Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 196362, “Philosophy of EOR,” by Tayfun Babadagli, SPE, University of Alberta, prepared for the 2019 SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition, Bali, Indonesia, 29-31 October. The paper has not been peer reviewed. Despite the substantial investment dedicated to research-to-pilot scale investigations, the ultimate profit from enhanced oil recovery (EOR) applications has been below expectations since the 1980s (less than 10% of total production). The author writes that revisiting and challenging the knowledge and dogmatic assumptions gathered during 5 decades of EOR is necessary. In the complete paper, a philosophy for the future of EOR projects is developed through a series of questions that applies to the industry’s transition from completion of conventional EOR toward unconventional EOR. Why Are We Afraid of EOR? The underperformance of EOR may be explained by the following reasons: Limitations in capturing the physics of the process for proper technical and economical assessment Risks involved in field pilots Securing the supply of the materials injected Difficulties involved in EOR design Risk resulting from economic uncertainties Why Are There Fewer EOR Projects Than Desired? True Drivers of EOR Applications, Technology, and Economics. Once the technical viability of an EOR project is proven, cost-effective applications can be achieved by the high-quality optimization efforts of engineers who can decide the appropriate optimization methods or at least the conditions under which a project can turn profitable. Insufficient Attention Given to Cost-Efficient EOR Methods. Use of air as the cheapest EOR agent has been investigated substantially for field-scale projects, but its applications are still limited. Recently, air injection was demonstrated to be safe under the low-temperature oxidation process and at atmospheric pressure/temperature conditions such as those of shallow heavy-oil reservoirs. Microbial injection also offers promising possibilities as a cost-effective approach. Fear of Most-Expensive Miscible Processes. The most-expensive EOR agents are miscible gases, or solvents, which are expected to yield the highest recovery under suitable conditions. Recyclability of the injected material from the miscible injection is an attractive part of the EOR process; however, unrecovered injectant can also be a critically limiting factor. Exploiting gas as a byproduct from other operations, however, allowed sustainable EOR development in Alaska (Prudhoe Bay), the North Sea, and Canada (Zama).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".