序贯指示模拟方法在煤层顶板岩性预测中的应用

渭北石炭二叠纪煤田煤层顶板横向非均质性强,依靠钻探所得煤层顶板岩性误差较大.地震资料具有较高的纵向、横向分辨率,利用地震资料可以提高煤层顶板岩性预测精度.本文采用序贯指示模拟方法进行煤层顶板岩性预测.通过分析煤层顶板岩性资料,利用数据空间变异性特征和相应的地震属性求取变差函数,将井点资料作为硬数据,地震数据作为软约束进行煤层顶板岩性预测.综合运用地质资料、地震资料,为煤层顶板岩性预测提供了一种有效的方法.     

多参数岩性地震反演在识别煤层顶板砂岩中的应用——以新景煤矿为例

山西国阳新能股份有限公司新景煤矿3煤层在掘进和回采过程中,频发煤与瓦斯突出事故,给煤矿安全生产带来隐患.通过多个采区的瓦斯地质调查,发现大部分区域的瓦斯突出与煤层的顶板岩性(砂岩)和煤层结构(煤层的冲刷变薄)密切相关,当3煤层被顶板砂岩冲刷变薄,即3煤层顶板砂岩的厚度增加时,极易发生瓦斯突出.本文对研究区的测井曲线进行特征分析,并对测井曲线进行融合处理,利用多参数岩性反演的方法获得的波阻抗数据体可以对3煤层顶板砂岩的分布特征进行详细研究.反演结果与实际情况吻合度较高,且在瓦斯突出点位置,煤层顶板的波阻抗值明显增大,并能够清晰地辨别砂岩的位置和空间形态,其研究成果对新景煤矿瓦斯预测和治理具有重要意义.     

利用地震属性技术研究岩溶裂隙发育带

利用地震属性技术,从三维地震数据体中提取对岩溶裂隙发育带相关的二维分形参数,预测岩溶裂隙发育带、奥灰顶部岩层的富水性以及有关的水文地质条件,为高产高效矿井地质保障系统提供依据。通过许厂煤矿430采区实际资料的检验,预测的地质效果良好。     

东营凹陷王庄--宁海地区馆陶组储层预测

在研究地质资料、测井资料的基础上,针对胜利油田王庄宁海地区馆陶组地层的变化特征规律,制定了新的储层预测技术方法.（1）精细构造技术研究：利用原始地震资料、地震道积分资料及小波分解资料,结合地质平面层序地层单元分析方法,划分出地质单元边界.（2）反演测井声波时差技术研究：利用地震小波变换属性、测井声波时差资料,采用稳健回归方法直接反演声波时差,进而计算孔隙度.该方法,可提高反演的分辨率,增加了反演结果的可靠性.结合研究区地质资料,把这种研究技术方法应用到王庄宁海地区馆陶组储层的预测研究,取得了良好的效果.     

基于地震属性的煤层火成岩侵入预测

煤层火成岩侵入给煤矿生产以及经济效益带来了极大的影响,属于亟待解决的问题.本文充分发挥测井信息的作用,基于测井数据建立正演模型,获得不同侵入模型叠加记录,并提取多种地震属性;利用灰色关联和模糊聚类方法对提取的地震属性进行分类和优化,得到与地质目标相关性较好,且相互独立的4种地震属性;利用井旁道地震记录和井信息作为BP神经网络的学习样本进行训练,在训练好的BP神经网络中输入从地震数据中提取的优化后的地震属性,预测煤层火成岩侵入区的分布情况.从实际测区的预测情况看,该方法准确性和可靠性较高,可对实际生产进行理论指导.     

煤层冲刷带地震响应特征及其与煤层瓦斯突出的相关性分析

阳泉新景煤矿生产实践发现3#煤层发生的煤与瓦斯突出事故大部分位于煤层冲刷带.三维地震勘探记录地层连续性好、控制精度高,是识别煤层冲刷带最常用的技术手段.本文以新景矿芦南二区为研究区,建立二维正演模型,研究煤层的地震响应规律,发现其振幅和相位属性对煤层冲刷带响应明显.利用该区现有的三维地震资料,综合瞬时振幅属性特征、地震相分类特征及声波阻抗响应特征,圈定冲刷带平面位置及分布范围.运用有限元分析软件对煤层冲刷带应力分布进行三维正演模拟,结合煤样测试结果,对区内应力异常带的煤层瓦斯和煤体结构等进行分析.研究发现煤层冲刷带过渡区段突出危险性最大,是圈定冲刷带的关键部位,研究成果与实际突(喷)出点位置吻合度较高.总之:1)煤层冲刷作用是导致瓦斯地质条件差异性的主要因素之一,过渡区段为重点防突区域;2)综合多种地震岩性信息响应特征在准确识别煤层冲刷带位置及分布范围方面效果显著.     

基于FCM聚类约束的直流电阻率法与地震走时成像法二维联合反演

通过引入模糊均值聚类(FCM)模型约束函数对电阻率与速度进行约束,开展二维直流电阻率法与地震初至波走时成像法联合反演研究.在地下浅层结构勘探中,通常低电阻率的地质体具有低速特征,较高电阻率的地质体表现为较高的地震波速度.直流电阻率法因为低电阻率区域吸引电流而对其敏感,地震走时成像法因为射线集中在高波速区而对高速体敏感,因此,两者联合成像能够大幅度提高反演效果.合成数据反演表明,直流电阻率法和地震初至波走时联合反演对于两类地质体的分辨能力均有提升,能够优势互补.尤其是引入FCM模型约束进行联合反演,根据已知物性进行监督学习,进一步提高了反演质量,改善了成像模型的分辨率.     

波阻抗反演预测奥陶系灰岩顶部含隔水性

地震反演技术是岩性地震勘探的重要手段之一,其中地震井约束波阻抗反演技术是利用钻孔测井数据高的纵向分辨率,对井旁地震资料进行井约束反演,然后在此基础上对井间地震资料进行反演,推断地层岩性在空间上的变化情况.本文根据新驿煤矿首采区的三维地震和测井资料,利用波阻抗反演技术预测该区奥陶系灰岩顶部的含隔水性,并依据波阻抗值的大小圈定含水层中的可能富水区.     

NBR油田优质储层叠前叠后联合反演与应用

开展NBR油田地震优质储层预测,可有效支撑低效井区开发和外扩区储量动用,对油田产能建设有重要意义.本文针对地震反演存在叠后反演参数单一、叠前反演多解性强的问题,提出三步流程实现叠前叠后联合反演,达到优质储层定量表征目的.步骤一通过叠后地质统计学反演预测砂泥岩;步骤二开展岩石物理分析,明确了剪切模量为含油砂体敏感弹性参数,再以步骤一反演结果为约束,采用叠前扩展弹性阻抗反演(EEI)预测储层含油性;步骤三仍以步骤一结果为约束,采用高斯配置协模拟测井参数反演预测孔隙度、渗透率.最后按照优质储层物性界限,通过剪切模量、孔隙度和渗透率体得到优质储层三维表征体.上述方法在NBR油田A组油层中优质储层预测符合率达到78.3%,优选甜点区部署水平井效果显著.     

测井约束与神经网络联合反演储层预测技术

目前地震反演方法广泛应用于储层预测,并已取得重要进展.常规反演技术以测井数据为约束,有效结合测井资料与地震资料的优势频段信息,根据反演结果波阻抗的差异区分储层.但在复杂地质条件下薄储层砂泥岩波阻抗信息较为接近,反演结果难以满足储层精细刻画要求,为此在已有波阻抗信息的基础上进行神经网络岩性反演,将波阻抗等地震属性转化为与岩性更密切的伽玛数据体,使地震反演的地质属性与测井标定的地质属性达到最大程度的相关,从而提高对簿储层的识别能力.实际资料的应用效果表明,该方法较为准确地实现了对簿储层的精细刻画,为非构造油气藏的勘探提供了重要依据.     

应用三分量地震数据反演煤系地层孔隙含水量

地层孔隙水含量的计算主要受地层孔隙度与孔隙含水饱和度的影响.通过实验室煤层及其顶底板岩芯物理测试与孔隙度测量,可以获得煤系地层岩芯的孔隙度与岩芯纵、横波速度的经验公式;结合三分量地震勘探获得的纵波与横波速度,能够计算出煤系地层的孔隙度;含水饱和度的计算通过实验室岩电参数测试和阿尔奇公式可以计算得到.结合淮南某矿区三维三分量地震勘探和地球物理测井及岩样的岩电试验,实现了三维空间煤系地层孔隙含水体积百分比的精确计算.该预测方法与传统的、钻孔控制的地下水评价方法相比可以提供相对精细的、三维空间的、定量化的煤系地层孔隙相对含水量,对煤炭与煤层气资源开采中所涉及的地下水分布规律预测有一定的参考意义.     

综合勘探方法在解决地质问题中的应用

三维地震勘探在解决诸如小断层、小构造、煤矿采区陷落柱等方面的应用日益广泛。然而,由于地质条件的复杂性以及各种物探方法的特点不同,有时三维地震勘探方法需要与其它物探方法相结合才能更好地发挥其作用。本文以济三煤矿十四采区为例,介绍了利用三维地震和瞬变电磁法勘探相结合,探测采区的断层及煤层顶板上100m内的岩层富水体的方法、特点及经验。     

Dispersion features of transmitted channel waves and inversion of coal seam thickness

In-seam seismic survey currently is a hot geophysical exploration technology used for the prediction of coal seam thickness in China. Many studies have investigated the relationship between the group velocity of channel wave at certain frequency and the actual thickness of exposed coal beds. But these results are based on statistics and not universally applicable to predict the thickness of coal seams. In this study, we first theoretically analyzed the relationship between the depth and energy distribution of multi-order Love-type channel waves and found that when the channel wave wavelength is smaller than the thickness of the coal seam, the energy is more concentrated, while when the wavelength is greater than the thickness, the energy reduces linearly. We then utilized the numerical simulation technology to obtain the signal of the simulated Love-type channel wave, analyzed its frequency dispersion, and calculated the theoretical dispersion curves. The results showed that the dispersion characteristics of the channel wave are closely related to the thickness of coal seam, and the shear wave velocity of the coal seam and its surrounding rocks. In addition, we for the first time realized the joint inversion of multi-order Love-type channel waves based on the genetic algorithm and inversely calculated the velocities of shear wave in both coal seam and its surrounding rocks and the thickness of the coal seam. In addition, we found the group velocity dispersion curve of the single-channel transmitted channel wave using the time–frequency analysis and obtained the phase velocity dispersion curve based on the mathematical relationship between the group and phase velocities. Moreover, we employed the phase velocity dispersion curve to complete the inversion of the above method and obtain the predicted coal seam thickness. By comparing the geological sketch of the coal mining face, we found that the predicted coal seam thickness is in good agreement with the actual thickness. Overall, adopting the channel wave inversion method that creatively uses the complete dispersion curve can obtain the shear wave velocities of the coal and its surrounding rocks, and analyzing the depth of the abruptly changed shear wave velocity can accurately obtain the thickness of the coal seam. Therefore, our study proved that this inversion method is feasible to be used in both simulation experiments and actual detection.     

3D inversion of DC data using artificial neural networks

In this paper, we investigate the applicability of artificial neural networks in inverting three-dimensional DC resistivity imaging data. The model used to produce synthetic data for training the artificial neural network (ANN) system was a homogeneous medium of resistivity 100 Ωm with an embedded anomalous body of resistivity 1000 Ωm. The different sizes for anomalous body were selected and their location was changed to different positions within the homogeneous model mesh elements. The 3D data set was generated using a finite element forward modeling code through standard 3D modeling software. We investigated different learning paradigms in the training process of the neural network. Resilient propagation was more efficient than any other paradigm. We studied the effect of the data type used on neural network inversion and found that the use of location and the apparent resistivity of data points as the input and corresponding true resistivity as the output of networks produces satisfactory results. We also investigated the effect of the training data pool volume on the inversion properties. We created several synthetic data sets to study the interpolation and extrapolation properties of the ANN. The range of 100–1000 Ωm was divided into six resistivity values as the background resistivity and different resistivity values were also used for the anomalous body. Results from numerous neural network tests indicate that the neural network possesses sufficient interpolation and extrapolation abilities with the selected volume of training data. The trained network was also applied on a real field dataset, collected by a pole-pole array using a square grid (8 ×8) with a 2-m electrode spacing. The inversion results demonstrate that the trained network was able to invert three-dimensional electrical resistivity imaging data. The interpreted results of neural network also agree with the known information about the investigation area.     

基于正演模拟的煤层采空区地震响应特征

上组煤采空区对于下组煤的成像和安全回采有着重要影响,有必要研究矿区上组煤采空区精准成像技术.对于实际采空区及其下组煤的地震响应特征预测来说,建立和实际地质情况相吻合的正演模型是基础.根据搜集到的研究矿区钻孔测井资料,获取建模所需的等效地层物性参数、采空区垮落带等效模量、裂隙带等效模量等数据,建立与研究矿区地质情况比较接...     

An application of 4D seismic monitoring technique to modern coal mining

To study the impact of modern coal mining on the overlying formation, a full‐life‐cycle four‐dimensional seismic monitoring study has been carried out. Four seismic data campaigns have been performed using flexi‐bin geometry with square bins, with total duration of 171 days. The four seismic datasets have been processed with the same processing workflow and parameters; major problems such as statics correction, signal‐to‐noise ratio, resolution, and consistency processing are addressed taking into account the geological features of the research area. This guarantees that remaining four‐dimensional differences between the time‐lapse datasets show mostly geological factors due to the coal mining and effects such as surface subsidence. Our four‐dimensional seismic monitoring of modern coal mining shows that mined and unmined areas have significant zoning characteristics; coal mining has a direct impact on the overlying formation. The mining leads to obvious event subsidence, which reflects that overlying formations undergo subsidence during the mining process. The overlying formation appears as two zones called caving zone and fractured zone. We determine the fault dip of the overlying formation at one end of the working face to be 56°or so by calculation and conversion. We also see that, during the coal mining process, over time, the overlying formation has a self‐recovery capability, which gradually strengthens from the roof siltstone upward to the Aeolian sandstone near the surface. The stability of 20‐m coal pillars between working faces displays a strengthening trend and remains safe during the mining process due to both coal seam supporting and formation compaction effects.     

利用多源信息方法预测煤矿水害

煤层顶底板突水是一种受许多因素控制的动态现象,其影响因素是多种多样的。本文将地球物理技术与GIS技术进行有机结合,利用地理信息系统作为平台,把三维地震、电法、构造地质、水文地质等多源信息进行融合,综合确定各要素属性特征及其相互联系,突出有用的专题信息,建立考虑各种影响因素的煤矿水害预测模型,对煤矿采区的突水危险程度做更为科学、准确的预测。     

矿井全空间孔中瞬变电磁响应积分方程法数值模拟研究

基于地面-钻孔瞬变电磁法,将发射源布设于煤矿巷道中,而将接收探头放置在钻孔内,从上至下依次观测,形成矿井巷道-钻孔瞬变电磁法.为了研究巷道全空间条件下巷道-钻孔瞬变电磁场的响应特征,选取煤层底板受水害威胁的代表性地层为研究对象,建立煤层底板存在含水低阻地质异常体的三维数学模型,采用积分方程法进行数值模拟,结果表明：相对高阻的煤层侧帮对孔内垂向感应电动势的影响较小,而对孔内水平感应电动势除浅部约30 m内影响相对较大外,对深部影响较小.当煤层底板含水平低阻板状异常体时,在对异常体的纵向分辨率和异常响应的延续时间方面,孔内感应电动势的水平分量相对优于垂向分量,但垂向感应电动势的幅值强于水平感应电动势.因此,实际观测时,不仅要观测孔内感应电动势的垂向分量,也要观测水平分量.     

The Influence of a Local Fault Zone on High Energy Tremor Occurrence During Longwall Mining of a Coal Seam

Underground mining of coal seams in the Upper Silesian Coal Basin in Poland is accompanied by seismic activity of varying magnitude. The investigations which have been performed for several years distinguished high energy mine tremors connected directly with mining or coupled with geological structures, such as large faults. In mined seams, local fault zones occur. Faults in these zones are usually small, with throws comparable with coal seams thicknesses. Local fault zone may be responsible for the occurrence of high energy tremors as well as large faults, as presented in this article. An analysis of source mechanism of high energy tremors generated during longwall mining of the coal seam No. 510, with presence of a local fault zone, in one of the Polish hard coal mines in the Upper Silesian Coal Basin was performed. For this purpose, the seismic moment tensor inversion method was used. In most of foci, the process of shear predominated. Determined nodal plane parameters were correlated with parameters of faults forming the local fault zone. High energy tremors were generated mostly by dislocations on faults of the local fault zone. Weakening of roof rocks in the neighborhood of local fault zone takes an important role too, and was responsible for share of implosion in the focal mechanism.     

Inversion of DC resistivity data using neural networks

The inversion of geoelectrical resistivity data is a difficult task due to its non-linear nature. In this work, the neural network (NN) approach is studied to solve both 1D and 2D resistivity inverse problems. The efficiency of a widespread, supervised training network, the back-propagation technique and its applicability to the resistivity problem, is investigated. Several NN paradigms have been tried on a basis of trial-and-error for two types of data set. In the 1D problem, the batch back-propagation paradigm was efficient while another paradigm, called resilient propagation, was used in the 2D problem. The network was trained with synthetic examples and tested on another set of synthetic data as well as on the field data. The neural network gave a result highly correlated with that of conventional serial algorithms. It proved to be a fast, accurate and objective method for depth and resistivity estimation of both 1D and 2D DC resistivity data. The main advantage of using NN for resistivity inversion is that once the network has been trained it can perform the inversion of any vertical electrical sounding data set very rapidly.