Machine-learning-based hydraulic fracturing flowback forecasting

IF 2.6 3区 工程技术 Q3 ENERGY & FUELS Journal of Energy Resources Technology-transactions of The Asme Pub Date : 2023-02-24 DOI:10.1115/1.4056993
Jinyuan Guo, Weisi Guo, Lixia Kang, Xiaowei Zhang, Jinliang Gao, Yuyang Liu, Ji Liu, Haiqing Yu
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Abstract

Hydraulic fracturing is an indispensable procedure to the economic development of shale gas. The flowback of the hydraulic fracturing fluid is one of the most important parameters recorded after shale gas wells are put into production. Generally, the flowback ratio is used as the flowback indicator. The flowback ratio has a great influence on shale gas production. However, the flowback ratio is subjected to various affecting factors with their correlativity unclear. Based on a large amount of original geological, engineering, and dynamic data acquired from 373 hydraulically-fractured horizontal wells in the Weiyuan Shale Gas Field, the flowback characteristics were systematically studied based on machine learning. Based on the data analysis and random forest forecasting, a new indicator, single-cluster flowback ratio, was proposed, which can more effectively reflect the inherent relationship between flowback fluid volume and influencing factors. The results of training random forests show that this indicator has better learnability and predictability. A good linear relationship exists between single-cluster flowback ratios in different production stages. Accordingly, the 30-day single-cluster flowback ratio can be used to predict the 90-day and 180-day single-cluster flowback ratios. The main controlling factors of production and flowback ratio were also systematically analyzed. It is found that the main controlling factors of the flowback ratio include the number of fracturing clusters, the total amount of sand and number of fracturing stages. This study can provide a fundamental reference for analyzing the hydraulically fracturing fluid flowback for shale gas reservoirs.
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基于机器学习的水力压裂返排预测
水力压裂是页岩气经济开发中必不可少的环节。水力压裂液的返排是页岩气井投产后记录的最重要的参数之一。通常,回流比被用作回流指示器。返排率对页岩气产量有很大影响。然而,返排率受到各种影响因素的影响,其相关性不明确。基于威远页岩气田373口水力压裂水平井的大量原始地质、工程和动态数据,基于机器学习对其返排特性进行了系统研究。在数据分析和随机森林预测的基础上,提出了一种新的指标——单簇返排率,它可以更有效地反映返排液量与影响因素之间的内在关系。训练随机森林的结果表明,该指标具有更好的可学习性和可预测性。不同生产阶段的单簇返排率之间存在良好的线性关系。因此,30天单集群回流比可以用于预测90天和180天单集群的回流比。系统分析了产量和返排比的主要控制因素。研究发现,控制返排率的主要因素包括压裂丛数、砂总量和压裂阶段数。该研究可为页岩气藏水力压裂液返排分析提供基础参考。
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来源期刊
CiteScore
6.40
自引率
30.00%
发文量
213
审稿时长
4.5 months
期刊介绍: Specific areas of importance including, but not limited to: Fundamentals of thermodynamics such as energy, entropy and exergy, laws of thermodynamics; Thermoeconomics; Alternative and renewable energy sources; Internal combustion engines; (Geo) thermal energy storage and conversion systems; Fundamental combustion of fuels; Energy resource recovery from biomass and solid wastes; Carbon capture; Land and offshore wells drilling; Production and reservoir engineering;, Economics of energy resource exploitation
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