Bibliographic record
Abstract
While supply disruptions and price volatility are not new to the oil and gas industry, the dramatic drop in oil prices because of the COVID-19 pandemic severely affected global enhanced oil recovery (EOR) projects. To add fuel to the fire, the current geopolitical scenario leading to a surge in commodity prices and supply chain disruptions exacerbated this situation by negating any chances of recovery, at least in the short term. Concurrently, the rise in energy consumption and increasing demand for energy security worldwide warrants the need to increase global hydrocarbon production. However, rising environmental concerns with regard to greenhouse gas emissions, coupled with a decrease in the availability of easy-to-produce hydrocarbon resources, mandate the need for a gradual shift toward optimized recovery strategy in a sustainable and cost-efficient manner. As a result, current circumstances dictate focusing on low-cost and low-carbon EOR barrels to secure energy supply in the shorter term while continuing to develop innovative technologies and strategies for enhancing production in the longer term. Energy consumption for mature waterfloods increases sharply at higher water cuts primarily because of the recirculation of water through the existing swept pathways. Lower volumetric sweep in reservoirs leads to excessive water production and less oil recovery. To curb the menace of excess water production, conventional EOR techniques such as polymerflooding or in-depth conformance-control techniques are mostly implemented. Both methods have demonstrated cost savings because of reduced watercuts and reductions in CO2 generation by lowering reinjection of produced water. Additional costs of injectants, transport, and topside facilities, however, contribute to higher project costs. Early optimization measures for existing waterfloods using machine-learning (ML) approaches could enable optimal reservoir management and production by delineating reservoir heterogeneities more efficiently and predicting more-effective interwell connectivity. Furthermore, ML could contribute directly to the optimization and surveillance of EOR applications, which would subsequently affect the performance and the cost of such projects, thereby supporting the achievement of low-cost and low-carbon EOR barrels. Recommended additional reading at OnePetro: www.onepetro.org. SPE 214268 A Novel Approach To Combine Models To Evaluate Interwell Connectivity in a Waterflooded Reservoir With Limited Injection History by Yanfidra Djanuar, Dragon Oil, et al. SPE 210657 A Laboratory-to-Field Approach and Evaluation of Low-Salinity Waterflooding Process for High-Temperature/High-Pressure Carbonate Reservoirs by Hemanta Kumar Sarma, University of Calgary, et al. IPTC 22733 Like Cures Like Microbial Enhanced Oil Recovery in Biodegraded Crude by Thanapong Ketmalee, PTTEP, 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.544 | 0.470 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".