An intelligent identification method of safety risk while drilling in gas drilling

In view of the shortcomings of current intelligent drilling technology in drilling condition representation, sample collection, data processing and feature extraction, an intelligent identification method of safety risk while drilling was established. The correlation analysis method was used to determine correlation parameters indicating gas drilling safety risk. By collecting monitoring data in the safety risk period of more than 20 wells, a sample database of a variety of safety risks in gas drilling was established, and the number of samples was expanded by using the method of few-shot learning. According to the forms of gas drilling monitoring data samples, a two-layer convolution neural network architecture was designed, and multiple convolution cores of different sizes and weights were set to realize the vertical and horizontal convolution computations of samples to extract and learn the variation law and correlation characteristics of multiple monitoring parameters. Finally, based on the training results of neural network, samples of different kinds of safety risks were selected to enhance the recognition accuracy. Compared with the traditional BP(error back propagation) full-connected neural network architecture, this method can more deeply and effectively identify safety risk characteristics in gas drilling, and thus identify and predict risks in advance, which is conducive to avoid and quickly solve safety risks while drilling. Field application has proved that this method has an identification accuracy of various safety risks while drilling in the process of gas drilling of about 90% and is practical.

Stratigraphic identification using real-time drilling data

Identification of stratigraphic interfaces and lithology is a key aspect in geological and geotechnical investigations.In this study,a monitoring while-drilling system was developed,along with a corresponding data pre-processing method.The method can handle invalid drilling data generated during manual operations.The correlation between various drilling parameters was analyzed,and a database of stratigraphic interfaces and key lithology identification based on the monitoring parameters was established.The average drilling speed was found to be the most suitable parameter for stratigraphic and lithology identification,and when the average drilling speed varied over a wide range,it corresponded to a stratigraphic interface.The average drilling speeds in sandy mudstone and sandstone strata were in the ranges of 0.1e0.2 m/min and 0.2e0.29 m/min,respectively.The results obtained using the present method were consistent with geotechnical survey results.The proposed method can be used for realtime lithology identification and represents a novel approach for intelligent geotechnical surveying.

A Study on the Classification and Well-Logging Identification of Eclogite in the Main Hole of Chinese Continental Scientific Drilling Project

Eclogite, one of the important lithologies in the main hole of the Chinese Continental Scientific Drilling （CCSD） Project, exists above the depth of 3 245 m and has distinctive responses of gamma-ray, compensating density and neutron well-logging, and so on. In this study, according to the diversities of minerals and chemical components and well-logging responses, edogites are classified from three aspects of origin, content of oxygen, and sub-mineral. We studied the logging identification method for eclogite sub-classes based on multi-element statistics and reconstructed 11 kinds of eclogite. As a result, eclogites can be divided into 6 types using well logs. In the light of this recognition, the eclogite in the main hole is divided into 20 sections, and the distribution characters of all sub-classes of eclogite are analyzed, which will provide important data for geological research of CCSD.

Intelligent risk identification of gas drilling based on nonlinear classification network

During the transient process of gas drilling conditions,the monitoring data often has obvious nonlinear fluctuation features,which leads to large classification errors and time delays in the commonly used intelligent classification models.Combined with the structural features of data samples obtained from monitoring while drilling,this paper uses convolution algorithm to extract the correlation features of multiple monitoring while drilling parameters changing with time,and applies RBF network with nonlinear classification ability to classify the features.In the training process,the loss function component based on distance mean square error is used to effectively adjust the best clustering center in RBF.Many field applications show that,the recognition accuracy of the above nonlinear classification network model for gas production,water production and drill sticking is 97.32%,95.25%and 93.78%.Compared with the traditional convolutional neural network(CNN)model,the network structure not only improves the classification accuracy of conditions in the transition stage of conditions,but also greatly advances the time points of risk identification,especially for the three common risk identification points of gas production,water production and drill sticking,which are advanced by 56,16 and 8 s.It has won valuable time for the site to take correct risk disposal measures in time,and fully demonstrated the applicability of nonlinear classification neural network in oil and gas field exploration and development.

Logging-while-drilling formation dip interpretation based on long short-term memory

Azimuth gamma logging while drilling(LWD)is one of the important technologies of geosteering but the information of real-time data transmission is limited and the interpretation is difficult.This study proposes a method of applying artificial intelligence in the LWD data interpretation to enhance the accuracy and efficiency of real-time data processing.By examining formation response characteristics of azimuth gamma ray(GR)curve,the preliminary formation change position is detected based on wavelet transform modulus maxima(WTMM)method,then the dynamic threshold is determined,and a set of contour points describing the formation boundary is obtained.The classification recognition model based on the long short-term memory(LSTM)is designed to judge the true or false of stratum information described by the contour point set to enhance the accuracy of formation identification.Finally,relative dip angle is calculated by nonlinear least square method.Interpretation of azimuth gamma data and application of real-time data processing while drilling show that the method proposed can effectively and accurately determine the formation changes,improve the accuracy of formation dip interpretation,and meet the needs of real-time LWD geosteering.

Characteristics of Late Quaternary Activity of the Luhuatai Buried Fault Revealed by Drilling

The Luhuatai fault is one of the important buried tectonics in the Yinchuan basin. Based on the results of shallow seismic exploration, we conducted composite drilling section exploration and dating of the samples from boreholes. Some useful data was obtained, such as the depth of the upper breaking point, the latest activity age, displacement in the late Quaternary, and slip rates, etc. This study shows that the activity is different between the north and south segment along the Luhuatai fault. The north segment is a Holocene fault, while the south segment is a late mid-Pleistocene fault. From north to south along the north segment of Luhuatai fault, the activity has been enhanced, and the faulting is stronger in late Pleistocene than Holocene.

Study on Acoustically Transparent Test Section of Aeroacoustic Wind Tunnel

On the purpose of accurate data acquisition for the aeroacoustic testing mostly in open jet test section of aeroacoustic wind tunnel, the large scale anechoic chamber is specifically designed to build the low background noise environment. A newly acoustic test section is presented in this paper, of which the contour is similar as the closed test section, and the wall is fabricated by the fiber fabric, both the characteristics of closed and open jet test section of conventional wind tunnel are combined in it. By thoroughly researching on the acoustics and aerodynamics of this acoustically transparent test section, significant progress in reducing the background noises in test section and improving the ratio of energy of the wind tunnel and some other aspects have been achieved. Acoustically transparent test section behaves better in acoustics and aerodynamics than conventional acoustic test section because of their high definition in detecting the sound sources and great performance in transmitting sounds.

Study on the Microscopic Identification of Chenopodium album L.

[Objectives]This study was conducted to identify the microscopic characteristics of Chenopodium album L.[Methods]The microscopic identification method was adopted.[Results]The xylem vessels and fiber bundles of the roots are arranged into 3-4 intermittent ring belts in a concave-convex pattern,alternately with the parenchymal cell ring belts;and the xylem rays vary in width.The cross section of the stems is polygonal;there are parenchyma cells at the corners of the cortex;there are many vascular bundles of varying sizes;and calcium oxalate clusters are common in the medulla and cortex.There are 2 to 4 vascular bundles in the main leaf vein,peltate;and fiber bundles exist below the phloem.The powder is characterized by numerous clusters of calcium oxalate,which are uniform in size,sharp at edges and corners.[Conclusions]This study can provide reference for the identification and quality standard of the crude drug.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

钻孔联合地质剖面探测施工中若干问题探讨

基于《活动断层探测》GB/T 36072—2018、《活动断层探查钻探》DB/T 92—2022等现行规范规程,对跨隐伏活动断层的钻孔联合地质剖面探测施工的基本要求和存在的主要问题进行了探讨,认为现行技术标准存在的问题有:(1)对岩芯采取率的要求偏低;(2)对横跨隐伏逆断层的钻孔深度和钻孔之间距离的要求偏低.提出了下列改进措施:(1)各类岩土的岩芯采取率应大于现行技术标准要求的数值;其中,黏土的岩芯采取率应不小于99%;(2)对横跨隐伏逆断层的钻孔深度应大于现行技术标准要求的孔深;在第四系厚度较薄时,在逆断层上断点附近,位于逆断层上盘的钻孔在钻遇上盘的前第四系后,还应继续钻进,钻孔深度应达到能够揭露逆断层下盘的前第四系的深度;(3)当隐伏逆断层在第四系中的断距较小时,逆断层上断点两侧的2个相邻钻孔间距就需要1~3 m甚至更小的数值.补充了钻孔联合地质剖面探测中的钻孔布设方式、隐伏断层产状的求取方法.分析了在钻孔联合地质剖面探测的断层识别中存在的一些不确定性因素,并提出了相应对策.指出钻孔联合地质剖面探测施工是基于动态设计的信息化施工,现场技术负责人及其施工现场技术管理在钻孔联合地质剖面探测工作中起着重要作用.

一种基于Section的水文地质钻孔综合图表绘制方法

将原始编录数据在Excel软件中进行整理,将整理后的数据通过Section软件的全部数据投影(Excel)、属性转为参数等功能达到快速制作水文地质钻孔综合图表的目的;该方法能充分利用Excel和Section的系统资源,大大缩短绘图时间,原始数据的成图避免了手工编辑的工程中可能造成的偏差,而且整个制作过程方便简洁,无需进行软件的二次开发,具有成图准确可靠、效率高等特点,实用性较强,可显著提高工作效率。

Similarity identification method on formational interfaces and application in general granite

An instrumented drilling system can be applied for the acquisition of drilling process parameters. The system can provide continuous and huge data for geotechnical engineering. However, due to the complexity of ground strata, the variation in the drilling parameters with stratigraphical characteristics is great and the correlation between likely comparable parameters is not high, which limits the use of conventional correlation approaches in this field. How to use the data for engineering and how to get a reasonable interpretation for the relationships among the drilling parameters, as well as between a drilling parameter and formational characteristics, become a technical choke point for the development and application of the instrumented drilling system. Based on similarity criteria, the extraction of sample data and characteristics, the pretreatment of data and feature matching algorithms have been analyzed and an approach of slope coefficient searching identification has been established. A case study was carried out for the similarity between the rotational speed of the drill bit, flushing pressure, and effective thrust force graphics in general weathered granite. The result shows that the similarity coefficients between the rotational speed of the drill bit, flushing pressure, and effective thrust force are 0.72 and 0.83, respectively. Although there are differences between the distances of the graphics, the curves of both rotational speed and flushing pressure agree with the effective thrust curve in shape, which provides a possible method for the identification of various formations by use of the similarity between feature drilling parameters.

Contribution of Geophysical Prospecting(Geoelectrical Method) for the Hydrogeological Reconnaissance of the Region of Ouled Youssef(Tadla's Basin,Morocco)

The Turonian aquifer of the Tadla’s basin shows at present a pronounced reduction of its hydraulic potential linked to overexploitation and deficiency of effective rains.In order to make an evaluation of the resources of water and implant the exploitation’s drillings of groundwater,a geophysical study by

39株指/趾甲分离黄曲霉复合体(Aspergillus section Flavi)菌种鉴定和抗真菌药物敏感性

目的对来自伊朗马什哈德地区(Mashhad,Iran)甲真菌病患者指/趾甲分离的39株黄曲霉复合体(Aspergillus section Flavi)菌株进行菌种鉴定及抗真菌药物敏感性研究。方法菌种鉴定采用β-tubulin/calmodulin基因测序的分子鉴定联合MALDI-TOF质谱鉴定;体外药物敏感性试验参照CLSI M38-A3方案检测包括特比萘芬、新三唑类药物、棘白菌素类等10种抗真菌药物。结果基于形态学鉴定的39株黄曲霉复合体(Aspergillus section Flavi)菌株中,分子鉴定结果表明38株为Aspergillus flavus(A.flavus)/Aspergillus oryzae(A.oryzae),1株为Aspergillus minisclerotigenes(A.minisclerotigenes)。对38株分子鉴定为A.flavus/A.oryzae菌株采用MALDI-TOF质谱鉴定结果为A.flavus,而MALDI-TOF质谱错误鉴定同形菌种A.minisclerotigenes为A.flavus。抗真菌药物敏感性结果表明,特比萘芬、泊沙康唑和棘白菌素类药物对黄曲霉复合体(Aspergillus section Flavi)菌种均具有抗真菌活性。结论分子鉴定联合MALDI-TOF质谱鉴定可准确鉴定黄曲霉复合体菌株到菌种水平,特别是分子鉴定不能区分A.flavus/A.oryzae,而MALDI-TOF鉴定可将二者区分。完善参考菌种数据库是MALDI-TOF质谱准确鉴定的关键。特比萘芬、泊沙康唑和棘白菌素类药物可作为治疗黄曲霉复合体(Aspergillus section Flavi)菌种所致甲真菌病的潜在有效药物。

隧道不良地质识别:方法、现状及智能化发展方向

随着隧道施工对于不良地质识别精度要求的不断提高以及人工智能技术的发展,融合多源信息的不良地质智能化识别已成为发展趋势。本文首先阐述了常见的6种隧道不良地质类型及其地质成因,回顾分析了隧道主要的不良地质识别方法及现状,详细介绍了笔者在不良地质智能化识别方面的探索性研究:基于机器学习利用图像识别技术对隧道围岩岩性与裂隙特征进行智能识别;融合图像和光谱特征进行不良地质识别;将地化分析融入到传统的超前钻探中,融合随钻参数和地化信息进行不良地质随钻识别,既可以发挥超前钻探在感知岩体质量和地层信息变化方面的优势,又可以发挥地化分析在岩性和不良地质异常识别方面的优势;基于地质与物探联合反演进行不良地质识别,旨在实现掌子面前方不良地质体“形”(位置、形态、规模)和“性”(性质和类型)的精确识别。最后,对隧道不良地质智能化识别的发展趋势进行了展望。

基于MobileViT的岩石薄片图像岩性识别方法研究

岩石薄片图像中包含了大量肉眼无法观察到的地质特征信息,对岩石薄片图像的岩性识别结果为后续的石油勘探和开发奠定了基础。针对岩性识别数据集不均衡、识别模型参数多等问题,提出一种改进的轻量化MobileViT模型,该模型针对涵盖了90%以上常见岩性的岩石薄片图像进行建模分析。首先,为使模型更好地学习到每类岩石薄片图像中所包含的独特特征,对数据集进行数字增加。其次,使用GELU替换MobileViT中MV2模块中常规ReLU6,从而作为该模块的激活函数,有效解决神经元死亡的问题,提升模型的收敛速度。最后,划分训练集和测试集,使用余弦退火算法自动更新学习率,以迁移学习加速训练过程,实现岩石薄片图像中针对岩性的自动识别。实验结果表明,改进后的MobileViT对岩性识别的准确率达82.9%,模型的参数仅为7.66M,通过实例验证该算法具有较好的鲁棒性。

矿物组分识别与智能解释在不同岩性之间的信息共享与迁移学习

在地球科学领域,岩石微观观测数据的采集过程繁琐且效率低下,这不仅增加了研究成本,降低了可靠性,同时也限制了数据的开源共享。此外,由于岩性的多样性和观测手段的差异,单一数据集的规模通常较小,这对于依赖大规模数据集的深度学习框架而言是一大挑战。为此,本研究探索迁移学习如何促进不同岩性间的信息共享,并通过此机制提高矿物组分识别与智能解释任务的模型性能。通过采集不同区域、岩性、矿物组分和偏光模式下的铸体薄片样本,本文深入研究了深度学习模型在不同观测对象和手段下的迁移学习机制,并聚焦于探索地质信息的深层表征。研究成果不但揭示了迁移学习在促进地质学领域信息共享与模型性能提升中的关键作用,还为自动化和智能化地质认识融合奠定了基础。实验结果显示,通过迁移学习,本文模型在智能解释任务中的准确率显著提高,从53.3%提高至98.73%,而在矿物组分识别任务中,准确率也实现了近10%的提升。这些成果证明了迁移学习在地质学领域内解决实际问题和提高模型泛化能力、性能和稳定性方面的巨大潜力。

不同勘探方法对浅层小断距隐伏断裂探测效果

采用浅层地震勘探、钻孔联合剖面勘探、槽探3种方法联合探测临汾盆地郭家庄断裂的空间位置,并研究其活动特征。结果表明,不同探测方法对断层信息的揭示存在很大差异,浅层地震勘探在探测具有一定断距的断层,同时地层存在一定物性差异的情况下能取得理想的效果,且不同的工作参数探测效果不相同;钻孔联合剖面勘探能提高对小断距断层的分辨率,但受地形及地层主观认别等因素的影响,可能会遗漏断距小、标识层薄的断层;槽探能较好地识别地表断层的微小断距,但受施工深度和施工条件影响。3种方法相结合,可以精确确定断裂的空间位置和活动特征。

黄河流域(山东段)村镇聚落文化景观基因识别指标体系构建

黄河流域(山东段)村镇聚落孕育了丰富独特的文化景观,建立识别指标体系对其文化景观基因进行挖掘整理,对在黄河流域生态保护和高质量发展战略背景下赓续传统文化具有重要意义。结合景观基因理论的研究成果和对国家有关政策法规文件的剖析,在历史景观视角下对黄河流域(山东段)的村镇聚落开展深入的文化景观田野调查、典籍研究等,提出具有针对性的文化景观基因识别指标体系构建逻辑,通过“识别对象—识别因子—识别指标”解析出13项识别因子、40项识别指标,为黄河流域村镇聚落文化景观识别全覆盖提供新的思路与方法。

玛西斜坡区风城组高含盐致密储集层流体识别

准噶尔盆地玛西斜坡区二叠系风城组发育碱湖沉积的高含盐致密储集层,其油水关系复杂,流体识别难度大。利用岩性扫描测井资料构建饱含盐水地层热中子宏观俘获截面,并利用测井热中子宏观俘获截面与饱含盐水地层热中子宏观俘获截面的差值构建含油性敏感因子,再利用氯元素相对产额与总孔隙度比值构建含盐度敏感因子,将含油性敏感因子与含盐度敏感因子交会形成流体识别图版。实例应用表明,利用该流体识别图版可以对储集层流体性质进行准确判断,为试油选层提供依据。