基于机器学习的裂缝水驱气藏采收率预测方法

摘要/Abstract

摘要： X气藏为受背斜构造控制的块状裂缝性边水气藏,受边水水侵的影响,气藏见水迅速,严重降低采收率,亟须科学的理论与方法指导开发实践。为此,研究在系统分析关键参数对采收率的影响规律基础上,构建裂缝水驱气藏采收率预测方法,为动态优化开发方案提供科学依据。基于气藏基础地质与生产数据,运用EDFM（嵌入式离散裂缝模型）建立单井机理模型,通过单因素敏感性分析,揭示水体倍数、采气速度、基质渗透率、渗透率各向异性的作用规律：水体倍数与采收率呈负相关,随着水体倍数增加,气井水气比上升速率显著加快,稳产时间大幅缩短;采气速度对气藏稳产时间影响尤为突出,存在特定的最佳采气速度阈值,可使采收率达到最大值;基质渗透率与采收率呈正相关,基质渗透率越低,气藏采收率越低,水气比上升速度越快,稳产时间越短;渗透率各向异性过低时,由于渗流能力较差导致采收率降低,比值增大则加速水侵使采收率降低。在此基础上,设计125组交叉实验方案,通过数值模拟获取基础数据,进而建立裂缝性水驱气藏采收率预测模型。为提升模型预测精度,对原始数据进行离散化处理,并采用决策树算法构建预测模型。经参数优化后,模型预测精度达到96%。基于X气藏2口生产井的实际动态数据,将该模型预测结果与Blasingame产量递减分析法进行对比验证,结果表明：该模型预测结果与实际生产数据高度吻合,具有较高的可靠性与实用性,为裂缝性水驱气藏采收率预测提供了一种高效、精准的技术手段。

关键词: 机器学习, 嵌入式离散裂缝模型, 采收率预测, 裂缝性水驱气藏, 数值模拟

Abstract: X gas reservoir is a massive fractured edge-water gas reservoir controlled by anticline structures, where rapid water breakthrough induced by edge water invasion significantly compromises recovery efficiency, thereby necessitating scientific theories and methodologies to guide development practices. This study systematically investigates the influence of key parameters on recovery efficiency and establishes a prediction model for recovery efficiency in fractured water-drive gas reservoirs, aiming to provide a scientific basis for dynamic optimization of development strategies. Leveraging basic geological and production datasets of the reservoir, a single-well mechanistic model was constructed using the Embedded Discrete Fracture Model (EDFM). Through single-factor sensitivity analysis, the study elucidates the governing mechanisms of water body multiple, gas production rate, matrix permeability, and permeability anisotropy: recovery efficiency exhibits a negative correlation with water body multiple, characterized by accelerated water-gas ratio growth and shortened stable production duration as the multiple increases; gas production rate exerts a pronounced impact on stable production time, with an optimal operational threshold identified to maximize recovery efficiency; matrix permeability demonstrates a positive correlation with recovery efficiency, where lower permeability correlates with faster water-gas ratio escalation and shorter stable production time; excessively low permeability anisotropy ratios degrade recovery efficiency due to inadequate seepage capacity, while higher ratios exacerbate water invasion and further diminish recovery efficiency. 125 sets of cross-experimental schemes were designed. Basic data were obtained through numerical simulation to establish a recovery rate prediction model for fractured water-driven gas reservoirs. Raw data were discretized to enhance predictive accuracy, and a decision tree algorithm was employed to construct the model, which achieved a prediction accuracy of 96% following parameter optimization. Validation against Blasingame production decline analysis using field dynamic data from two production wells in the X gas reservoir demonstrates a high degree of consistency between model predictions and actual production data, underscoring the model's reliability and practical utility. This research provides an efficient and precise technical methodology for predicting recovery efficiency in fractured water-drive gas reservoirs, offering valuable insights for similar reservoir systems.

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

中图分类号:

TE319

孙秋分,秦佳正,冯乔, 等. 基于机器学习的裂缝水驱气藏采收率预测方法[J]. 油气藏评价与开发, 0, (): 2024280-2024280.

SUN Qiufen,QIN Jiazheng,FENG Qiao, et al. A machine learning-based method for predicting recovery rate in fractured water-drive gas reservoirs[J]. Petroleum Reservoir Evaluation and Development, 0, (): 2024280-2024280.

参考文献

[1] 国家能源局. 天然气可采储量计算方法: SY/T 6098—2022[S]. 北京: 石油工业出版社, 2022.
National Energy Bureau of the People's Republic of China. Natural gas recoverable reserves calculation method: SY/T 6098—2022[S]. Beijing: Petroleum Industry Press, 2022.
[2] 尹涛, 杨屹铭, 靳锁宝, 等. 概率法在岩性气藏储量风险评估中的应用[J]. 西南石油大学学报(自然科学版), 2020, 42(3): 60-68.
YIN Tao, YANG Yiming, JIN Suobao, et al.Application of probability method in the reserves risk evaluation of lithologic gas reservoirs[J]. Journal of Southwest Petroleum University (Science & Technology Edition), 2020, 42(3): 60-68.
[3] 孙贺东, 王宏宇, 朱松柏, 等. 基于幂函数形式物质平衡方法的高压、超高压气藏储量评价[J]. 天然气工业, 2019, 39(3): 56-64.
SUN Hedong, WANG Hongyu, ZHU Songbai, et al.Reserve evaluation of high pressure and ultra high pressure reservoirs with power function material balance method[J]. Natural Gas Industry, 2019, 39(3): 56-64.
[4] 付斌, 李进步, 张晨, 等. 强非均质致密砂岩气藏已开发区井网完善方法[J]. 天然气地球科学, 2020, 31(1): 143-149.
FU Bin, LI Jinbu, ZHANG Chen, et al.Improvement of well pattern in development area of tight sandstone gas reservoir[J]. Natural Gas Geoscience, 2020, 31(1): 143-149.
[5] 郑玲丽, 朱冰倩, 张宇豪, 等. 缝洞型碳酸盐岩油藏水驱特征曲线类型及适应性: 以塔河油田为例[J]. 油气藏评价与开发, 2024, 14(6): 899-907.
ZHENG Lingli, ZHU Bingqian, ZHANG Yuhao, et al.Types and applicability of waterflooding characteristic curves in fractured-cavity carbonate reservoirs: A case study of Tahe Oilfield[J]. Petroleum Reservoir Evaluation and Development, 2024, 14(6): 899-907.
[6] 崔传智, 李怀亮, 吴忠维, 等. 考虑压驱注水诱发裂缝影响的注水井压力分析[J]. 油气藏评价与开发, 2023, 13(5): 686-694.
CUI Chuanzhi, LI Huailiang, WU Zhongwei, et al.Analysis of pressures in water injection wells considering fracture influence induced by pressure-drive water injection[J]. Petroleum Reservoir Evaluation and Development, 2023, 13(5): 686-694.
[7] 梁运培, 张怀军, 王礼春, 等. 连续加载应力下真实裂缝流场和渗透率演化规律数值研究[J]. 油气藏评价与开发, 2023, 13(6): 834-843.
LIANG Yunpei, ZHANG Huaijun, WANG Lichun, et al.Numerical simulation of flow fields and permeability evolution in real fractures under continuous loading stress[J]. Petroleum Reservoir Evaluation and Development, 2023, 13(6): 834-843.
[8] 陈祥, 王冠, 刘平礼, 等. 四川盆地灯影组酸压裂缝导流能力实验和模拟研究[J]. 油气藏评价与开发, 2024, 14(4): 569-576.
CHEN Xiang, WANG Guan, LIU Pingli, et al.Experimental and simulation study on fracture conductivity of acid-fracturing in Dengying Formation of Sichuan Basin[J]. Petroleum Reservoir Evaluation and Development, 2024, 14(4): 569-576.
[9] SIDLE R E, LEE W J.An update on the use of reservoir analogs for the estimation of oil and gas reserves[C]//SPE Hydrocarbon Economics and Evaluation Symposium. SPE, 2010: 1-9.
[10] 谭晓华, 彭港珍, 李晓平, 等. 考虑水封气影响的有水气藏物质平衡法及非均匀水侵模式划分[J]. 天然气工业, 2021, 41(3): 97-103.
TAN Xiaohua, PENG Gangzhen, LI Xiaoping, et al.Material balance method and classification of non-uniform water invasion mode for gas reservoirs with water considering the effect of water sealed gas[J]. Natural Gas Industry, 2021, 41(3): 97-103.
[11] 吕志凯, 唐海发, 刘群明, 等. 塔里木盆地库车坳陷超深层裂缝性致密气藏水封气动态评价方法[J]. 天然气地球科学, 2022, 33(11): 1874-1882.
LYU Zhikai, TANG Haifa, LIU Qunming, et al.Dynamic evaluation method of water sealed gas for ultra-deep fractured tight gas reservoir in Kuqa Depression, Tarim Basin[J]. Natural Gas Geoscience, 2022, 33(11): 1874-1882.
[12] 王国锋, 周梦飞, 胡勇, 等. 裂缝—孔隙型边底水气藏提高采收率大型物理模拟实验[J]. 天然气地球科学, 2024, 35(1): 96-103.
WANG Guofeng, ZHOU Mengfei, HU Yong, et al.Large-scale physical simulation experiment for enhanced gas recovery in fractured-porous water-drive gas reservoirs[J]. Natural Gas Geoscience, 2024, 35(1): 96-103.
[13] 刘念肖, 雷登生, 黄小亮, 等. 考虑水封气的水驱气藏开发因素数值模拟研究[J]. 重庆科技学院学报(自然科学版), 2022, 24(2): 31-36.
LIU Nianxiao, LEI Dengsheng, HUANG Xiaoliang, et al.Numerical simulation research on development factors of water drive gas reservoir considering water-sealed gas[J]. Journal of Chongqing University of Science and Technology (Natural Sciences Edition), 2022, 24(2): 31-36.
[14] 胡景涛, 王本成, 王勇飞, 张明迪. 基于试井分析方法的元坝气田水侵早期识别[J]. 非常规油气, 2023, 10(3): 89-97.
HU Jingtao, WANG Bencheng, WANG Yongfei, et al.Early identification of water invasion in Yuanba Gas Field based on well test analysis method[J]. Unconventional Oil & Gas, 2023, 10(3): 89-97.
[15] 李兴娟, 姜应兵, 丁立明. 塔河油田AD4单元流场变化及水侵规律认识[J]. 非常规油气, 2022, 9(5): 123-128.
LI Xingjuan, JIANG Yingbing, DING Liming.Understanding of flow distribution and water intrusion rule in AD4 well area of Tahe Olifield[J]. Unconventional Oil & Gas, 2022, 9(5): 123-128.
[16] 侯亚伟, 刘超, 徐中波, 等. 多层水驱开发油田采收率快速预测方法[J]. 石油钻探技术, 2022, 50(5): 82-87.
HOU Yawei, LIU Chao, XU Zhongbo, et al.A method for rapidly predicting recovery of multi-layer oilfields developed by water-flooding[J]. Petroleum Drilling Techniques, 2022, 50(5): 82-87.
[17] 盖建. 基于自动机器学习的采油井压裂效果预测方法[J]. 油气地质与采收率, 2023, 30(1): 161-170.
GAI Jian.Prediction method for hydraulic fracturing effect of oil production well based on automatic machine learning technology[J]. Petroleum Geology and Recovery Efficiency, 2023, 30(1): 161-170.
[18] 尚福华, 郑伟. 基于决策树的注采连通关系判别研究[J]. 计算机应用研究, 2013, (7): 2051-2054.
SHANG Fuhua, ZHENG Wei.Study on inferring interwell connectivity of injection-production system based on decision tree[J]. Application Research of Computers, 2013, 30(7): 2051-2054.
[19] 张世昆, 陈作. 人工智能在压裂技术中的应用现状及前景展望[J]. 石油钻探技术, 2023, 51(1): 69-77.
ZHANG Shikun, CHEN Zuo.Status and prospect of artificial intelligence application in fracturing technology[J]. Petroleum Drilling Techniques, 2023, 51(1): 69-77.
[20] 徐帅. 致密油藏注水吞吐数值模拟及开发制度优化[D]. 北京: 中国石油大学(北京), 2022.
XU Shuai.Numerical simulation and development system optimization of water injection huff and puff in tight oil reservoirs[D]. Beijing: China University of Petroleum (Beijing), 2022.
[21] 窦凯文. 致密油水平井体积压裂参数优化研究[D]. 东营: 中国石油大学(华东), 2018
DOU Kaiwen.Optimization of volumetric-fracturing parameters for horizontal wells in tight oil reservoirs[D]. Dongying: China University of Petroleum (East China), 2018.
[22] LEE S H, LOUGH M F, JENSEN C L.Hierarchical modeling of flow in naturally fractured formations with multiple length scales[J]. Water Resources Research, 2001, 37(3): 443-455.
[23] LI L, LEE S H.Efficient field-scale simulation of black oil in a naturally fractured reservoir through discrete fracture networks and homogenized media[J]. SPE Reservoir Evaluation &Engineering, 2008, 11(4): 750-758.
[24] XU Y, CAVALCANTE FILHO J S, YU W, et al. Discrete-fracture modeling of complex hydraulic-fracture geometries in reservoir simulators[J]. SPE Reservoir Evaluation & Engineering, 2017, 20(2): 403-422.
[25] 王平, 沈海超. 加拿大M致密砂岩气藏高效开发技术[J]. 石油钻探技术, 2022, 50(1): 97-102.
WANG Ping, SHEN Haichao.High-efficient development technologies for the M tight sandstone gas reservoir in Canada[J]. Petroleum Drilling Techniques, 2022, 50(1): 97-102.
[26] BREIMAN L, FRIEDMAN J, OLSHEN R A, et al.Classification and regression trees[M]. Belmont California: Wadsworth, 1984.