Bibliographic record
Abstract
Technology Focus I was asked to serve on the JPT Editorial Committee for another 3-year term and happily accepted the offer. In pre-paring for this month’s feature, I revisited my first Technology Focus writeup for the March 2016 issue. I concluded the piece by saying, “Before closing, I would like to bring your attention to two critical points as we experience one of the more severe economic downturns in the oil industry. First, research on technology for heavy-oil recovery must go on one way or another. … Second, cost-effective solutions should be sought and materialized immediately to sustain many ongoing heavy-oil (especially thermal) operations.” What has happened over the last 3 years in relation to these two issues I raised in March 2016? Here are some highlights of my observations. Heavy-oil recovery is a challenge mainly because of the high cost of investment and difficulties and uncertainties in operations; hence, the main issue is the reduction of cost per barrel of oil produced. This reduction to cost can be achieved either by recovery improvement or operational-/capital-expenditure (OPEX/CAPEX) reduction through optimization studies. Recovery improvement requires more research efforts and time-demanding technology development, but the cost reduction is less uncertain and the focus has been on both reduction methods, mainly CAPEX, during the low-oil-price term. OPEX constitutes a greater portion of the total cost than CAPEX. OPEX-reduction efforts have focused on the optimization of artificial lift in producing wells, steam delivery, and monitoring in injectors, as well as tackling problems such as emulsification, sand production, and asphaltene/wax precipitation. Solar steam is an option for OPEX reduction but is still a challenge because it requires high CAPEX and raises sustainability issues. Considering the development of new technologies for efficient recovery improvement, all agree that collaboration is a must, especially in carrying the research results to the field. Yet who (e.g., national oil companies, international oil companies, or service companies) will lead this action is still a question. Government involvement also should be part of this collaboration. Another necessary discussion point is the replacement of costly and environmentally risky steam operations. Nonsteam applications are showing promising results at the laboratory scale (e.g., solvents, electrical heating, and waterflooding with chemicals and nanomaterials), and methods to improve steam efficiency through chemical additives are yet to be tested at the field scale. The cost reduction per barrel of oil produced and the extension of sustainable production life by optimization have been two major areas of focus, but the investments in new technologies and recovery-improvement research have not received sufficient attention during the downturn. Recommended additional reading at nePetro: www.onepetro.org. SPE 189716 Shallow Horizontal-Well Cyclic Steam Stimulation in a Clastic Unconsolidated Unconventional Reservoir in Kuwait: A Case Study by Shaikha Al-Ballam, Kuwait Oil Company, et al. SPE 190111 Laboratory Tests Conducted To Perforate and Displace Viscous Oil From Saturated Formation Core To Help Optimize Steamflood Completion by Dennis J. Haggerty, Halliburton SPE 190770 Visualization of Heavy-Oil Mobilization by Associative Polymer by Tormod Skauge, Centre for Integrated Petroleum Research, et al.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.088 | 0.062 |
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".