Characterization of reservoir properties and pore structure based on micro-resistivity imaging logging: Porosity spectrum, permeability spectrum, and equivalent capillary pressure curve

According to the capillary theory,an equivalent capillary model of micro-resistivity imaging logging was built.On this basis,the theoretical models of porosity spectrum(Ф_(i)),permeability spectrum(K_(i))and equivalent capillary pressure curve(pe)were established to reflect the reservoir heterogeneity.To promote the application of the theoretical models,the Archie's equation was introduced to establish a general model for quantitatively characterizing bi,K,and pei.Compared with the existing models,it is shown that:(1)the existing porosity spectrum model is the same as the general equation of gi;(2)the Ki model can display the permeability spectrum as compared with Purcell's permeability model;(3)the per model is constructed on a theoretical basis and avoids the limitations of existing models that are built only based on the component of porosity spectrum,as compared with the empirical model of capillary pressure curve.The application in the Permian Maokou Formation of Well TsX in the Central Sichuan paleo-uplift shows that the Ф_(i),K_(i),and p_(ci) models can be effectively applied to the identification of reservoir types,calculation of reservoir properties and pore structure parameters,and evaluation of reservoir heterogeneity.

Discrimination of Natural Fractures Using Well Logging Curve Unit

It is very difficult to discriminate natural fractures using conventional well log data, especially for most of the matured oilfields in China, because the raw data were acquired with relatively obsolete tools. The raw data include only GR and SP curves, indicative of lithology, AC curves, used to calculate the porosity of the formation, and a set of logging curves from various electrode length resistivity by laterolog. On the other hand, these oilfields usually have a large amount of core data which directly display the characteristics of the formation, and enough information of injection and production. This paper describes an approach through which logging curves are calibrated in terms of the raw data, and then a prototype model of natural fractures is established based on the investigation of core data from 43 wells, totaling 4 000 m in length. A computer program has been developed according to this method. Through analysis and comparison of the features of logging curves, this paper proposes a new concept, the well logging curve unit. By strictly depicting its shape through mathematical methods, the natural facture can be discriminated. This work also suggests an equation to estimate the probability of fracture occurrence, and finally other fracture parameters are calculated using some experimental expressions. With this methodology, logging curves from 100 wells were interpreted, the results of which agree with core data and field information.

The quadratic B-spline curve fitting for the shape of log cross sections

This paper describes a new method for simulation of the cross section shape of log. The self-developed MQK3102 log shape recognizing machine was used to acquire the finite discrete sampling points on the cross section of log and those points were fitted with the quadratic B-spline parametric curve. This method can clearly stimulate the real shape of the log cross section and is characterized by limited sampling points and high speed computing. The computed result of the previous curve does not affect the next one, which may avoid the graphic distortion caused by the accumulative error. The method can be used to simulate the whole body shape of log approximately by sampling the cross sections along the length direction of log, thus providing a reference model for optimum saw cutting of log.

A new method to construct reservoir capillary pressure curves using NMR log data and its application

By analyzing hundreds of capillary pressure curves, the controlling factors of shape and type of capillary pressure curves are found and a novel method is presented to construct capillary pressure curves by using reservoir permeability and a synthesized index. The accuracy of this new method is verified by mercury-injection experiments. Considering the limited quantity of capillary pressure data, a new method is developed to extract the Swanson parameter from the NMR T2 distribution and estimate reservoir permeability. Integrating with NMR total porosity, reservoir capillary pressure curves can be constructed to evaluate reservoir pore structure in the intervals with NMR log data. An in-situ example of evaluating reservoir pore structure using the capillary pressure curves by this new method is presented. The result shows that it accurately detects the change in reservoir pore structure as a function of depth.

Fractal Reconstruction of Log Curve

Sedimentary cyclothems at different scales show formations’ fractal structure which can be reflected on logs. The slope of the power spectrum of log is related to the fractal dimension of formations. The fractal dimensions from two logs with similar vertical resolutions are the same. Using fractal interpolating algorithm density log can be reconstructed. The reconstructed log can be compared with core density in washout intervals.

Studies on phase and group velocities from acoustic logging

It is still argued whether we measure phase or group velocities using acoustic logging tools. In this paper, three kinds of models are used to investigate this problem by theoretical analyses and numerical simulations. First, we use the plane-wave superposition model containing two plane waves with different velocities and able to change the values of phase velocity and group velocity. The numerical results show that whether phase velocity is higher or lower than group velocity, using the slowness-time coherence （STC） method we can only get phase velocities. Second, according to the results of the dispersion analysis and branch-cut integration, in a rigid boundary borehole model the results of dispersion curves and the waveforms of the first-order mode show that the velocities obtained by the STC method are phase velocities while group velocities obtained by arrival time picking. Finally, dipole logging in a slow formation model is investigated using dispersion analysis and real-axis integration. The results of dispersion curves and full wave trains show similar conclusions as the borehole model with rigid boundary conditions.

Study of real-time LWD data visual interpretation and geo-steering technology

LWD（logging while drilling） data has been used to explore complex subtle reservoirs by realtime visual interpretation and geo-steering.The method comprises of computer communication,well log data processing,formation recognition,reservoir modeling and model updating in real time.We studied the key technologies related to real-time LWD data visual interpretation and geo-steering and developed computer software with Chinese intellectual property rights covering the following important aspects： 1） real-time computer communication of well site LWD data;2） visualization of geological model and borehole information;3） real-time interpretation of LWD data;4） real-time geological model updating and geo-steering technology.We use field application examples to demonstrate the feasibility and validity of the proposed technologies.

Linearly optimum seeking K-value in the fitting of exponential and geometric curves

Based on logarithmic transformation of exponential and geometric curves, the paper proposes a method of choosing k-value to improve the goodness of fit. Functions between the sum of squares of regression(SSR) and k-value are built up; and the maximum of SSR value can be solved by derivation in which an iterative algorithm is used. A computer program has been developed for application. The result shows that the method is simple, and the goodness of fit is not inferior to other optimum methods of curve fitting.

基于小波变换和CNN-Transformer模型的测井储层流体识别

针对具有复杂储集空间和极强的非均质性的低孔低渗储层,常规测井响应特征不够明显,使用传统解释手段难以有效识别储层流体的问题,提出了一种基于小波变换和CNN-Transformer混合模型的储层流体识别方法。首先,使用小波变换将测井信号从时域扩展到时频域,并生成时频谱图以增强信号特征,然后使用滑动时窗沿着测井曲线深度方向滑动采样,获取代表解释深度处地层信息的频谱特征图,最后,通过训练CNN-transformer模型深度挖掘特征图信息,实现储层流体识别。混合模型在利用储层对应深度处测井数据的同时,又兼顾测井曲线随深度的变化趋势和地层前后信息的关联性,挖掘时频谱图的局部细节和全局特征表示,自动识别流体类型。将模型应用于大港油田22口实测测井资料中,并与CNN和BiLSTM等多个模型的流体识别效果进行对比分析,基于小波变换和CNN-Transformer模型识别效果明显优于其他方法,在测试集上识别准确率达到了92.7%。研究结果表明该方法可以作为低孔渗油藏常规测井资料识别储层流体的有效手段,为流体评价提供了新思路。

基于Stacking集成学习的声波时差测井曲线复原研究

声波时差测井曲线在石油勘探中发挥着不可或缺的作用,但是受地质或仪器的影响,经常会出现部分甚至完整的声波测井曲线缺失的情况。针对这一问题,提出了一种基于Stacking集成学习的声波时差测井曲线复原方法,该模型使用随机森林(RF)、梯度提升决策树(GBDT)、轻量梯度提升机(LightGBM)和极限梯度提升(XGBoost)作为基学习器,支持向量回归(SVR)作为元学习器,同时采用5折交叉验证的方法。实验选取了大庆油田某区块的实际测井数据,分别进行了同井和异井间的缺失声波时差测井曲线复原实验,结果表明,所提方法比单一模型预测更加准确,验证了此方法的可行性。

压裂施工曲线应用于深层页岩气水平井裂缝评价

页岩气水平井通常没有取芯、电成像等资料,增加了裂缝识别难度,而压裂施工曲线能较好地反映出压裂效果和压前天然裂缝的发育情况。以川南泸州Y101区块深层页岩气地层为例,根据携砂液阶段压裂施工曲线类型及特征刻度水平井压前裂缝层段、基质层段的测井响应特征,利用小波变换提取反映裂缝信息的高频微弱信号建立小波变换综合系数(W_(Ⅰ))、交会图优选6项对裂缝响应敏感的参数指标、层次分析法与模糊数学法构建裂缝评价综合系数(F_(Ⅰ)),并进一步结合不同压裂施工曲线类型与测井信息的关系,实现了水平井裂缝层段识别及发育级别评价。研究结果表明,携砂液阶段压裂施工曲线形态有下降型(Ⅰ)、下降稳定型(Ⅱ)、稳定型(Ⅲ)及上升型(Ⅳ)4种类型,其中,当自然伽马高值、补偿中子和密度明显增大、声波时差增大伴“锯齿状”周波跳跃、双侧向呈“正差异”且W_(Ⅰ)≥0.18,F_(Ⅰ)≥0.430时,压裂施工曲线为下降型,裂缝发育好(F-A级);0.390≤F_(Ⅰ)<0.430时,既有下降型又有下降稳定型,裂缝较发育(F-B级);0.350≤F_(Ⅰ)<0.390时,为下降稳定型,裂缝一般发育(F-C级)。

基于主成分自组织神经网络法的测井曲线分层技术

在砂岩型铀矿找矿工作中,提高测井岩性分层效率和精度至关重要。为提高砂岩型铀矿岩性分层效果,本文采用主成分分析法对多个测井曲线进行降维处理,将主成分分析法的第一主成分、第二主成分、第三主成分作为自组织神经网络的样本数据,进行自组织神经网络训练,将训练好的网络模型用于砂岩型铀矿岩性的自动化分层。实验结果显示:主成分自组织神经网络法岩性分层精度可达到85%以上,高于传统自组织神经网络算法78%的分层精度,具有更好的测井岩性分层效果。因此,主成分自组织神经网算法的岩性分层方法有效减少了输入样本的种类,简化了自组织神经网络结构,其自动化分层效果要优于传统的自组织神经网络算法。本文的研究结果表明,主成分自组织神经网算法在砂岩型铀矿领域岩性识别工作中具有较好的应用效果。

基于深度学习的栅格测井曲线数字化研究

栅格测井曲线是记录测井数据的模拟曲线,地质专家可根据所得曲线判断出具体岩性、层位等.目前无监督计算机视觉技术解释数字曲线算法中仍然存在对人工干预有依赖,处理时间较慢且准确率不高的问题,提出了一种基于深度学习的栅格测井曲线数字化研究方法.该方法采用改进的U⁃net启发架构作为基础模型,结合栅格测井图像分辨率高的特性改变上下采样的残差块数量,并利用注意力增强的处理策略平衡在处理不同的输入和输出大小时保留其关键信号,再对栅格图像中的曲线进行语义分割减少背景干扰,增加数字化准确率.实验结果表明,与多条曲线的二值分割的真实数据相比,所提出的架构有效地对曲线进行了分类和数字化,在栅格测井图像数字化方面表现出较高的准确度和性能.

基于斑马算法优化支持向量回归机模型预测页岩地层压力

针对陇东地区三叠系延长组7段(长7段)页岩孔隙结构复杂、非均质性强、地层压力预测精度较低等问题,提出了一种基于斑马算法优化支持向量回归机(ZOA-SVR)模型预测地层压力的方法,并在实际钻井中进行了应用,将预测结果与基于机器算法的模型和常规地层压力预测方法结果进行了对比。研究结果表明:①ZOA-SVR模型以实测地层压力数据为目标变量,优选与陇东地区长7段页岩地层压力数据关联度达到0.70以上的深度、声波时差、密度、补偿中子、自然伽马、深侧向电阻率、泥质含量等7个参数作为输入特征参数,设置训练样本数为40,交叉验证折数为5,初始化斑马种群数量为10,最大迭代次数为70,对惩罚因子和核参数进行优化并建模,参数优化后拟合优度指标R2达到0.942,模型预测的地层压力数据在训练集和测试集上的绝对误差均低于1 MPa,预测测试集地层压力数据与实测压力数据的平均相对误差为2.42%。②ZOA-SVR模型在研究区长7段地层压力预测中优势明显,比基于粒子群优化算法、灰狼算法和蚁群算法的模型具有更好的参数调节及优化能力,R2分别提高了0.209,0.327,0.142;比等效深度法、Eaton法、有效应力法预测的地层压力精度更高,相对误差分别降低了32.53%,15.31%,5.91%。③ZOA-SVR模型在实际钻井中的应用结果显示,研究区长7段地层压力在垂向上分布较稳定,泥页岩段的地层压力高于砂岩段,地层压力系数主要为0.80~0.90,整体上属于异常低压环境,与实际地层情况相符。

基于多信息融合的层次聚类测井曲线自动分层方法

随着人工智能的快速发展,机器学习的应用范围越来越广泛,将机器学习的方法用于测井曲线分层可以提高分层效率和精度。在利用测井资料进行岩性识别、沉积相分析等研究时,先要对测井曲线进行分层。文中提出一种基于多信息融合的层次聚类分层方法,实现了对测井曲线的自动分层。首先,采用滤波的方式滤除曲线上的噪点,对数据进行归一化处理,消除量纲的影响;其次,通过特征优选,选择包含较多地层信息的特征曲线,构造一个滤波器,将其中相似性较高的曲线融合,曲线融合的权值通过遗传算法求得;最后,使用层次聚类方法对多信息融合后的测井数据进行划分,将分层结果与人工分层结果进行对比验证。该方法能够提高分层效率,为地质勘探工作提供可靠的分层依据。

牛东地区砂砾岩储层测井层内非均质性评价

牛东地区砂砾岩储层具有低孔、低渗等特征,储层结构复杂,非均质性较强,常规测井对储层类型划分困难,影响了储层评价精度。岩心孔渗、薄片分析、X衍射等测试资料表明,牛东地区砂砾岩储层具有较强的非均质性,且根据毛管压力形态可将储层分为3个类别。本次研究利用电成像测井资料对储层层内非均质性进行评价:首先对电成像测井资料进行处理得到储层孔隙度谱,再对随深度变化的孔隙度谱进行研究,分别计算不同深度上孔隙度谱的均值、方差、洛伦兹系数和集中程度函数;根据综合概率模型,利用层次分析法确定评价指标的权重,得到储层非均质性综合指数,据此对储层进行划分,并建立储层非均质性的评价标准。应用该方法得到的研究结果与压汞实验结果一致,说明利用该方法能够有效地评价储层非均质性,可丰富现有的储层非均质性评价方法,为储层的精细评价提供一定的理论支撑。

基于模态分解与LSTM注意表征的测井曲线重构研究

测井曲线记录着地层物理性质随深度变化的幅值范围,是测井与地震资料之间的纽带,对储层岩性分析与识别和后续的油气勘探工程十分重要.然而,在实际的测井过程中仪器故障等原因会造成测井曲线缺失的问题,重新测井不仅价格昂贵而且难以实现.针对地质勘探时测井数据时常缺失的问题,本文提出了一种LSTM(Long Short-Term Memory)注意力表征的测井曲线重构方法.同时,对原始测井信号进行两种模态分解,计算分解后得到模态分量与原始信号之间的相关性,去除冗余分量,实现对缺失的测井曲线高效、高精度的人工补全.将该方法用于声波(ACoustic,AC)与密度(DENsity,DEN)曲线重构实验,并将实验结果与LSTM网络和BP(Back Propagation)神经网络预测的结果进行对比分析.结果表明,LSTM-Attention模型有着更为优异的预测效果,重构后的AC和DEN与原始曲线之间的相关性分别达到了86.8%和74.8%,高于传统LSTM和BP神经网络预测方法.在去除冗余的信号分量后,相关系数分别提高了1.4%和4.0%.同时,本文所提方法预测出的测井曲线具有最低的预测误差.因此,基于LSTM注意表征的网络结构对测井曲线重构具有较好的预测精度.

Drilog与Welleader系统的应用研究

自主研发的Drilog随钻测井系统由随钻测量仪、工程参数测量仪及随钻电阻率伽马一体化测井仪组成;自主研发的Welleader旋转导向钻井系统由涡轮发电机短节、有线柔性短节、以及导向短节组成。2个系统配合用于勘探开发中,对提高钻遇率有重要作用。介绍了2套系统组合在某油田X井8.5英寸井段的使用过程,通过实时数据研究,分析了轨迹控制、动态轨迹测量、导向工具时效、随钻测井质量等应用效果。统计结果显示系统的钻进效率高,测量数据准确,证明了2套系统在定向井作业中的测井质量优良,实时地质导向效果良好。本次高难度作业为2套系统推广积累了应用经验。

时域积分的LWD振动加速度处理电路

根据随钻测井(LWD)仪器的振动位移对随钻测井数据进行评估,是提高随钻测井准确性的有效方法。研制了一种基于时域积分方法的加速度信号处理电路,其可在井下实时测量随钻测井仪器的振动加速度,再通过积分计算获得井下实时振动位移。该电路的数字信号处理器件选用高可靠性的32位定点DSP器件TMS320F2812,加速度信号处理方法选用占用资源少、便于微型系统实时计算的时域积分方法,再采用拟合多项式极值方法,消除了积分中产生的趋势项误差。振动测试表明,该加速度测量电路所得的位移时程曲线与理论值曲线拟合的精度较高,能满足随钻测井仪器位移测量的需求。

轮廓曲线的LoG变换及图像共变区域的检测

本文将LoG(Laplacian of Gaussian)变换应用到轮廓曲线上,其范数的平方具有稳健的曲率特性,由此构造了轮廓角点的响应函数;在此基础上,结合轮廓LoG所检测的角点和角点邻域内轮廓方向的尺度共变性设计了一种新的关于旋转和尺度共变的特征区域检测算法.最后,利用相关度准则对多组受干扰的图像进行特征共变区域匹配,实验结果验证了所提出的共变特征区域检测算法具有计算简单、容易实现和较强的鲁棒性等特点.