多因素敏感性分析在低勘探程度区块价值评估中的应用与探讨

封永泰; 赵琳洁; 王保华; 李龙龙; 段铁军

doi:10.11781/sysydz2025020426

多因素敏感性分析在低勘探程度区块价值评估中的应用与探讨

doi: 10.11781/sysydz2025020426

1.
中国石化油田勘探开发事业部，北京 100728
2.
中国石化石油勘探开发研究院无锡石油地质研究所，江苏无锡 214126

基金项目:

中国石化科技部项目“油气勘查区块分类分级评价与矿权策略研究” P24235

中国石化“十四五”资源评价方法与数据库建设 P23229

详细信息

作者简介:
封永泰(1973—)，男，高级工程师，从事战略选区及矿权评价优选。E-mail: fengyt@sinopec.com

通讯作者:
赵琳洁(1992—)，女，硕士，助理研究员，从事区块价值评估研究。E-mail: zhaolj.syky@sinopec.com

中图分类号: TE132.1
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- 被引次数: 0
出版历程
- 收稿日期: 2024-10-28
- 修回日期: 2025-02-12
- 刊出日期: 2025-03-28

Application and discussion of multi-factor sensitivity analysis in value assessment of areas with low exploration level

1.
Oilfield Exploration and Development Division, SINOPEC, Beijing 100728, China
2.
Wuxi Research Institute of Petroleum Geology, SINOPEC, Wuxi, Jiangsu 214126, China

摘要

摘要: 随着矿业权竞争性政策在全国范围内的落地实施，油气勘查开采市场全面放开，但出让区块大多位于低勘探程度区。低勘探程度区存在资料缺乏、认识薄弱等现实因素，增大了各大油公司在“扩矿权”进程中所面临的区块评价、优选及竞标获取难度。在此背景下，对于各大油公司而言，如何在勘探认知有限的情况下，快速且有效地评估低勘探程度区块的价值，对各油公司在区块获取中掌握主动权显得尤为重要。考虑油气勘探中地质的复杂性、勘探的局限性、开发的不确定性以及经济环境的波动性等风险因素，以国际通行的预期货币价值法为基础，借助多参数敏感性分析手段，明确影响区块价值评估的关键敏感因素，建立单位地质资源量预期收益与主控因素之间的多元回归方程，实现对低勘探程度区块价值的有效评估，并在塔里木盆地北部某常规断溶体油藏区块开展应用。该方法充分考虑油气勘探中的地质、勘探、开发、经济等风险因素，通过多参数敏感性分析，定量确定主控因素，并建立类比模型，为类比法参数体系确定提供科学依据，进而客观、快速地评估低勘探程度区的预期收益，也为出让区块的竞标决策提供有力的支持。
- 低勘探程度区 /
- 敏感性分析 /
- 预期收益 /
- 价值评估 /
- 类比模型 /
- 经济评价场景
Abstract: With the full implementation of competitive mining rights policies in China, the oil and gas exploration and production market has been fully open, but most of the exploration blocks currently being offered are located in areas with low exploration level. Due to the lack of data and limited understanding in these areas, major oil companies face challenges in evaluating, selecting, and bidding during the process of obtaining mining rights. Against this background, it is particularly crucial for those companies to quickly and effectively assess the value of areas with low exploration level to gain a strategic advantage in block acquisition. This study takes into account the risks in oil and gas exploration, including geological complexity, exploration limitations, development uncertainties, and economic fluctuations. Based on the internationally recognized expected monetary value (EMV) method, the study employed multi-factor sensitivity analysis to identify the key sensitive factors affecting block value assessment. A multiple regression equation was established between the expected revenue per unit of geological resources and the main controlling factors, thereby enabling an effective assessment of the value of areas with low exploration level. The method has been applied to a conventional karstic-fault oil reservoir block in the northern Tarim Basin. This method comprehensively considers geological, exploration, development, and economic risks in oil and gas exploration. Through quantitative multi-factor sensitivity analysis, it determines the key controlling factors and establishes an analogy model. It provides a scientific basis for defining the parameter systems in analogy methods, enabling objective and rapid assessment of expected revenue in areas with low exploration level. It offers strong support for bidding decisions on blocks on offer.
- areas with low exploration level /
- sensitivity analysis /
- expected revenue /
- value assessment /
- analogy model /
- economic evaluation scenario
注释:

利益冲突声明/Conflict of Interests

作者赵琳洁、王保华、李龙龙、段铁军是本刊主办单位员工，均未参与本文的同行评审、编审流程或决策。

Authors ZHAO Linjie, WANG Baohua, LI Longlong, and DUAN Tiejun are employees of the sponsor of this journal. None of them took part in peer review/editorial procedures or decision-making of this article.

作者贡献/Authors’Contributions

封永泰、赵琳洁、王保华完成方法建立；赵琳洁完成应用操作与论文写作；李龙龙、段铁军参与论文调研和修改。所有作者均阅读并同意最终稿件的提交。

The study was designed by FENG Yongtai, ZHAO Linjie, and WANG Baohua. The application operation was completed by ZHAO Linjie. The manuscript was drafted and revised by LI Longlong and DUAN Tiejun. All authors have read the final version of the paper and consented to its submission.

HTML全文

注释:

利益冲突声明/Conflict of Interests

作者赵琳洁、王保华、李龙龙、段铁军是本刊主办单位员工，均未参与本文的同行评审、编审流程或决策。

Authors ZHAO Linjie, WANG Baohua, LI Longlong, and DUAN Tiejun are employees of the sponsor of this journal. None of them took part in peer review/editorial procedures or decision-making of this article.

作者贡献/Authors’Contributions

封永泰、赵琳洁、王保华完成方法建立；赵琳洁完成应用操作与论文写作；李龙龙、段铁军参与论文调研和修改。所有作者均阅读并同意最终稿件的提交。

The study was designed by FENG Yongtai, ZHAO Linjie, and WANG Baohua. The application operation was completed by ZHAO Linjie. The manuscript was drafted and revised by LI Longlong and DUAN Tiejun. All authors have read the final version of the paper and consented to its submission.

图 1 不同资源类型下的单位EMV敏感性分析

图中a、b、c代表不同敏感性参数对单位EMV数据的影响变化曲线。

Figure 1. Sensitivity analysis of EMV per unit under different oil and gas resource types

下载: 全尺寸图片幻灯片

图 2 不同场景下主控因素取值及单位EMV数据集

x₁₁为场景1下的主控因素1的取值，x₁₂为场景1下的主控因素2的取值，……，x_nn为场景n下的主控因素n的取值。

Figure 2. Main controlling factor values and EMV per unit data sets under different scenarios

下载: 全尺寸图片幻灯片

图 3 A区块常规油主控因素对单位EMV值的变化率敏感性分析

Figure 3. Sensitivity analysis of conventional oil main controlling factors on variation rates of EMV per unit in block A

下载: 全尺寸图片幻灯片

表 1 不同资源类型单位EMV值的评估影响因素

Table 1. Assessment factors affecting EMV per unit for different types of oil and gas resources

地质参数	勘探参数	开发参数	经济参数
资源丰度/(10⁴ t/km²)	二维覆盖率/%	可采系数/%	税金/万元
有利面积/km²	二维单价/(万元/km)	动用率/%	政策补贴/元
含气量/(m³/t)	三维覆盖率/%	采收率/%	汇率
地质成功概率/%	三维单价/(万元/km²)	单井投资(含压裂)/(万元/口)	吨桶比/(吨/桶)
	探井覆盖率/%	地面费用/(万元/口)
	探井费用/(万元/口)	海工费用/(万元/口)
		操作成本/(元/吨)或(元/千立方米)

下载: 导出CSV

表 2 A区块常规油不同场景下主控因素取值及单位EMV值数据集

Table 2. Main controlling factor values and EMV per unit data sets of conventional oil in block A under different scenarios

主控因素	地质成功概率/%	资源丰度/(10⁴ t/km²)	可采系数/%	采收率/%	单井投资/(万元/口)	地面费用/(万元/口)	操作成本/(元/吨)	单位 EMV值/(元/吨)
影响程度	30	38	35	63	-33	-3	-14	单位 EMV值/(元/吨)
场景1	100	127.28	12.25	6.77	6 965.00	661.00	535.00	26.53
场景2	80	127.28	12.25	6.77	6 965.00	661.00	535.00	20.59
场景3	100	114.55	12.25	6.77	6 965.00	661.00	535.00	22.45
场景4	100	127.28	13.47	6.77	6 965.00	661.00	535.00	29.87
场景5	100	127.28	12.25	5.42	6 965.00	661.00	535.00	13.88
场景6	100	127.28	12.25	6.77	7 661.50	661.00	535.00	23.18
……	……	……	……	……	……	……	……	……
场景n	100	127.28	12.25	6.77	6 965.00	661.00	428.00	29.35
注：场景1为原始参数；场景2为地质成功概率降低20%；场景3为资源丰度降低10%；场景4为可采系数增加10%；场景5为采收率减小20%；场景6为开发井费用增加10%；……；场景n为操作成本降低20%；影响程度为图 3中主控因素的单位EMV值变化率，单位为无量纲。

下载: 导出CSV

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