Uncertainty and Sensitivity Analysis of OIIP Estimation Based on Geological Model

Zeng Xing; Song Heng; He Congge; Bo Bing; Zhao Liangdong; Liu Yunyang; Cai Rui

doi:doi:10.11648/j.sd.20210906.26

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Uncertainty and Sensitivity Analysis of OIIP Estimation Based on Geological Model

Zeng Xing, Song Heng, He Congge, Bo Bing, Zhao Liangdong, Liu Yunyang, Cai Rui

Published in Science Discovery (Volume 9, Issue 6)

Received: 3 November 2021 Accepted: 23 November 2021 Published: 24 November 2021

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Abstract

Due to the limitations of understanding of geological parameters of oil and gas reservoirs, there is an uncertainty in the results of OOIP calculation. Study the oilfield OOIP distribution probability distribution and the sensitivity of relevant key parameters are quite meaningful to make development plan. In this paper, in the process of volumetric calculation based on the oilfield geological model, according to the actual geological conditions of the oilfield, the geological parameters with uncertainty are selected as variables for the uncertainty analysis, the variable types and distribution parameters are reasonably defined. The Monte Carlo sampling method with inplantation of Latin Hypercube principle principle obtains the probability distribution of the geological reserves of the oil field, and evaluates the sensitivity of the various variables to the OOIP. The results provide the recommended P50 geological reserves. This paper shows that analyzing the key influencing parameters of OOIP calculation and setting the variables reasonably, the Monte Carlo-Latin Hypercube sampling method can provide a representative probability distribution with limited sampling count, and can effectively evaluate OOIP uncertainty. Thus, give the recommended OOIP and relivant geological models for numerical simulation research.

Published in	Science Discovery (Volume 9, Issue 6)
DOI	10.11648/j.sd.20210906.26
Page(s)	371-374
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Geological model, OOIP calculation, Uncertainty analysis, Sensitivity analysis, Monte-Carlo sampling

References

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[3]	姜汉桥，谷建伟，陈民锋，等．时变油藏地质模型下剩余油分布的数值模拟研究［J］．石油勘探与开发，2005，32(2):91-93。
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[5]	周丽清,熊琦华,吴胜和.随机建模中相模型的优选验证原则[J].石油勘探与开发,2001,28(1)：68-71。
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[9]	王才经,乐友喜.Monte Carlo方法预测区带资源量［J］.石油大学学报:自然科学版2000，24（1）:104-105。
[10]	张明,张建云,金菊良等.蒙特卡罗法求解湖库未确知水环境容量［J］.水电能源科学2008，26（6）:33-34。
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[13]	孙润平，王亚会，闫正和等.礁滩灰岩油田基于地质模型的储量不确定性分析-以珠江口A油田为例［J］.广东石油化工学院学报,2020,30(1):15-18。

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APA Style

Zeng Xing, Song Heng, He Congge, Bo Bing, Zhao Liangdong, et al. (2021). Uncertainty and Sensitivity Analysis of OIIP Estimation Based on Geological Model. Science Discovery, 9(6), 371-374. https://doi.org/10.11648/j.sd.20210906.26

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ACS Style

Zeng Xing; Song Heng; He Congge; Bo Bing; Zhao Liangdong, et al. Uncertainty and Sensitivity Analysis of OIIP Estimation Based on Geological Model. Sci. Discov. 2021, 9(6), 371-374. doi: 10.11648/j.sd.20210906.26

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AMA Style

Zeng Xing, Song Heng, He Congge, Bo Bing, Zhao Liangdong, et al. Uncertainty and Sensitivity Analysis of OIIP Estimation Based on Geological Model. Sci Discov. 2021;9(6):371-374. doi: 10.11648/j.sd.20210906.26

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@article{10.11648/j.sd.20210906.26,
  author = {Zeng Xing and Song Heng and He Congge and Bo Bing and Zhao Liangdong and Liu Yunyang and Cai Rui},
  title = {Uncertainty and Sensitivity Analysis of OIIP Estimation Based on Geological Model},
  journal = {Science Discovery},
  volume = {9},
  number = {6},
  pages = {371-374},
  doi = {10.11648/j.sd.20210906.26},
  url = {https://doi.org/10.11648/j.sd.20210906.26},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20210906.26},
  abstract = {Due to the limitations of understanding of geological parameters of oil and gas reservoirs, there is an uncertainty in the results of OOIP calculation. Study the oilfield OOIP distribution probability distribution and the sensitivity of relevant key parameters are quite meaningful to make development plan. In this paper, in the process of volumetric calculation based on the oilfield geological model, according to the actual geological conditions of the oilfield, the geological parameters with uncertainty are selected as variables for the uncertainty analysis, the variable types and distribution parameters are reasonably defined. The Monte Carlo sampling method with inplantation of Latin Hypercube principle principle obtains the probability distribution of the geological reserves of the oil field, and evaluates the sensitivity of the various variables to the OOIP. The results provide the recommended P50 geological reserves. This paper shows that analyzing the key influencing parameters of OOIP calculation and setting the variables reasonably, the Monte Carlo-Latin Hypercube sampling method can provide a representative probability distribution with limited sampling count, and can effectively evaluate OOIP uncertainty. Thus, give the recommended OOIP and relivant geological models for numerical simulation research.},
 year = {2021}
}

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TY  - JOUR
T1  - Uncertainty and Sensitivity Analysis of OIIP Estimation Based on Geological Model
AU  - Zeng Xing
AU  - Song Heng
AU  - He Congge
AU  - Bo Bing
AU  - Zhao Liangdong
AU  - Liu Yunyang
AU  - Cai Rui
Y1  - 2021/11/24
PY  - 2021
N1  - https://doi.org/10.11648/j.sd.20210906.26
DO  - 10.11648/j.sd.20210906.26
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 371
EP  - 374
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20210906.26
AB  - Due to the limitations of understanding of geological parameters of oil and gas reservoirs, there is an uncertainty in the results of OOIP calculation. Study the oilfield OOIP distribution probability distribution and the sensitivity of relevant key parameters are quite meaningful to make development plan. In this paper, in the process of volumetric calculation based on the oilfield geological model, according to the actual geological conditions of the oilfield, the geological parameters with uncertainty are selected as variables for the uncertainty analysis, the variable types and distribution parameters are reasonably defined. The Monte Carlo sampling method with inplantation of Latin Hypercube principle principle obtains the probability distribution of the geological reserves of the oil field, and evaluates the sensitivity of the various variables to the OOIP. The results provide the recommended P50 geological reserves. This paper shows that analyzing the key influencing parameters of OOIP calculation and setting the variables reasonably, the Monte Carlo-Latin Hypercube sampling method can provide a representative probability distribution with limited sampling count, and can effectively evaluate OOIP uncertainty. Thus, give the recommended OOIP and relivant geological models for numerical simulation research.
VL  - 9
IS  - 6
ER  -

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Author Information

Zeng Xing

Research Institute of Petroleum Exploration & Development, Beijing, China
Song Heng

Research Institute of Petroleum Exploration & Development, Beijing, China
He Congge

Research Institute of Petroleum Exploration & Development, Beijing, China
Bo Bing

Research Institute of Petroleum Exploration & Development, Beijing, China
Zhao Liangdong

Research Institute of Petroleum Exploration & Development, Beijing, China
Liu Yunyang

Research Institute of Petroleum Exploration & Development, Beijing, China
Cai Rui

Research Institute of Petroleum Exploration & Development, Beijing, China

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Plain Text BibTeX RIS

APA Style

Zeng Xing, Song Heng, He Congge, Bo Bing, Zhao Liangdong, et al. (2021). Uncertainty and Sensitivity Analysis of OIIP Estimation Based on Geological Model. Science Discovery, 9(6), 371-374. https://doi.org/10.11648/j.sd.20210906.26

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ACS Style

Zeng Xing; Song Heng; He Congge; Bo Bing; Zhao Liangdong, et al. Uncertainty and Sensitivity Analysis of OIIP Estimation Based on Geological Model. Sci. Discov. 2021, 9(6), 371-374. doi: 10.11648/j.sd.20210906.26

Copy | Download

AMA Style

Zeng Xing, Song Heng, He Congge, Bo Bing, Zhao Liangdong, et al. Uncertainty and Sensitivity Analysis of OIIP Estimation Based on Geological Model. Sci Discov. 2021;9(6):371-374. doi: 10.11648/j.sd.20210906.26

Copy | Download

@article{10.11648/j.sd.20210906.26,
  author = {Zeng Xing and Song Heng and He Congge and Bo Bing and Zhao Liangdong and Liu Yunyang and Cai Rui},
  title = {Uncertainty and Sensitivity Analysis of OIIP Estimation Based on Geological Model},
  journal = {Science Discovery},
  volume = {9},
  number = {6},
  pages = {371-374},
  doi = {10.11648/j.sd.20210906.26},
  url = {https://doi.org/10.11648/j.sd.20210906.26},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20210906.26},
  abstract = {Due to the limitations of understanding of geological parameters of oil and gas reservoirs, there is an uncertainty in the results of OOIP calculation. Study the oilfield OOIP distribution probability distribution and the sensitivity of relevant key parameters are quite meaningful to make development plan. In this paper, in the process of volumetric calculation based on the oilfield geological model, according to the actual geological conditions of the oilfield, the geological parameters with uncertainty are selected as variables for the uncertainty analysis, the variable types and distribution parameters are reasonably defined. The Monte Carlo sampling method with inplantation of Latin Hypercube principle principle obtains the probability distribution of the geological reserves of the oil field, and evaluates the sensitivity of the various variables to the OOIP. The results provide the recommended P50 geological reserves. This paper shows that analyzing the key influencing parameters of OOIP calculation and setting the variables reasonably, the Monte Carlo-Latin Hypercube sampling method can provide a representative probability distribution with limited sampling count, and can effectively evaluate OOIP uncertainty. Thus, give the recommended OOIP and relivant geological models for numerical simulation research.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Uncertainty and Sensitivity Analysis of OIIP Estimation Based on Geological Model
AU  - Zeng Xing
AU  - Song Heng
AU  - He Congge
AU  - Bo Bing
AU  - Zhao Liangdong
AU  - Liu Yunyang
AU  - Cai Rui
Y1  - 2021/11/24
PY  - 2021
N1  - https://doi.org/10.11648/j.sd.20210906.26
DO  - 10.11648/j.sd.20210906.26
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 371
EP  - 374
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20210906.26
AB  - Due to the limitations of understanding of geological parameters of oil and gas reservoirs, there is an uncertainty in the results of OOIP calculation. Study the oilfield OOIP distribution probability distribution and the sensitivity of relevant key parameters are quite meaningful to make development plan. In this paper, in the process of volumetric calculation based on the oilfield geological model, according to the actual geological conditions of the oilfield, the geological parameters with uncertainty are selected as variables for the uncertainty analysis, the variable types and distribution parameters are reasonably defined. The Monte Carlo sampling method with inplantation of Latin Hypercube principle principle obtains the probability distribution of the geological reserves of the oil field, and evaluates the sensitivity of the various variables to the OOIP. The results provide the recommended P50 geological reserves. This paper shows that analyzing the key influencing parameters of OOIP calculation and setting the variables reasonably, the Monte Carlo-Latin Hypercube sampling method can provide a representative probability distribution with limited sampling count, and can effectively evaluate OOIP uncertainty. Thus, give the recommended OOIP and relivant geological models for numerical simulation research.
VL  - 9
IS  - 6
ER  -

Copy | Download