Old Unworkable Oil, Gas and Mineral Assets: There is No Room for Pride and Elitism, and to Remain Hostage to Litigations, Sadness, Past Blunders, or Even Glories-Unshackle Legacy Assets. Make Difficult Choices to Embrace a New Path and Make Your Asset Great Again!
Bibliographic record
Abstract
Determining the exact value of legacy assets in coal, minerals, oil, and gas that are currently under litigation in countries like India, Russia, the USA, Canada, and Saudi Arabia is challenging due to the lack of comprehensive public data. Litigation details, especially concerning asset valuations, are often confidential or not aggregated in publicly accessible databases. There has been a notable increase in climate-related lawsuits targeting major fossil fuel companies. As of recent analyses, 86 climate lawsuits have been filed against some of the world’s largest oil, gas, and coal-producing corporations, including BP, Chevron, Eni, ExxonMobil, Shell, and Total Energies. The number of cases filed against fossil fuel companies each year has nearly tripled since the Paris Agreement was reached in 2015. Western sanctions have significantly impacted Russia’s hydrocarbon revenues, with varying effects across different sectors. These sanctions have introduced legal and financial challenges for Russian energy assets, leading to disputes and potential litigations. The valuation of fossil fuel assets is becoming increasingly complex due to climate change concerns and market volatility. For instance, the oil industry faced significant challenges when a price war between Russia and Saudi Arabia coincided with a global crisis, leading to unprecedented market conditions. For a minerals, oil, and gas company dealing with legacy litigation issues, a multi-dimensional approach is necessary. Combining legal, financial, environmental, stakeholder, and risk management strategies will help mitigate risks, optimize assets, and enhance long-term sustainability. Globally, it’s estimated that there are approximately 29 million abandoned oil and gas wells. The number of inactive wells varies significantly across countries, influenced by factors such as production rates, regulatory frameworks, and economic conditions. The market for selling abandoned mines, oil, and gas fields is either non-existent or inefficient, often serving purposes other than reinvestment and redevelopment. A national study by the Indian Bureau of Mines identified 297 abandoned mine sites, of which 106 required reclamation and rehabilitation. Subsequently, 24 of these sites became operational again, leaving 82 sites needing attention. An asset is not for keeps, if it does not get rent or return: Anonymous
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 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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".