partners, and/or striving to incorporate emerging best practices in management of stakeholder and community relations.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".