The Oil and Gas ROFR: Understanding Current ROFR Issues from the Point of View of the Transactional Lawyer, the Litigator, and In-House Counsel
Bibliographic record
Abstract
Rights of first refusal and other preferential or pre-emptive rights (together, ROFRs, and individually a ROFR) routinely find their way into oil and gas industry agreements. Disputes often arise because of the complex nature and significant economic consequences of ROFRs. In recent years, a number of reported cases, either relating directly to ROFRs or more generally relating to contractual interpretation, have clarified (or at times muddied) the waters surrounding the use, application, and interpretation of ROFRs. However, most ROFR disputes never result in a reported decision because the parties typically negotiate solutions long before trial.The authors consider current trends involving ROFRs in oil and gas agreements, and how they believe the law and legal practice surrounding ROFRs might continue to evolve in the years to come. The authors do not attempt to rehash the fundamentals of the law surrounding ROFRs; instead, they focus on how the courts have dealt with ROFRs in recent cases as well as how corporate lawyers and in-house counsel grapple with ROFRs day-today. The authors utilize the ROFR provisions found in industry standard contracts to analyze outstanding areas of uncertainty as well as what lawyers should contemplate prior to including a ROFR in an agreement. Additionally, the article examines the implications of recent rulings on the duty of good faith that may affect ROFRs. Finally, the article considers selected subjects of topical interest, including ROFRs in the context of busted butterfly transactions, insolvency proceedings, and package deals.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.030 | 0.058 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.024 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".