一种新的岩石可钻性分级方法研究

提出了用岩石Ａ、Ｂ值来表示岩石可钻性级别的新观点,研究了岩石Ａ、Ｂ值测定方法和原理;用岩石Ａ、Ｂ值来指导钻头配方设计是可行的。     

Stress and energy reflection from impact on rocks using different indentors

Experiments have been conducted using an impact method with drop-hammers to obtain the required signals so that a detailed analysis of these data could be undertaken to quantify the drillability index of rocks. The dynamic stress waveform or reflected energy from the rock-drill bit interface is represented as a drillability index. This information enables the assessment of the characteristics of the rock under the bit. The results show that neither the amplitude nor the frequency spectra of the reflected stress wave significantly changes with the type of rock tested. However, the energy reflection coefficient varies with rock hardness. In the process of drilling the rocks, the energy reflection coefficient can increase by approximately 20% when the hardness or penetration resistance index of rock is doubled. This investigation confirmed the variation of reflected energy of rocks. For a single sharp or domed indentor, energy reflection decreases with penetration resistance index of the rock while it increases with this index in actual drilling or testing of percussive bits. In drill bit design and in tests for using a single indentor, a reasonable indentor shape should be adopted to correspond to a high energy reflection coefficient. By contrast, the energy reflection from a drill system or percussive bit should be minimised in practice.     

Evaluation of drill cuttings in prediction of penetration rate by using coarseness index and mean particle size in percussive drilling

Penetration rate of rocks is influenced by geological parameters, machine parameters and operating parameters. This paper presents a study on the coarseness index (CI) and mean particle size (d) to evaluate the penetration rate (PR) in percussive drilling in a limestone and in a marl quarry. The coarseness index was determined from sieve analysis. The mean particle size (d) and specific surface area (SSA) were determined by using Rossin-Rammler-Sperling (RRS) graph for each hole samples. The relationships between PR and CI, d, SSA were investigated by regression analysis. The best lines were selected in these graphs. Reliable relations were found between penetration rate and coarseness index and mean particle size. Finally, it was seen that the coarseness index and mean particle size could be used in evaluating the penetration rate in percussive drilling operations. This revised version was published online in July 2006 with corrections to the Cover Date.     

煤系钻探岩石可钻性分级方法的研究

研究了煤系中采用硬质合金钻进工艺时岩石可钻性分级的指标体系及测试方法;研制了一套能在野外流动条件下进行岩石可钻性分级测试的轻便仪器及工具。在7项分级指标体系中,压入强度、研磨性、塑性系数和用微型针状硬质合金钻头测试“微钻钻速”是首次应用,尤以用小试样测试“压入强度”及用微型针状硬质合金钻头测试“微钻钻进速度”两项指标能有效地反映煤系软岩石分级档次,可以弥补用微型孕镶金刚石钻头在6级以下较软岩石中无法钻进或达不到钻进指标的不足。     

实钻条件下井底岩石可钻性预测模型研究

标准岩石可钻性是在室内常压下测定的,主要反映岩石固有的力学性质,没有考虑围压对于可钻性的影响。实际钻井条件下岩石受到井底各种应力的作用,其可钻性会发生变化。根据对可钻性测定原理的分析,牙齿吃入深度是影响可钻性的重要因素,基于摩尔-库仑准则,在考虑岩石本身的力学性质和岩石围压的基础上,建立了牙齿吃入深度计算公式。通过对某油田岩芯进行可钻性试验测定,并计算出试验条件下各岩芯的牙齿吃入深度,经过拟合发现,两者可以建立较为密切的关系。由于牙齿吃入深度的计算公式中包含围压以及岩石的力学性质两方面的影响,因此,可以利用计算吃入深度来预测实际钻井条件下的岩石可钻性。     

基于优化的BP神经网络地层可钻性预测模型

提出了一种粒子群算法（PSO）优化的BP网络模型预测地层可钻性的新方法。利用粒子群算法优化BP网络模型的参数,避免了BP网络陷入局部极小值的缺点,提高了模型的预测速度和精度。结合钻探实例,利用测井资料和地层可钻性级别的关系建立了可钻性级别实时预测模型,并将该模型与传统的BP网络进行对比,结果表明,该模型优于BP网络,具有较高的精度和较快的收敛速度,有一定的适用性。