Case Studies Demonstrating Sustainability and Risk Evaluations in Environmental Due Diligence for Upstream Oil and Gas Transactions in Alberta
Bibliographic record
Abstract
Abstract Recent advances in the unconventional oil & gas plays have resulted in increased investment activities in the upstream space aimed at monetizing large acreage lease holdings as well as cash management to fund exploration and development of new plays. Often these transactions involve the global investment community at both the private and government owned company level. Coupled with these transactions, Global corporations are moving from a narrow view of environmental health and safety (EHS) management to a holistic Corporate and Social Governance (CSG) approach that includes EHS as just one component of managing a sustainable and profitable business. To compete successfully, it is critical that investors understand the implications of CSG risk management strategies and their impact on sustainable business growth plans. Traditional due diligence activities have focused on quantifying and minimizing EHS impairment liabilities, at best considering a snap shot of current conditions and regulatory operating status. This is due to the fact that the cash flows of established operations are well understood. However, for upstream oil and gas plays with little to no operational assets, the most significant risks are not environmental impairment but rather are the environmental and sustainability risks that could affect asset development. Further, most oil and gas investments involve vast amounts of land and leaseholds, often at various stages within the project life cycle, for which traditional ASTM type due diligence methodologies are not practical. This paper uses case studies involving due diligence for acquisitions in the Alberta oil reserves to illustrate a forward-looking assessment methodology for evaluating key non-technical risks such as community relations, water availability and management, sustainable risk management strategies, and reclamation planning. The case studies demonstrate how the results of the due diligence activities were incorporated into the financial models for the investment, allowing the investors to more fully understand the potential risks and opportunities associated with the acquisitions and asset development and incorporate these into their financial models. The approaches used in these case studies represent a significant and important shift in thought processes around risk evaluation and prioritization during due diligence.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".