A statistical method for determining the hydrocarbon accumulation coefficient and its application to assessment of hydrocarbon resources in Huanghekou Sag, Bohai Bay Basin, Eastern China
Bibliographic record
Abstract
Research Article| September 01, 2012 A statistical method for determining the hydrocarbon accumulation coefficient and its application to assessment of hydrocarbon resources in Huanghekou Sag, Bohai Bay Basin, Eastern China 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 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 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 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 Jiang Fujie; Jiang Fujie 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 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 Zhou Xiaohui; Zhou Xiaohui 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 Bulletin of Canadian Petroleum Geology (2012) 60 (3): 200–208. https://doi.org/10.2113/gscpgbull.60.3.200 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 Pang Xiongqi, Jiang Zhenxue, Li Zhuo, Wu Li, Jiang Fujie, Zhou Jingjing, Zhou Xiaohui, Li Xiaolei; A statistical method for determining the hydrocarbon accumulation coefficient and its application to assessment of hydrocarbon resources in Huanghekou Sag, Bohai Bay Basin, Eastern China. Bulletin of Canadian Petroleum Geology 2012;; 60 (3): 200–208. doi: https://doi.org/10.2113/gscpgbull.60.3.200 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 Hydrocarbon Accumulation Coefficient (HAC) is an important parameter in the genetic method of hydrocarbon resource assessment. This parameter is usually derived from a simple geological analogy or from expert judgment based on experience, which can lead to large uncertainties in hydrocarbon resource assessment results. In this article, we introduce a new method for determining the HAC, based on Single Factor Correlation Analysis and Multivariate Regression Analysis, using data collected from basins with a high degree of exploration maturity and large amounts of proven reserves. Firstly, the principal factors that control the hydrocarbon accumulation coefficient, such as the time ratio of peak expulsion and caprock formation, the ratio of maximum fault throw and caprock thickness, and the rate of fault displacement, are determined by Single Factor Correlation Analyses. A quantitative model can then be established between HAC and the principal controlling factors using the Multivariate Regression Analysis. The proposed method was applied to the Huanghekou Sag in Bohai Bay Basin, eastern China. The results show that the HAC is 29% in the Huanghekou Sag, with a total resource of 1435 × 106 m3 oil equivalent, which is consistent with geological observations of this basin and suggests that the proposed method improves the applicability of the generic method in resource assessment. Abstract Le coefficient d’accumulation d’hydrocarbures (CAH) est un important paramètre de la méthode génétique pour évaluer les ressources en hydrocarbures. Ce paramètre dérive généralement d’une simple analogie géologique ou d’une appréciation experte fondée sur l’expérience, ce qui peut mener à des incertitudes notables sur les résultats d’évaluation des ressources en hydrocarbures. Dans le présent article, nous introduisons une nouvelle méthode pour déterminer le CAH fondée sur l’analyse corrélative à facteur unique et l’analyse de régression multivariée. Pour ce faire, nous utilisons les données recueillies de bassins ayant un degré élevé de maturité d’exploration et de grandes quantités de réserves prouvées. En premier lieu, les principaux facteurs qui régissent le coefficient d’accumulation d’hydrocarbures, tels que la valeur temporelle de l’expulsion maximale des hydrocarbures, la formation de la roche-couverture, le coefficient du rejet de la faille et de l’épaisseur de la roche-couverture maximums, ainsi que le taux de rejet, sont déterminés par les analyses corrélatives à facteur unique. En second lieu, on peut alors établir un modèle quantitatif entre le CAH et les principaux facteurs ci-dessus, en utilisant l’analyse de régression multivariée. Nous avons appliqué la méthode proposée dans l’affaissement de Huanghekou du bassin de Bohaï, en Chine orientale. Les résultats montrent que le CAH est de 29 % dans l’affaissement de Huanghekou avec des ressources totales de 1435 × 106m3 d’équivalent pétrole, ce qui correspond aux observations géologiques de ce bassin, et nous suggère que la méthode proposée améliore l’applicabilité de la méthode générique pour estimer les ressources. 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".