An improved multi-parameter-constrained Pareto model for hydrocarbon resource assessment and its application in Bozhong Sag, Eastern China
Bibliographic record
Abstract
Research Article| September 01, 2012 An improved multi-parameter-constrained Pareto model for hydrocarbon resource assessment and its application in Bozhong Sag, Eastern China Jiang Zhenxue; Jiang Zhenxue State Key Laboratory of Petroleum Resource and Prospecting China University of Petroleum Beijing, China 102249Institute of Unconventional Natural Gas China University of Petroleum Beijing, China 102249 Search for other works by this author on: GSW Google Scholar Pang Xiongqi; Pang Xiongqi State Key Laboratory of Petroleum Resource and Prospecting China University of Petroleum Beijing, China 102249College of Geosciences China University of Petroleum Beijing, China 102249 Search for other works by this author on: GSW Google Scholar Li Zhuo; Li Zhuo State Key Laboratory of Petroleum Resource and Prospecting China University of Petroleum Beijing, China 102249Institute of Unconventional Natural Gas China University of Petroleum Beijing, China 102249 Search for other works by this author on: GSW Google Scholar Zhou Jingjing; Zhou Jingjing State Key Laboratory of Petroleum Resource and Prospecting China University of Petroleum Beijing, China 102249College of Geosciences China University of Petroleum Beijing, China 102249 Search for other works by this author on: GSW Google Scholar Wu Li; Wu Li State Key Laboratory of Petroleum Resource and Prospecting China University of Petroleum Beijing, China 102249College of Geosciences China University of Petroleum Beijing, China 102249 Search for other works by this author on: GSW Google Scholar Li Xiaolei; Li Xiaolei State Key Laboratory of Petroleum Resource and Prospecting China University of Petroleum Beijing, China 102249College of Geosciences China University of Petroleum Beijing, China 102249 Search for other works by this author on: GSW Google Scholar Zhang Yingying Zhang Yingying State Key Laboratory of Petroleum Resource and Prospecting China University of Petroleum Beijing, China 102249College of Geosciences China University of Petroleum Beijing, China 102249 Search for other works by this author on: GSW Google Scholar Bulletin of Canadian Petroleum Geology (2012) 60 (3): 209–217. https://doi.org/10.2113/gscpgbull.60.3.209 Article history received: 15 Mar 2011 accepted: 25 Jul 2012 first online: 12 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Jiang Zhenxue, Pang Xiongqi, Li Zhuo, Zhou Jingjing, Wu Li, Li Xiaolei, Zhang Yingying; An improved multi-parameter-constrained Pareto model for hydrocarbon resource assessment and its application in Bozhong Sag, Eastern China. Bulletin of Canadian Petroleum Geology 2012;; 60 (3): 209–217. doi: https://doi.org/10.2113/gscpgbull.60.3.209 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of Canadian Petroleum Geology Search Advanced Search Abstract AbstractThe field size distribution method, such as the Pareto model, is commonly used in hydrocarbon resource assessment in China. It has the advantage of predicting not only the total resources but also the individual field sizes. However, the method has a large uncertainty range in the model parameter estimates. A multi-parameter-constrained Pareto model is proposed to improve parameter estimations. The constraints introduced include: a) total resource estimate from other methods (e.g. genetic method); b) the largest size of undiscovered fields from an empirical relationship between play resources and the largest field sizes in well explored basins; and c) the number of undiscovered fields constrained by the number of untested traps (including subtle traps). By introducing the three constraints, the method provides realistic results with less uncertainty and is consistent with petroleum system models derived from hydrocarbon exploration. The application of this proposed method to Bozhong Sag in the Bohai Bay Basin suggests a total resource of 4130 × 106 m3 in 48 fields, of which 1165×106 m3 are in 27 discovered fields and 2965 × 106 m3 are in the remaining 21 undiscovered fields. The largest discovered field is 369 × 106 m3 and the largest remaining field expected is 853 × 106 m3. The results of the application appear to be consistent with the current exploration status and geological understanding of the study area. Abstract La méthode de répartition de la dimension des champs de ressources, notamment le modèle de Pareto est couramment utilisée en Chine pour évaluer l’étendue des hydrocarbures. L’utilisation de cette méthode permet de prévoir non seulement les ressources totales, mais également la dimension des ressources des champs individuels. Toutefois, la méthode présente un degré important d’incertitude à l’égard des estimations paramétriques du modèle. Dans le même ordre d’idées, nous proposons un modèle de Pareto à paramètres multiples restreints pour améliorer les estimations paramétriques. Les contraintes que nous introduisons incluent : a) une estimation des ressources totales venant d’autres méthodes (p. ex. : la méthode génétique); b) la plus grande dimension des champs non découverts issue d’une relation empirique entre les zones de ressources et les champs les plus étendus dans les bassins largement explorés; et c) le nombre de champs non découverts contraints par le nombre de pièges inéprouvés (y compris les pièges subtils). À l’aide des trois contraintes, la méthode fournit des résultats réalistes avec moins d’incertitude et elle rejoint les modèles de système pétrolier provenant de l’exploration des hydrocarbures. L’application de la méthode proposée dans la dépression de Bozhong, dans le bassin de la baie de Bohaï, suggère des ressources totales de 4130 × 106 m3 dans 48 champs dont 1165 × 106 m3 sont logées dans 27 champs découverts et 2965 × 106 m3 se trouvent dans les 21 champs non découverts restants. Le plus grand champ découvert est égal à 369 × 106 m3 et le plus grand champ restant prévu représente 853 × 106 m3. Les résultats de l’application semblent rejoindre les conditions actuelles de l’exploration et notre compréhension de la géologie de la zone étudiée. Michel Ory You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".