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岩石的研磨性是钻井过程中钻头的磨损预测及优化的重要因素。为了预测钻遇地层岩石的研磨性,建立了钻井过程中PDC复合片的磨损计算模型,从而得到了岩石研磨性的预测方法。首先,通过复合片与地层之间的受力分析并结合岩石的破碎条件,建立了不同钻压条件下地层对复合片的作用力计算模型。根据石英含量的概率密度分布情况,获得了岩石中参与磨损的颗粒与复合片底部的真实作用力。然后,根据PDC复合片磨损的几何原理,建立了地层对钻头复合片的磨损计算模型。通过室内实验对模型进行修正,分析了岩石各种属性对复合片磨损的影响规律,揭示了各参数影响复合片磨损的主次顺序依次为：弹性模量>石英含量>内摩擦角>表面粗糙度>泊松比>内聚力。基于该磨损模型建立了岩石研磨性评价指标,对制定了岩石研磨性的分级标准具有一定的借鉴意义。 相似文献
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通过机械比能对煤矿瓦斯抽采钻孔过程中的围岩进行可钻性分级,可为钻机调整钻进参数提供依据。针对瓦斯抽采钻孔过程中人工判层难度大、效率低的问题,提出一种以机械比能为可钻性评价指标,结合极限学习机的煤岩可钻性分级方法。采用ABAQUS建立了PDC钻头破岩仿真模型,从材料类型、钻头转速和钻压力三个方面研究了PDC钻头破岩过程中钻进速度和机械比能的变化规律。同时,获得了钻进参数及机械比能的训练数据,采用极限学习机分别对钻进参数和机械比能数据进行学习,最后,对这两种可钻性分级指标下的分级准确率进行对比。结果表明:以机械比能作为可钻性指标时的分级准确率达到90%以上,高于以钻进参数作为可钻性指标时的准确率。分级结果可以为钻机调整钻进参数、实现自适应钻进提供理论依据。 相似文献
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MDES2000型微钻实验装置可模拟真实钻进情况,可开展岩石研磨性与可钻性试验、金刚石钻头性能参数及寿命试验、优化钻进规程参数试验等室内的各种微钻实验研究工作。该实验装置可通过手动或程序控制完成模拟钻进工作,能够实现钻进过程中各项钻进参数（钻压、钻速、扭矩、转速及进尺）的监测、采集、处理及存储功能,还可建立钻进参数报表文件数据库。该实验装置对岩心钻探研究工作具有很好的实际指导意义。 相似文献
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近年来,南海西部海域部分油田的下第三系地层机械钻速较慢,严重影响了勘探开发进度,为了降低钻井成本,增加钻井时效,必须掌握该海域难钻地层的特征及破碎机制。首先开展了岩屑矿物组分测定,结合录井资料,掌握了南海西部海域难钻地层的岩性特征。在对难钻地层的破坏强度、硬度、塑性系数、可钻性、研磨性等相关参数室内试验的基础上,建立了该地层岩石力学参数的预测模型,并基于30口测井的资料建立了难钻地层抗钻特性剖面和南海西部海域三维可钻性剖面,揭示了区域难钻地层的分布情况及抗钻特性;为解决难钻地层的工艺技术难题,还进行了难钻地层PDC钻头的破岩机制试验研究,研究了PDC钻头牙齿齿形、钻压和转速对破岩效率的影响。研究发现：北部湾盆地建议提高钻头的攻击性,并采用高转速的动力钻具复合钻进方式;珠江口盆地应提高钻头复合片的耐磨性,采用较高钻压和合理顶驱转速,为更好地控制钻井成本和提高钻井综合经济效益提供技术指导。 相似文献
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钻机钻进过程中钻头旋转产生的扭矩以及作用于钻头的轴推进力是破碎岩石的能量来源,当钻进条件确定时,可用破碎单位体积岩石的实际能耗来反映岩石的物理力学性质。基于此原理在青岛胶州湾海底隧道FK4+375.5的上断面进行了超前地质探孔作业,得到凝灰岩地层中的钻进参数及钻进能量随钻头位移的变化曲线,并利用能量理论对钻进过程中监测的钻机参数进行分析,研究发现：在凝灰岩中,数字钻机参数与围岩岩性响应程度较高,围岩完整、坚硬、无裂隙水时,整体钻进参数值较高;围岩裂隙发育、含水或有断层、夹泥层时,钻进速度、推进力、转速、扭矩、打击能等数据会突变,其值变小。分析所得结果与钻孔取芯、TSP超前预报等物探手段得到的结果基本一致。并采用能量法对围岩进行分析,利用钻进比能划分相应的岩体区段,判断出围岩等级。通过对能量曲线的分析发现,在凝灰岩地层中,当钻进能量小于0.95 kJ时会出现断层或较大的节理裂隙区。 相似文献
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Omid Saeidi Seyed Rahman Torabi Mohammad Ataei 《Geotechnical and Geological Engineering》2013,31(5):1477-1495
Knowledge of drillability of rock masses in engineering projects is very important in determining drilling costs. In drilling operations, so many parameters such as the properties of rock and the drilling equipment affect the drilling performance. In this study, after discussing the rock mass drillability process and identifying all the effective parameters, interaction matrixes based on the rock engineering systems, that analyze the interrelationship between the parameters affecting rock engineering activities, is introduced to study the rock mass drillability tribosystem. Given that interaction matrix codes are not unique numbers, and then possible interactive intensities are calculated for each matrix and a group decision-making method, Fuzzy–Delphi–AHP technique has been used to obtain appropriate weights. As a result, rock mass drillability index (RMDI) is presented to classify the rock mass drillability. The results indicate the ability of this method to analyze rock mass drillability procedure. Drilling data along with laboratory rock properties from Sungun copper mine were collected and were ranked according to the new classification system. Fifteen zones at the mine site were ranked based upon the new index RMDI and a reasonable correlation was obtained between measured drilling rate at the zones and RMDI data. 相似文献
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《Geomechanics and Geoengineering》2013,8(1):53-61
In many rock engineering applications such as foundations, slopes and tunnels, the intact rock properties are not actually determined by laboratory tests, due to the requirements of high quality core samples and sophisticated test equipments. Thus, predicting the rock properties by using empirical equations has been an attractive research topic relating to rock engineering practice for many years. Soft computing techniques are now being used as alternative statistical tools. In this study, artificial neural network models were developed to predict the rock properties of the intact rock, by using sound level produced during rock drilling. A database of 832 datasets, including drill bit diameter, drill bit speed, penetration rate of the drill bit and equivalent sound level (Leq) produced during drilling for input parameters, and uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (ρ), P-wave velocity (Vp), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) of intact rock for output, was established. The constructed models were checked using various prediction performance indices. Goodness of the fit measures revealed that recommended ANN model fitted the data as accurately as experimental results, indicating the usefulness of artificial neural networks in predicting rock properties. 相似文献
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Characteristics of Water Ingress in Norwegian Subsea Tunnels 总被引：3，自引：1，他引：2
Bjørn Nilsen 《Rock Mechanics and Rock Engineering》2014,47(3):933-945
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This paper describes the results of the engineering geological investigations and rock mechanics studies carried out at the proposed Uru
Dam site. Analyses were carried out in terms of rock mass classifications for diversion tunnel, kinematic analysis of excavation slopes, permeability of the dam foundation and determination of rock mass strength parameters.Uru
Dam is a rock-filled dam with upstream concrete slab. The dam will be built on the Suveri River in the central part of Turkey. The foundation rocks are volcanic rocks, which consist of andesite, basalt and tuff of Neogene Age. Studies were carried out both at the field and the laboratory. Field studies include engineering geological mapping, intensive discontinuity surveying, core drilling, pressurized water tests and sampling for laboratory testing.Uniaxial, triaxial and tensile strength tests were performed and deformation parameters, unit weight and porosity were determined on the intact rock specimens in the laboratory. Rock mass strength and modulus of elasticity of rock mass are determined using the Hoek–Brown empirical strength criterion. Rock mass classifications have been performed according to RMR and Q systems for the diversion tunnel.Engineering geological assessment of the proposed dam and reservoir area indicated that there will be no foundation stability problems. Detailed geotechnical investigations are required for the final design of the dam. 相似文献
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围岩类别超前分类是隧道施工过程中必须开展的一项工作,其直接关系到后续的开挖及施工支护方案。为有效地进行隧道围岩类别超前分类,提出了基于数字钻进技术和量子遗传（QGA）-径向基函数（RBF）神经网络的围岩类别超前分类方法。以数字钻进技术为基础,从钻进参数中提取有用信息,构建围岩类别超前分类指标体系。采用量子计算原理对遗传算法进行改进,通过量子位编码和量子旋转门更新种群,以此来确定RBF神经网络的参数,建立了基于QGA-RBF神经网络的围岩类别超前识别系统。最后将该方法应用于青岛胶州湾海底隧道的围岩类别超前识别中,结果表明,该方法具有较高的准确性,其结果为围岩类别超前分类提供了一种新思路。 相似文献
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A model of tunnel boring machine performance 总被引：2，自引：0，他引：2
G. Wijk 《Geotechnical and Geological Engineering》1992,10(1):19-40
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Drillability indices, i.e., the Drilling Rate Index? (DRI), Bit Wear Index? (BWI), Cutter Life Index? (CLI), and Vickers Hardness Number Rock (VHNR), are indirect measures of rock drillability. These indices are recognized as providing practical characterization of rock properties used in the Norwegian University of Science and Technology (NTNU) time and cost prediction models available for hard rock tunneling and surface excavation. The tests form the foundation of various hard rock equipment capacity and performance prediction methods. In this paper, application of the tests for tunnel boring machine (TBM) and drill and blast (D&B) tunneling is investigated and the impact of the indices on excavation time and costs is presented. 相似文献
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钻头胎体硬度是一项重要技术指标,应与所钻岩石的硬度（可钻性）和研磨性相适应。胎体硬度直接影响钻探技术经济指标的提高和钻进工艺参数的选择。试验研究表明,钻头胎体硬度分布很不均匀,差别很大。首先应从胎体烧结工艺上找原因,烧结压力偏小（5 MPa）可能是首要原因。钻头、工艺参数、操作技术等都很重要,缺一不可。钻进技术经济指标低,不一定都是钻头质量问题,要从多方面进行研究。提倡把经验打钻提高到科学打钻上来。 相似文献
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岩石可钻性的分级是确定生产定额、设计钻头和选择最佳参数的依据。本文论述了作者在实验台上用微钻法进行岩石分级的研究成果。该实验台是自行设计并可以同时自动记录9个钻进参数的一种装置,微钻法可以很好地表示在两向力同时作用下,岩石破碎过程的主要力学特征。岩石抗破碎阻力是岩石机械性质的综合反映,因此,以往用单一的性质来进行分级并不完善,用微钻法进行分级、经野外实践,符合率可达85％以上。 相似文献
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This paper demonstrates the applicability of cognitive systems or neural networks in predicting the drillibality of rocks
and wear factor using engineering properties of rocks. Drillability of rocks is a useful guide for evaluating the suitability
of drills for different ground operations. The wear factor of different materials subsequently helps in the selection of proper
drills for different drilling operations. Different rocks were tested for Protodyakonov index, impact strength index, shore
hardness number, Schmidt hammer number, drillability and micro bit chisels for wear factor. The data obtained from the tests
were used to train and test the neural network. Results from the analysis demonstrate that cognitive systems are an effective
tool in the prediction and suitability of drilling operations. Application of these predictive models can be a useful tool
to obtain the value of these important parameters, they can save time and help to avoid the tedious process of instrumentation. 相似文献