Technology Trends of Oil-sands Plant Modularization using Patent Analysis
Bibliographic record
Abstract
원유 생산의 정점이 예상되기 때문에 비전통 자원과 대체에너지에 대한 연구가 많이 진행되고 있다. 본 연구에서는 비전통 자원 중 오일샌드에 한정하였고, 동토 지역은 건설 가능한 기간이 제한적이고 현지건설인력 확보가 쉽지 않으며, 공사기간을 단축시킬 수 있는 오일샌드 플랜트 모듈화에 대한 관심이 크기 때문에 특허를 통한 기술동향을 분석해 보았다. 특허 분석은 1994년-2015년 데이터를 이용하였고 한국, 미국, 일본, 유럽 및 캐나다 특허를 분석 대상으로 하였다. 기술분류체계로 노천채굴 기술, 지하회수법 기술, 분리/개질/환원물 기술, 모듈설계/패키징 기술, 모듈운송기술 및 소재/유지관리 기술 분야로 나누었고 이를 국가별 landscape, 세부기술 동향분석, 주요 경쟁사 심층분석을 하였다. 특허 분석결과, 오일샌드 플랜트 기술은 미국 및 캐나다에 89%의 특허가 집중 되어 출원되고 있었다. 주로 경쟁사로는 Shell, Suncor 그리고 Exxon-mobil로 각각의 핵심특허를 분석하였다. 오일샌드는 타유전개발과 달리 장기간 안정적 생산량 유지가 가능한 사업적 특성을 가지므로, 장기적 관점에서 특허를 확보하여 오일샌드 사업의 경쟁력을 확보하는 것이 중요할 것으로 분석된다. Non-conventional resource and alternative energy were researched for predicting oil peak. In this study, one of many non-conventional resources, specifically oil-sands, was investigated due to the increasing interest of oil-sands plant modularization in permaforst areas for reducing the construction periods through modular transportation while limiting local construction workers. Hence, tehcnological trends were analyzed for oil-sand plant modularization. Data used were between 1994 and 2015 for patent analysis while targets included Korea, US, Japan, Europe and Canada. Technology classification system consisted of mining, steam assisted gravity drainage(SAGD), separation/upgrading/tailors ponds, module design/packaging, module transportation and material/maintenance. Result of patent analysis, patent application accounts 89% in US and Canada. The main competitive companies were Shell, Suncor and Exxon-mobil. Unlike other oil developments, oil-sands have a long-term stable production characteristic, hence, it is important to ensure the competitiveness of oil-sands for obtaining a patent in the long run.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".