Current Status of Commercial In Situ Combustion Projects Worldwide
Bibliographic record
Abstract
Abstract Picture of Alex Turta (Available In Full Paper) Alex Turta is a project leader for Improved Oil Recovery at Alberta Research Council (ARC) in Calgary. His research interests include primary recovery of heavy oils, waterflooding of light oils, and thermal recovery methods for heavy oil. He has extensive experience of heavy oil exploitation, from laboratory to field pilots, and has undertaken international consultancy for thermal pilot evaluation. He assisted in the development of the enhanced oil recovery evaluation software PRIze. Alex holds M.Sc. and Ph.D. degrees from the University of Oil and Gas and Petroleum Engineering, Bucharest, Romania, and worked previously at the Research and Development Institute for Oil and Gas, Campina, Romania. He is a co-inventor of the THAI and CAPRI processes for heavy oil recovery and upgrading, and is a member of SPE, the Petroleum Society and the Canadian Heavy Oil Association. Picture of Dr. S. K. Chattopadhyay (Available In Full Paper) Dr. S. K. Chattopadhyay is Chief Chemist for the Oil and Natural Gas Corporation (ONGC) Ltd., India, working at the Mehsana Asset. He joined ONGC Ltd. in 1983 as a Graduate Trainee in Chemistry. Over the last 24 years at ONGC, he has gained experience working at different offshore and onshore production installations, the LPG/CSU/C2-C3 process control laboratory, onshore drilling rigs, the in-situ combustion process monitoring laboratory and, presently, he is working in a multi-disciplinary team for the monitoring, interpretation and process control of the commercial in-situ combustion process at the Balol and Santhal Fields of the Mehsana Asset, India. Dr. Chattopadhyay has presented several technical papers on the in-situ combustion process at various national and international conferences and symposiums. He graduated with a Ph.D in chemistry from the University College of Science, Calcutta University, India. Picture of R. N. Bhattacharya (Available In Full Paper) R. N. Bhattacharya is the General Manager (Reservoir) for the Oil and Natural Gas Corporation (ONGC) Ltd., India, working at the Mehsana Asset. He is presently working on the company 's commercial in situ combustion scheme in Western India. Mr. Bhattacharya has had experience working on different assets and projects for ONGC, including overseas projects. He has over 30 years of oil industry experience as petrophysicist, reservoir engineer and in contract monitoring. Mr. Bhattacharya earned an M.Sc (physics) in 1972 and M.Sc. (geophysics) from Banaras Hindu University, India. He studied reservoir engineering at the India School of Mines (ISM), India, the University of Austin and Stanford University, USA. He is the author of several technical papers and numerous technical reports. Picture of Alexandru Condrachi (Available In Full Paper) Alexandru Condrachi is a Reservoir Engineer for PETROM S. A. Member of OMV Group, E&P Central Region Division, Ploiesti. He earned a B.C., M.S. and Ph.D. degrees from the Petroleum-Gas University of Ploiesti, Romania. Picture of Wayne Hanson (Available In Full Paper) Wayne Hanson has been with the Bayou State Oil Corporation (BSOC), Bellevue, Louisiana since 1980. Initially, he served as a Petroleum Chemist, and starting from 1990, he has been Supervisor of the BSOC In-Situ Combustion Project.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.013 |
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".