Q&A with Mayank Ashar, Managing Director and Chief Executive Officer, Cairn India
Bibliographic record
Abstract
Q&A Mayank Ashar is the managing director and chief executive officer (CEO) of Cairn India. He has more than 36 years of experience in the international oil and gas industry. He previously served in various senior management and top leadership roles in global organizations such as BP, Petro-Canada, and Suncor Energy. He also served as the CEO and president of Irving Oil. In recognition of his operational excellence and large-scale project management leadership in the oil sands project with Suncor Energy, Ashar was named the “Operations Executive of the Year” by the Canadian Business magazine in 2003. Ashar holds bachelor’s degrees in chemical engineering and philosophy and economics, and master’s degrees in engineering and business administration from the University of Toronto. What are Cairn India’s major projects? Cairn India is one of the largest oil and gas exploration and production (E&P) companies in India, contributing approximately 27% of India’s domestic crude oil production. With our affiliates, we have been operating for more than 2 decades, playing an active role in developing India’s oil and gas resources. Our discovery of the Mangala field, the largest onshore crude oil discovery in India in more than 2 decades, opened up the prolific Rajasthan block. Since the resumption of exploration in 2013, Cairn India has made more than 12 discoveries. The total discoveries in Rajasthan alone are 37. The Rajasthan block is situated in the Barmer basin. The block contributed about 23% of India’s domestic crude oil production in FY 2015 [from April 2014 to March 2015]. To maximize the potential of the block and enhance ultimate recovery, Cairn India has initiated one of the world’s largest polymer flood EOR [enhanced oil recovery] programs in the Mangala field, which is the largest discovery made in the block to date. The polymer injection has started and is expected to lead to an increase in production from the field in FY 2016. There are plans to implement similar floods in the other two major fields, Bhagyam and Aishwariya. Apart from its oil reserves, there is a significant gas resource potential in our Rajasthan block. Together with our joint venture partner Oil and Natural Gas Corp. (ONGC), Cairn India is working toward creating appropriate infrastructure to monetize the gas potential, aiming to double gas production over this fiscal year. In addition, we have plans to drill 42 wells over the next 3 years and build a new processing terminal to increase production to approximately 100 million scf/D. The management committee has approved the Raageshwari deep gas field development plan for 100 million scf/D and contracting for this project is currently under way.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.311 | 0.164 |
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".