多属性融合定量储层预测方法研究与应用——以廊固凹陷杨税务潜山为例

doi:10.11720/wtyht.2022.2371

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Abstract

The Yangshuiwu buried hill is located in the northern part of the Langgu sag,where fractured porous reservoirs are well developed.As indicated by comprehensive geological studies,it is in the direction favorable for hydrocarbon migration and accumulation.However,owing to the deep reservoirs and low imaging accuracy of seismic data,it is difficult to predict effective reservoirs using conventional attributes and the obtained prediction results feature strong multiplicity of solution.Based on full analyses of the geophysical response characteristics of existing drilled wells,this study selects three optimal attributes sensitive to the response of effective reservoirs,namely mean amplitude,variance,and arc length,and calculates the correlation coefficient between the thickness of effective reservoirs and each of the optimal attributes.Then it determines the fusion weight of each attribute according to corresponding correlation coefficient,and finally obtains the fused attribute than can reflect the thickness of effective reservoirs.The fused attribute can be used to effectively reduce the multiplicity of solution compared with single attribute prediction and quantitatively predict effective reservoirs.Practice has proved that the multi-attribute fusion technology is effective and practical and has achieved accurate application results in the Yangshuiwu buried hill.

Key words： Yangshuiwu buried hill quantitative reservoir prediction multi-attribute fusion

Received: 14 October 2020 Published: 25 February 2022

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	Articles by authors
	Cheng-Quan WANG
	Meng-Hua WANG
	Jia-Yi ZHOU
	Sheng-Liang WANG
	Zhou-Peng YANG
	Hui LIU
	Hong-Wen ZHANG

Cite this article:

Cheng-Quan WANG,Meng-Hua WANG,Jia-Yi ZHOU, et al. Application of multi-attribute fusion in quantitative prediction of reservoirs: A case study of Yangshuiwu buried hill in Langgu sag[J]. Geophysical and Geochemical Exploration, 2022, 46(1): 87-95.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2022.2371 OR https://www.wutanyuhuatan.com/EN/Y2022/V46/I1/87

Statistical figure of dolomite content

Carbonate reservoir type of buried hill

Log reservoir evaluation classification

Well-to-seismic calibration result of 1X、2X、3X、4X、5X、W1

The preferred attribute prediction of each reservoir

地层	属性类别		1X井	2X井	3X井	4X井	5X井	W1	实钻厚度与属性拟合公式
峰峰组	平均振幅	属性值	9826	13665	5636	9773	13562	10593	y=45.7ln x-389.3
		预测厚度/m	30.8	45.9	5.4	30.6	45.5	34.2
		实钻厚度/m	16.2	64.4	12.8	27.8	40.0	31.2
		绝对误差/m	14.6	18.5	7.4	2.8	5.5	3.0
	方差属性	属性值	2923	2109	2004	3409	3813	3098	y=-5.6ln x+76.2
		预测厚度/m	31.8	33.6	33.9	30.9	30.3	31.5
		实钻厚度/m	16.2	64.4	12.8	27.8	40.0	31.2
		绝对误差/m	15.6	30.8	21.1	3.1	9.7	0.3
	弧长属性	属性值	4373	3409	3023	4696	5308	3809	y=63.5ln x-496.7
		预测厚度/m	35.6	19.8	12.2	40.2	47.9	26.9
		实钻厚度/m	16.2	64.4	12.8	27.8	40.0	31.2
		绝对误差/m	19.4	44.6	0.6	12.4	7.9	4.3
上马家沟组	平均振幅	属性值	8992	209	1809	4768	5698	3676	y=14.6ln x-91.9
		预测厚度/m	41.0	13.9	17.6	31.8	34.4	28.0
		实钻厚度/m	66.6	28.1	15.4	37.2	31.8	22.2
		绝对误差/m	25.6	14.2	2.2	5.4	2.6	5.8
	方差属性	属性值	4637	2108	2809	3365	4598	2323	y=31.5ln x-227.5
		预测厚度/m	38.4	13.6	22.6	28.3	38.2	16.6
		实钻厚度/m	66.6	28.1	15.4	37.2	31.8	22.2
		绝对误差/m	28.2	14.5	7.2	8.9	6.4	5.6
	弧长属性	属性值	5325	3356	2586	4508	5089	2292	y=25ln x-177.1
		预测厚度/m	37.4	25.9	19.3	33.2	36.3	16.3
		实钻厚度/m	66.6	28.1	15.4	37.2	31.8	22.2
		绝对误差/m	29.2	2.2	3.9	4.0	4.5	5.9
下马家沟组	平均振幅	属性值	7870	5567	2096	5765	6908	670	y=29.2ln x-215.7
		预测厚度/m	46.2	36.1	7.6	37.2	42.4	25.7
		实钻厚度/m	42.6	40.8	21.8	82.4	24.2	6.4
		绝对误差/m	3.6	4.7	14.2	45.2	18.2	19.3
	方差属性	属性值	3809	2809	1679	3565	3609	1345	y=19.6ln x-126.7
		预测厚度/m	34.9	28.9	18.8	33.6	33.8	14.5
		实钻厚度/m	42.6	40.8	21.8	82.4	24.2	6.4
		绝对误差/m	7.7	11.9	3.0	48.8	9.6	8.1
	弧长属性	属性值	4871	3868	2508	5282	5071	1813	y=20.1ln x-135.6
		预测厚度/m	35.1	30.4	21.7	36.7	35.9	15.2
		实钻厚度/m	42.6	40.8	21.8	82.4	24.2	6.4
		绝对误差/m	7.5	10.4	0.1	45.7	11.7	8.8
亮甲山组	平均振幅	属性值	8034	7785	13926	2567	11735	9817	y=17.4ln x-122.1
		预测厚度/m	34.4	33.8	43.9	14.5	40.9	37.8
		实钻厚度/m		12.8	35.3	8.4	36.6	19.0
		绝对误差/m		21.0	8.6	6.1	4.3	18.8
	方差属性	属性值	3546	2071	4673	1893	5634	3865	y=21.8ln x-151.8
		预测厚度/m	26.4	14.7	32.4	12.7	36.5	28.3
		实钻厚度/m		12.8	35.3	8.4	36.6	19.0
		绝对误差/m		1.9	2.9	4.3	0.1	9.3
	弧长属性	属性值	3523	3630	3960	3675	4930	2031	y=25.5ln x-181.9
		预测厚度/m	26.4	27.1	29.3	27.4	34.9	12.3
		实钻厚度/m		12.8	35.3	8.4	36.6	19.0
		绝对误差/m		14.3	6.0	19.0	1.7	6.7

Statistical relationship between preferred attribute and effective thickness

Relation between attribute and effective thickness

Coefficient of association between preferred attribute and effective thick

Fusion weight calculation of preferred attribute

Fusion attribute formula and error analysis

Thickness diagram of fusion attribute prediction

Analysis diagram of multi-attribute fusion predicition

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