在全球经济蓬勃发展的大背景下，新兴市场崛起，能源需求持续攀升。尽管可再生能源的发展势头强劲，但石油作为关键能源的地位依旧稳固，需求热度不减。近年来，石油行业在技术创新上屡获突破，但也面临着环境保护、能源转型等方面的挑战。

本期刊·见为诸位介绍石油科学领域期刊Petroleum Science and Technology。除了对期刊进行详尽的介绍外，还向您介绍刊内近三年高被引文章，以及近一年高阅读文章：

• 使用机器学习方法对钻井速率指数进行预测建模：LSTM, simple RNN,RFA

• 天然裂缝储层中孔隙间流动的勘探半径和影响

• 利用深度学习模型识别页岩气生产的控制地质和工程因素：以中国威远为例

Print ISSN: 1091-6466 Online ISSN: 1532-2459

Petroleum Science and Technology发表高质量的原创研究，主要探讨以下方面话题：

• 与先进石油采收工艺相关的流体-流体和岩石-流体相互作用以及多孔介质中的多相界面和传输现象的基础科学

• 以碳敏感方式提高地下能源资源采收率的新概念和工艺流程的应用

• 将实验室研究成果扩展到现场试点和全面应用的案例研究

• 石油行业面临的其他突出技术挑战

期刊鼓励来自以下领域的投稿：

在表面活性剂、纳米流体、二氧化碳和其他可以提高常规和非常规（页岩油、重油和沥青）资源石油采收率的药剂存在的情况下，碳氢化合物的流体-流体相互作用（溶解度、混溶性、相行为）影响碳氢化合物在多孔地下介质中分布和多相流动的润湿性、扩散粘附性和相对渗透性特征的岩石-流体相互作用

• 通过化学、气体和热注入工艺提高石油采收率

• 涉及石油采收和生产环境方面的原创研究

• 近井筒过程、储层和井筒输送过程之间的耦合，良好井筒性能的管理技术

• 储层尺度的孔隙弹性和水力压裂研究，探索这种机制对石油科学和技术的影响

• 实验研究以及理论和多尺度建模工作，并对结果进行了实质性验证

该期刊已被SCIE, Scopus, Engineering Index/COMPENDEX PLUS等国际知名数据库收录。

根据JCR显示，Petroleum Science and Technology

• 2023年影响因子为1.3

• 在能源和燃料领域排名148/170

• 在工程：化学领域排名122/170

• 在工程：石油领域排名12/23

根据Scopus显示, Petroleum Science and Technology的

• CiteScore（2023）为2.9

地球与行星科学：

• 岩土工程与工程地质领域排名107/229

能源：

• 能源工程与电力技术领域排名138/272

• 燃料技术领域排名71/128

化学：

• 通用化学领域排名223/408

化学工程：

• 通用化学工程领域排名150/273

• 稿件一旦接受后，在线出版平均需要12天

Petroleum Science and Technology的主编由来自得克萨斯农工大学（Texas A&M University）的Berna Hasçakır教授担任。副主编及编委会团队由来自中国、美国、伊朗等多地的行业翘楚组成。

Berna Hasçakır，得克萨斯农工大学（ Texas A&M University, US）石油工程专业教授。

她的研究领域包括：以提高石油采收率的方法开采稠油和油页岩、储层岩石和流体诊断研究、采出水管理。

杨仁锋，中海油研究总院有限责任公司勘探开发研究院副院长、中国海洋石油集团有限公司所属单位专家、第二十五届茅以升科学技术奖—北京青年科技奖获得者。研究领域包括渗流力学基础理论、渗流力学理论、低渗透储层数值模拟与开发技术、稠油与非常规油气藏、储层损害机理与应用、数据挖掘与人工智能在气油藏开发中的应用。

根据JCR显示，近三年在Petroleum Science and Technology发文的国家中，发文前三的国家/地区有：

• 中国

• 伊朗

• 印度

近三年，在Petroleum Science and Technology发文的全球高校和科研机构中，发文较活跃的中国机构是：

• 中国石油天然气集团有限公司

• 西南石油大学

• 中国石油大学

使用机器学习方法对钻井速率指数进行预测建模： LSTM, simple RNN,RFA

通讯作者： 郑西贵 中国矿业大学

文章摘要

Drilling rate index (DRI) is a fundamental parameter in the investigation of rock drillability, as drillability is considered one of the main problems in rock engineering. Several researchers have continuously tried to analyze and correlate rock DRI, but the problem remains unchanged. This study elucidates the machine learning approaches, namely long short term memory (LSTM), simple recurrent neural network (RNN) and random forest algorithm (RFA) to predict DRI of rocks using multivariate inputs, that is, uniaxial compressive strength in MPa; Brazilian tensile strength (BTS) in MPa; brittleness value (S20); Sievers’ J value (Sj); modulus ratio (MR); shore hardness (SH), porosity (n) in %; shimazeks F abrasitivity in N/mm; and equivalent quartz content in %. For all proposed methods, the original dataset was divided into 70% for training and the remaining 30% for testing. Next, the performance indices, such as correlation coefficient (R2), root mean square error (RMSE), variance accounts for (VAF) and a-20 index of each proposed method were determined to examine the accuracy of the predicted data. In this study, according to the results of LSTM, simple RNN and RFA methods, the LSTM revealed the best prediction output for DRI with the strongest R2, the lowest RMSE, the largest VAF and an appropriate a-20 index values as 0.999, 0.13416, 0.997, and 0.999 in the training stage and 0.998, 0.19479, 0.996, and 0.997 in the testing stage, respectively. Therefore, LSTM is an applicable machine learning approach that can be applied to accurately predict the DRI.

天然裂缝储层中孔隙间流动的勘探半径和影响

作者：范海军 中国石油大学（华东）

文章摘要

The radius of investigation (ROI) is an essential parameter in many reservoir engineering applications. Inconsistencies and ambiguities still exist between different ROI evaluation methods, especially for the dual-porosity (DP) system representing naturally fractured reservoirs. This paper revisited the impact of inter-porosity flow in the DP system and presented ROI equations in the early and late flow periods based on the Laplace space solution of the Warren-Root dual-porosity model. A new comprehensive ROI expression for the DP system is derived which accounts for both storativity ratio (ω) and inter-porosity flow coefficient (λ). The new method in this paper reveals that the log-log plot of ROI vs. time for DP models exhibits two parallel straight lines linked by a smooth transition curve. The distance of these two straight lines is dependent on ω, and ROI in the intermediate flow period is highly dependent on the value of λ. The new ROI equation for the DP system can degenerate to similar ROI formula for the single-porosity system in early and late flow periods. Comparison analysis of the new method with other approaches is conducted and the validation of the new method is realized by analytical and numerical matching.

Figure 1. Pressure profile for a dual-porosity system at different time (ω = 0.01, λ = 0.00001).

利用深度学习模型识别页岩气生产的控制地质和工程因素：以中国威远为例

通讯作者：郝有志 中国科学技术大学

文章摘要

Predicting shale gas production is challenging due to varying and unclear influencing factors. In this work, we explore the average daily production rate (ADPR) and its determinants by analyzing data from 119 horizontal wells in the Weiyuan block. Initial data analysis revealed weak Spearman correlations between ADPR and geological and engineering parameters. Then, we develop four feed-forward deep learning models to predict ADPR and compare them. One of the proposed models utilizes geological and engineering data while the other three models utilize additional early-stage production data. The model with test-stage gas production can reach R2>0.95 while the model without production data can also reach R2>0.88, indicating ADPR can be efficiently predicted using only geological and engineering parameters. Moreover, the multiplication of the thickness of the target formation and the drilled length in the high-quality reservoir, as well as total organic carbon content (TOC), are the two most important influencing factors of ADPR apart from test-stage gas production. In contrast, the first-month flowback ratio is not important and cannot improve model performance. These findings can provide experts with theoretical suggestions for shale gas development. The workflow is also beneficial to future research by repeating it on larger datasets with more parameters.

Figure 1. A demonstration of drilling and hydraulic fracturing parameters.