基于机器学习的裂缝水驱气藏采收率预测方法

doi:10.13809/j.cnki.cn32-1825/te.2025.05.013

Abstract

Abstract:

Gas reservoir X is a block-fractured edge-water gas reservoir controlled by anticline structures. Due to edge-water invasion, rapid water breakthrough severely reduced recovery efficiency, highlighting the urgent need for theoretical and methodological guidance for reservoir development. To address this, based on a systematic analysis of the influence patterns of key parameters on recovery rate, a recovery prediction model for fractured water-driven gas reservoirs was established. This model provides a scientific basis for the dynamic optimization of development schemes. Using basic geological and production data, a single-well mechanistic model was constructed with the embedded discrete fracture model (EDFM). Through single-factor sensitivity analysis, the influence patterns of water body multiple, gas production rate, matrix permeability, and permeability anisotropy were revealed. Recovery rate exhibited a negative correlation with water body multiple. As the water body multiple increased, the water-gas ratio of gas wells rose significantly faster, and the stable production time was greatly shortened. Gas production rate had a pronounced impact on the stable production time of the reservoir. An optimal gas production rate threshold existed that maximized the recovery rate. Matrix permeability was positively correlated with recovery rate. Lower matrix permeability led to lower recovery rate. The faster the increase in water-gas ratio, the shorter the stable production time. When permeability anisotropy was too low, poor seepage capacity resulted in a reduced recovery rate. An increased ratio accelerated water invasion, further decreasing the recovery rate. Based on these findings, 125 sets of cross-experimental schemes were designed, and basic data were obtained through numerical simulations. A recovery rate prediction model for fractured water-driven gas reservoirs was established. To improve the prediction accuracy of the model, the original data were discretized, and a decision tree algorithm was used. After parameter optimization, the prediction accuracy of the model reached 96%. The model was validated using actual dynamic data from two production wells in the gas reservoir X. The prediction results were compared with those from the Blasingame production decline analysis method. The results showed high consistency between model predictions and actual production data, indicating high reliability and practicality of the model. This provides an efficient and precise technical method for recovery rate prediction in fractured water-driven gas reservoirs.

Key words: machine learning, embedded discrete fracture model, recovery rate prediction, fractured water-driven gas reservoirs, numerical simulation

CLC Number:

TE319

SUN Qiufen,QIN Jiazheng,FENG Qiao, et al. A machine learning-based method for recovery rate prediction in fractured water-driven gas reservoirs[J]. Petroleum Reservoir Evaluation and Development, 2025, 15(5): 834-843.

Figures/Tables 12

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Table 1

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References 26

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井名	M	k/10^-3 μm²	v/%	A	B
水平1	1	0.1	3	0.10	1
水平2	5	0.5	6	0.50	5
水平3	10	1.0	9	1.00	10
水平4	15	5.0	12
水平5	20	10.0

方案	M	k/10^-3 μm²	v/%	A	B	R/%
方案1	1	5.0	3	0.10	1	54.71
方案2	1	1.0	12	0.10	10	47.94
方案3	1	10.0	9	0.50	10	49.00
方案4	1	0.1	12	1.00	1	22.10
方案5	1	10.0	6	0.10	5	53.28
方案6	1	0.5	12	0.50	5	50.26
方案7	1	0.1	3	1.00	5	25.12
方案8	1	0.5	6	1.00	5	43.74
方案9	1	5.0	12	0.10	10	52.77
方案10	5	1.0	3	0.10	1	60.10
方案11	5	0.1	6	1.00	5	23.01
方案12	5	10.0	3	0.50	1	64.19
方案13	5	0.5	9	0.10	10	60.38
方案14	5	5.0	6	1.00	1	52.85
方案15	5	0.1	12	0.10	10	36.59
方案16	5	10.0	12	0.50	5	63.54
方案17	5	10.0	3	1.00	10	61.92
方案18	5	0.1	9	0.50	1	25.33
方案19	10	0.1	3	0.10	5	35.12
方案20	10	5.0	9	1.00	5	67.12
方案21	10	1.0	6	0.50	10	67.11
方案22	10	1.0	12	0.50	5	66.47
方案23	10	0.5	6	0.10	1	56.43
方案24	10	10.0	9	1.00	1	66.84
方案25	10	5.0	9	1.00	10	67.05
…	…	…	…	…		…
方案122	20	0.5	9	0.10	10	61.07
方案123	20	1.0	12	0.50	5	61.65
方案124	20	5.0	3	0.10	1	61.67
方案125	20	0.5	3	0.50	5	57.33

数据类型	参数	分类规则	分类结果
建模特征	M	M<10	1
		10≤M<20	2
		M≥20	3
	k/10^-3μm²	k<1	1
		1≤k≤5	2
		k>5	3
	v/%	v≤6	1
	v/%	v>6	2
	A	A<0.5	1
	A	A≥0.5	2
	B	B<5	1
	B	B≥5	2
建模标签	R/%	R<35	1（低）
		35≤R≤55	2（中）
		R>55	3（高）

参数	最优值
评估指标	基尼系数
决策树最大深度	6
叶节点存在所需的最小样本数	1
节点分枝策略	最佳随机分枝

A machine learning-based method for recovery rate prediction in fractured water-driven gas reservoirs

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