直觉模糊MABAC法在低渗碳酸盐岩气藏有利区优选中的应用

doi:10.13809/j.cnki.cn32-1825/te.2024.04.007

Abstract

Abstract:

The geological structure and reservoir characteristics of low-permeability carbonate gas reservoirs are complex, and the high uncertainty in data acquisition and interpretation makes it challenging to identify optimal development areas. To address this, the study introduces an intuitionistic fuzzy Multi-Attributive Border Approximation area Comparison（MABAC） model, combined with the Best Worst Method（BWM） for weighting, to select the most suitable areas for developing these reservoirs. The approach begins by establishing a comprehensive evaluation index system that considers geological characteristics, hydrocarbon generation capacity, and gas storage capability of the low-permeability carbonate formations. This system not only integrates geological data but also considers the construction conditions relevant to these reservoirs. Using the BWM, weights for each key evaluation indicator are determined, taking into account the multifactorial impacts on various oil and gas fields and establishing the hierarchical relationships between these factors. An improved intuitionistic fuzzy multi-attribute decision-making model is then developed. This model uses intuitionistic fuzzy data instead of precise information, enhancing the selection process for favorable areas. The application of this model on ten candidate blocks indicated that Block C is the most favorable, outperforming others in terms of remaining dynamic reserves, effective reservoir thickness, and other crucial indicators. The blocks were ranked as follows based on the overall distance value: Block C > Block I > Block H > Block A > Block D > Block G > Block J > Block E > Block F > Block B. The effectiveness and rationality of the proposed intuitionistic fuzzy MABAC method were validated through comparisons with existing methods and analysis of numerical simulation results, confirming its utility in optimizing the development of low-permeability carbonate gas reservoirs.

Key words: low permeability carbonate rock, selection of favorable areas, evaluation index system, Best Worst Method（BWM）, intuitive fuzzy MABAC method

CLC Number:

TE155

Chao MIN,Yingjun LI,Xiaogang LI, et al. Application of intuitive fuzzy MABAC method in optimizing favorable areas of low permeability carbonate gas reservoirs[J]. Petroleum Reservoir Evaluation and Development, 2024, 14(4): 577-585.

Figures/Tables 9

Fig. 1

Fig. 2

Table 1

Table 2

Intuitionistic fuzzy multi-attribute initial decision matrix"

备选方案	属性
备选方案	$C 1$	$C 2$	…	$C j$	…	$C n$
$A 1$	$α 11$	$α 12$	…	$α 1 j$	…	$α 1 n$
$A 2$	$α 21$	$α 22$	…	$α 2 j$	…	$α 2 n$
$?$	$?$	$?$	$?$	$?$	$?$	$?$
$A i$	$α i 1$	$α i 2$	…	$α i j$	…	$α i n$
$?$	$?$	$?$	$?$	$?$	$?$	$?$
$A m$	$α m 1$	$α m 2$	…	$α m j$	…	$α m n$

Table 2

Table 3

Table 4

Intuitive fuzzy evaluation matrix for optimal selection of favorable areas in carbonate rocks"

区块	储层厚度	剩余动态储量	含气饱和度	渗透率	孔隙度	泊松比	地层压力	温度	埋深
A	<0.373 7, 0.394 2>	<0.010 3, 0.898 6>	<0.536 9, 0.408 6>	<0.081 2, 0.715 0>	<0.141 2, 0.681 1>	<0.337 8, 0.581 1>	<0.938 2, 0.046 9>	<0.245 6, 0.702 1>	<0.578 3, 0.354 4>
B	<0.002 9, 0.958 0>	<0.000 4, 0.979 4>	<0.546 7, 0.399 1>	<0.000 2, 0.987 5>	<0.015 4, 0.934 4>	<0.454 1, 0.463 0>	<0.089 9, 0.873 2>	<0.227 7, 0.722 2>	<0.087 4, 0.882 0>
C	<0.960 4, 0.020 3>	<0.438 0, 0.338 2>	<0.738 0, 0.222 9>	<0.812 7,3 0.098 5>	<0.255 7, 0.541 1>	<0.074 9, 0.893 4>	<0.964 5, 0.026 8>	<0.280 3, 0.664 0>	<0.681 6, 0.261 6>
D	<0.470 5, 0.318 6>	<0.005 7, 0.924 8>	<0.542 8, 0.402 9>	<0.038 0, 0.805 0>	<0.265 1, 0.531 1>	<0.318 2, 0.601 9>	<0.734 8, 0.209 9>	<0.160 9, 0.799 0>	<0.485 5, 0.442 0>
E	<0.122 1, 0.659 7>	<0.007 4, 0.913 7>	<0.393 8, 0.550 3>	<0.000 0, 0.997 4>	<0.253 7, 0.543 3>	<0.434 7, 0.482 1>	<0.684 7, 0.252 4>	<0.179 1, 0.777 7>	<0.310 3, 0.620 4>
F	<0.551 7, 0.260 9>	<0.000 6, 0.976 2>	<0.312 0, 0.635 7>	<0.000 3, 0.982 0>	<0.214 0, 0.587 8>	<0.144 2, 0.803 1>	<0.124 9, 0.827 5>	<0.195 9, 0.758 2>	<0.116 0, 0.845 6>
G	<0.724 9, 0.150 7>	<0.004 0, 0.937 0>	<0.272 7, 0.678 0>	<0.003 8, 0.938 0>	<0.217 8, 0.583 3>	<0.474 6, 0.443 2>	<0.931 2, 0.052 3>	<0.273 8, 0.671 0>	<0.744 0, 0.207 6>
H	<0.335 3, 0.427 0>	<0.044 8, 0.788 3>	<0.668 2, 0.285 7>	<0.208 8, 0.534 0>	<0.222 6, 0.577 8>	<0.249 7, 0.677 5>	<0.996 6, 0.002 6>	<0.141 1, 0.822 4>	<0.753 4, 0.199 6>
I	<0.715 2, 0.156 5>	<0.076 1, 0.724 1>	<0.862 9, 0.114 3>	<0.003 6, 0.940 0>	<0.808 1, 0.111 1>	<0.174 9, 0.765 2>	<0.744 4, 0.201 9>	<0.341 4, 0.598 6>	<0.457 1, 0.469 6>
J	<0.244 2, 0.513 0>	<0.001 2, 0.965 4>	<0.578 9, 0.368 6>	<0.001 8, 0.957 9>	<0.222 6, 0.577 8>	<0.253 1, 0.673 6>	<0.966 4, 0.025 3>	<0.203 9, 0.749 1>	<0.794 2, 0.165 2>

Table 4

Table 5

Overall distance and weighted distance between each scheme and the approximate boundary region"

区块	储层厚度	剩余动态储量	含气饱和度	渗透率	孔隙度	泊松比	地层压力	温度	埋深	$S i$
A	0.005 3	0.001 4	0.000 1	0.001 6	-0.004 0	-0.005 8	0.032 2	-0.002 3	-0.003 3	0.025 2
B	-0.058 2	-0.028 8	0.000 5	-0.001 4	-0.012 1	-0.014 0	-0.061 9	-0.000 6	0.019 2	-0.157 4
C	0.081 7	0.095 1	0.007 6	0.016 7	0.001 5	0.016 6	0.041 0	-0.004 9	-0.007 6	0.247 7
D	0.012 4	-0.007 7	0.000 4	0.000 9	0.001 7	-0.004 0	0.003 2	0.003 3	0.001 1	0.011 2
E	-0.033 1	-0.002 4	-0.008 1	-0.001 6	0.001 4	-0.012 9	-0.004 5	0.002 3	0.006 5	-0.052 4
F	0.018 7	-0.027 7	-0.011 6	-0.001 3	0.000 4	0.009 2	-0.059 6	0.001 5	0.016 5	-0.054 0
G	0.034 6	-0.013 0	-0.013 1	-0.000 4	0.000 5	-0.015 2	0.030 4	-0.004 4	-0.009 8	0.009 7
H	0.002 3	0.013 2	0.004 8	0.003 6	0.000 6	0.002 1	0.071 2	0.004 5	-0.010 1	0.092 3
I	0.033 6	0.021 1	0.014 5	-0.000 4	0.019 3	0.006 8	0.004 1	-0.008 4	0.001 9	0.092 6
J	-0.013 8	-0.023 9	0.001 7	-0.000 9	0.000 6	0.001 9	0.041 9	0.001 1	-0.011 4	-0.002 8

Table 5

Table 6

Fig. 3

References 24

[1]	史亚红, 陈文安, 李纲, 等. 风西混积碳酸盐岩储层测井流体识别及定量评价[J]. 石油地质与工程, 2023, 37(5): 10-16.
	SHI Yahong, CHEN Wen'an, LI Gang, et al. Logging fluid identification and quantitative evaluation of mixed carbonate reservoir in Fengxi area[J]. Petroleum Geology & Engineering, 2023, 37(5): 10-16.
[2]	于吉旺, 庞雄奇, 郑定业, 等. 普光地区深层碳酸盐岩气藏主控因素及有利区预测[J]. 特种油气藏, 2022, 29(3): 28-35. doi: 10.3969/j.issn.1006-6535.2022.03.004
	YU Jiwang, PANG Xiongqi, ZHENG Dingye, et al. Main controlling factors and favorable area prediction of deep carbonate gas reservoirs in Puguang Area[J]. Special Oil and Gas Reservoirs, 2022, 29(3): 28-35. doi: 10.3969/j.issn.1006-6535.2022.03.004
[3]	祝渭平, 姚清洲, 李闯, 等. 塔中低凸起奥陶系深层鹰山组三、四段碳酸盐岩油气成藏要素及有利区带[J]. 地球科学, 2023, 48(2): 690-704.
	ZHU Weiping, YAO Qingzhou, LI Chuang, et al. Hydrocarbon accumulation factors and favorable exploration of carbonate reservoirs in the 3rd-4th members of Ordovician Yingshan Formation of Tazhong Low Salient[J]. Earth Science, 2023, 48(2): 690-704.
[4]	贾俊. 叙利亚Tishirine油田复杂碳酸盐岩储层有效性综合评价[J]. 油气藏评价与开发, 2020, 10(2): 30-36.
	JIA Jun. Comprehensive evaluation for effectiveness of complex carbonate reservoir: A case from Tishirine Oilfield in Syria, Syria[J]. Reservoir Evaluation and Development, 2020, 10(2): 30-36.
[5]	季天愚. 塔里木盆地中—下寒武统台缘带分布、沉积相及有利区的地震解释研究[D]. 西安: 西北大学, 2021.
	JI Tianyu, Seismic interpretation of the platform margin distribution, sedimentary facies and favorable areas of middle-lower Cambrian in Tarim Basin[D]. Xi'an: Northwest University, 2021.
[6]	陈思禹, 王颖晋, 郭俊阳, 等. 塔中东部碳酸盐岩储集层特征对比及勘探开发有利区预测[J]. 东北石油大学学报, 2021, 45(2): 10-19.
	CHEN Siyu, WANG Yingjin, GUO Junyang, et al. Comparison of carbonate reservoir characteristics in Eastern Tazhong Block of Tarim Basin and prediction of favorable development zone[J]. Journal of Northeast Petroleum University, 2021, 45(2): 10-19.
[7]	彭勇民, 龙胜祥, 何希鹏, 等. 彭水地区常压页岩气储层特征及有利区评价[J]. 油气藏评价与开发, 2020, 10(5): 12-19.
	PENG Yongmin, LONG Shengxiang, HE Xipeng, et al. Characteristics of normal-pressure shale gas reservoirs and evaluation of its favorable areas in Pengshui[J]. Reservoir Evaluation and Development, 2020, 10(5): 12-19.
[8]	徐腾. 川东地区下三叠统嘉陵江组二段储层特征及有利储层发育区预测[D]. 成都: 成都理工大学, 2021.
	XU Teng. Reservoir characteristics of the second member of Lower Triassic Jialingjiang formation and prediction of favorable reservoir development area in eastern Sichuan[D]. Chengdu: Chengdu University of Technology, 2021.
[9]	戴明刚, 雷海飞, 凌安航, 等. 太原地区奥陶系碳酸盐岩热储特征及勘探有利区[J]. 天然气工业, 2020, 40(4): 143-155.
	DAI Minggang, LEI Haifei, LING Anhang, et al. Thermal reservoir characteristics and favorable targets of Ordovician carbonate rocks in the Taiyuan area[J]. Natural Gas Industry, 2020, 40(4): 143-155.
[10]	张立安, 王少鹏, 张岚, 等. 通过地质建模剖析古潜山碳酸盐岩裂缝性储层地质特征[J]. 油气藏评价与开发, 2021, 11(5): 688-693.
	ZHANG Li'an, WANG Shaopeng, ZHANG Lan, et al. Analysis on geological characteristics of fractured carbonate reservoir in buried-hill by geological modeling[J]. Reservoir Evaluation and Development, 2021, 11(5): 688-693.
[11]	王志伟, 张凯, 武群虎, 等. 基于井震裂缝识别敏感性参数模型的碳酸盐岩储层裂缝预测方法[J]. 煤田地质与勘探, 2023, 51(6): 163-174.
	WANG Zhiwei, ZHANG Kai, WU Qunhu, et al. A method for predicting fractures in carbonate reservoirs based on fracture identification-sensitive log-seismic parameter model[J]. Coal Geology & Exploration, 2023, 51(6): 163-174.
[12]	袁倩. 川东北地区黄龙场构造飞仙关组储层特征及有利区优选[D]. 成都: 西南石油大学, 2019.
	YUAN Qian. Reservoir characteristics and favorable area selection of Feixianguan Formation in Huanglongchang structure in Northeast Sichuan[D]. Chengdu: Southwest Petroleum University, 2019.
[13]	孙斌, 张培先, 高全芳, 等. 川东南南川地区茅口组一段碳酸盐岩储层特征及富集模式[J]. 非常规油气, 2022, 9(3): 21-31.
	SUN Bin, ZHANG Peixian, GAO Quanfang, et al. Reservoir properties and accumulation mode of carbonate rocks in Mao1 Member of Nanchuan Area in southeast Sichuan[J]. Unconventional Oil & Gas, 2022, 9(3): 21-31.
[14]	周红, 朱芳冰. 基于优化组合权—灰关联—TOPSIS的页岩气有利区优选[J]. 地质科技情报, 2018, 37(5): 106-113.
	ZHOU Hong, ZHU Fangbing. Optimization of favorable areas of shale gas based on optimal combination weights and grey correlation and TOPSIS[J]. Geological Science and Technology Information, 2018, 37(5): 106-113.
[15]	马旭, 杨双春, 李秉繁, 等. 基于改进AHP-熵权法和TOPSIS的火山岩气藏开发方案优选[J]. 新疆石油地质, 2017, 38(3): 325-330.
	MA Xu, YANG Shuangchun, LI Bingfan, et al. Optimization of volcanic gas reservoir development program based on the improved AHP-entropy weight method and TOPSIS[J]. Xinjiang Petroleum Geology, 2017, 38(3): 325-330.
[16]	李婷. 基于随机模拟的油田开发规划二步决策优选方法[J]. 世界石油工业, 2022, 29(5): 39-45.
	LI Ting. Two-step decision optimization method of oilfield development planning based on stochastic simulation[J]. World Petroleum Industry, 2022, 29(5): 39-45.
[17]	杨兆中, 杨晨曦, 李小刚, 等. 基于灰色关联的逼近理想解排序法的煤层气井重复压裂选井——以沁水盆地柿庄南区块为例[J]. 科学技术与工程, 2020, 20(12): 4680-4686.
	YANG Zhaozhong, YANG Chenxi, LI Xiaogang, et al. Multiple fracturing well selection of coalbed methane wells based on technique for order preference by similarity to ideal solution method of gray correlation: Taking the case of Qinshui Basin Shizhuang South Block as an examples[J]. Science Technology and Engineering, 2020, 20(12): 4680-4686.
[18]	PAMUČAR D, ĆIROVIĆ G. The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison(MABAC)[J]. Expert Systems with Applications, 2015, 42(6): 3016-3028.
[19]	彭定洪, 黄子航, 罗雪, 等. 基于情景模糊动态MABAC的可再生能源选择方法[J]. 环境科学研究, 2019, 32(3): 532-538.
	PENG Dinghong, HUANG Zihang, LUO Xue, et al. Renewable energy selection method based on picture fuzzy dynamic MABAC[J]. Environmental Science Research, 2019, 32(3): 532-538.
[20]	REZAEI J. Best-worst multi-criteria decision-making method[J]. Omega: The international journal of management science, 2015, 53: 49-57.
[21]	王思航, 田小林, 王楚柯, 等. 模糊相似法对页岩气选区评价的应用——以四川盆地海相页岩气为例[J]. 油气藏评价与开发, 2018, 8(1): 71-75.
	WANG Sihang, TIAN Xiaolin, WANG Chuke, et al. Application of fuzzy similarity methods for evaluating the district of shale gas: A case of marine shale gas in Sichuan Basin[J]. Reservoir Evaluation and Development, 2018, 8(1): 71-75.
[22]	TVERSKY A, KAHNEMAN D. Advances in prospect theory: Cumulative representation of uncertainty[J]. Journal of Risk and Uncertainty, 1992, 5(4): 297-323.
[23]	HUANG C L, YOON K. Multiple attribute decision making: Methods and applications: A state-of-the-art survey[M]. Heidelberg: Springer Berlin, 1981.
[24]	KESHAVARZ-GHORABAEE M, ZAVADSKAS E K, OLFAT L, et al. Multi-criteria inventory classification using a new method of evaluation based on distance from average solution(EDAS)[J]. Informatica, 2015, 26(3): 435-451.

区块	有效储层厚度/ m	剩余动态储量/10⁸ m³	含气饱和度/ %	渗透率/ 10^-3 μm²	孔隙度/ %	泊松比	地层压力/ MPa	温度/ ℃	埋深/ m
A	21.40	2.53	80.70	0.57	3.87	0.28	58.59	107.87	4 614
B	1.95	0.52	81.03	0.03	1.59	0.31	33.81	96.67	3 295
C	34.30	16.50	87.20	1.80	5.13	0.22	59.20	110.16	4 846
D	24.01	1.88	80.90	1.39	5.22	0.28	53.70	102.06	4 395
E	12.24	2.16	75.74	1.01	5.11	0.30	52.43	103.34	3 949
F	26.00	0.59	72.75	1.04	4.71	0.24	35.18	94.51	3 386
G	29.80	1.57	71.27	1.12	4.75	0.31	58.43	109.74	4 981
H	20.27	5.29	85.00	1.91	4.80	0.26	59.92	100.66	5 001
I	29.60	6.90	91.00	2.40	9.00	0.25	53.94	114.09	4 326
J	17.30	0.86	82.10	1.08	4.80	0.27	59.24	105.05	5 087

Application of intuitive fuzzy MABAC method in optimizing favorable areas of low permeability carbonate gas reservoirs

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 24

Related Articles 1

Metrics

Comments

Recommended 0

区块	直觉模糊MABAC排序	EDAS排序	TOPSIS排序
A	4	4	5
B	10	10	10
C	1	1	1
D	5	5	6
E	8	7	9
F	9	9	8
G	6	6	4
H	3	3	3
I	2	2	2
J	7	8	7