鄂尔多斯盆地中、东部深层煤岩气水平井高效开发主控因素

doi:10.11743/ogg20250119

Abstract

Abstract:

Abundant deep coal-rock gas in the Ordos Basin boasts enormous potential for exploration and development. The geological characteristics of deep coal-rock gas exhibit substantial regional changes， and reservoir simulation tests under various process conditions have revealed notable differences in the productivity of coal-rock gas wells. This study aims to investigate the main factors controlling the coal-rock gas productivity of horizontal wells. Using the dynamic and static data from deep coal-rock gas in the pilot test area and employing the analytical approach of geology-engineering integration， we depict the geological features of coals in detail and thoroughly assess the production indices of producing wells. Furthermore， geological and engineering assessments are carried out using methods such as Pearson correlation analysis， hierarchical clustering， and machine learning. The results indicate that under similar regional geological features such as coal structure and thermal maturity， coal thickness and gas-bearing property， two geological factors， significantly affect the productivity of coal-rock gas wells. Meanwhile， the total length of coals encountered during drilling （L）， total drill-in liquid volume （W）， proppant volume （S）， and proppant intensity （Sq） among engineering factors exhibit positive correlations with the productivity of coal-rock gas wells. Notably， engineering factors show more pronounced correlations with the first-year daily gas production compared to geological factors， and the composite geology-engineering factors display more significant correlations than individual factors. Increasing well-controlled drainage volume and stimulated reservoir volume （SRV） contributes to the high productivity of coal-rock gas wells. Assuming target coal thicknesses measuring 6 ~ 10 m and an average proppant intensity of 5.5 t/m， a lateral length of 1 000 ~ 1 500 m is required to achieve a single-well estimated ultimate recovery （EUR） of 5 000 × 10⁴ m³. A new method for predicting the single-well productivity of deep coal-rock gas， based on geological and engineering parameters， is developed using intelligent algorithms such as deep neural networks， support vector machines （SVMs）， and random forest models. This method achieves a coincidence rate of up to 91 % according to blind well verification with 22 wells involved.

Key words: machine learning, geological characteristics, productivity prediction, horizontal well development, deep coal-rock gas, Ordos Basin

CLC Number:

TE132.2

Shixiang FEI, Yuehua CUI, Xiaofeng LI, Shujie WANG, Ye WANG, Zhengtao ZHANG, Peilong MENG, Xiaopeng ZHENG, Yundong XU, Jianwen GAO, Wenqin LUO, Tingting JIANG. Main factors controlling the efficient production of horizontal wells for deep coal-rock gas in the eastern and central Ordos Basin[J]. Oil & Gas Geology, 2025, 46(1): 273-287.

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井号	N1H	S5H	M3H	Y1H	J26H	N2H	M2H	T11H	N3H	D1H	M5H	ZT1H	T7H	M1H	Q35H	N4H	M125H
水平段长度/m	1 500	1 066	1 506	1 650	1 240	1 037	2 222	1 550	1 040	876	1 562	1 408	1 314	1 023	638	1 130	350
钻遇煤岩长度/m	760	974	1 466	1 591	1 240	849	2 222	1 489	664	455	1 216	1 238	1 239	906	552	1 046	273
加砂量/m³	2 920	2 460	4 170	5 722	4 393	3 760	7 520	6 250	2 763	2 200	3 980	6 570	6 100	3 400	911	2 070	1 260
加砂强度/（t/m）	2.9	3.5	4.3	5.2	5.3	5.8	5.9	6.0	6.2	6.4	6.9	6.9	7.0	5.5	2.1	3.4	6.6
排量/（m³/min）	9 ~ 15	18 ~ 24	10 ~ 12	12 ~ 18	14 ~ 18	10 ~ 18	17 ~ 22	18 ~ 20	14 ~ 20	15 ~ 18	17 ~ 21	18 ~ 22	18 ~ 22	16 ~ 18	6 ~ 9	4 ~ 13	9 ~ 14
总液量/m³	36 642	17 210	28 435	41 708	34 052	30 144	43 701	33 085	21 770	19 475	23 400	35 594	38 897	22 103	5 165	15 646	8 346
埋深/m	3 221	2 358	2 582	3 211	3 101	3 163	2 379	2 645	3 258	2 376	2 721	2 351	2 425	3 137	2 819	3 177	2 848
煤岩厚度/m	6.5	5.3	7.0	6.2	11.2	5.8	9.3	9.5	6.0	6.4	8.5	5.1	10.2	4.9	7.5	6.5	13.5
煤岩结构	Ⅱ型	Ⅱ型	Ⅱ型	Ⅰ型	Ⅰ型	Ⅱ型	Ⅰ型	Ⅰ型	Ⅱ型	Ⅱ型	Ⅰ型	Ⅱ型	Ⅱ型	Ⅰ型	Ⅰ型	Ⅰ型	Ⅱ型
含气量/（m³/t）	15.0	21.0	23.0	19.0	18.6	15.5	22.0	17.0	17.0	21.5	22.0	21.0	23.5	15.0	23.1	14.5	19.0
平均气测值/%	32.3	24.2	60.5	61.8	59.1	45.7	41.1	57.2	32.7	71.8	57.8	31.3	65.8	40	49.7	36.6	40.3
压力系数	1.07	0.79	0.87	1.07	1.03	1.05	0.79	0.88	1.09	0.79	0.91	0.78	0.81	0.79	0.94	1.06	0.95

地质-工程复合因子	与首年日产气量相关性		与单井EUR相关性
地质-工程复合因子	线性相关系数（R²）	R²排序	线性相关系数（R²）	R²排序
①钻遇煤岩长度 × 煤岩厚度	0.652 9	4	0.630 0	4
②钻遇煤岩长度 × 煤岩厚度 × 含气量	0.632 7	5	0.597 8	5
③钻遇煤岩长度 × 煤岩厚度 × 加砂量	0.642 0	3	0.630 6	3
④钻遇煤岩长度 × 煤岩厚度 × 加砂强度	0.728 6	1	0.715 5	1
⑤钻遇煤岩长度 × 煤岩厚度 × 含气量 × 加砂强度	0.659 6	2	0.674 0	2
⑥钻遇煤岩长度 × 煤岩厚度 × 含气量 × 加砂量	0.581 7	7	0.597 5	6
⑦钻遇煤岩长度 × 煤岩厚度 × 含气量 × 总液量	0.606 2	6	0.570 8	7

项目	方案一	方案二	方案三
参数选择	全部参数	钻遇煤岩长度、煤岩厚度、含气量、总液量、加砂量、成熟度（R_o）	钻遇煤岩长度、煤岩厚度、含气量、加砂量、总液量
模型优选	RandomForestRegressor	PolynomialFeatures	linear_model
模型打分	0.88	0.99	0.97
均方根误差	722.35	56.88	468.80
均方根误差比值	0.90	0.85	0.94
与产能相关性	0.93	0.95	0.97
优缺点	考虑因素全面，但数据点少，准备度欠佳	准备度高，多项式复杂，不利于快捷计算	组合优势参数，计算方便

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Main factors controlling the efficient production of horizontal wells for deep coal-rock gas in the eastern and central Ordos Basin

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