We analyze the sensitivity of a three-dimensional crosswell electromagnetic (EM) system based on finite-difference time-domain modeling. To reinforce the simulation accuracy of low-frequency electromagnetic propagation in a small space, we limit the boundary reflection energy by suppressing the numerical dispersion of convolutional perfectly matched layers. The numerical simulation results for an isotropic medium indicate that an anomalous conductivity will induce a large magnetic field strength disturbance at the receiving coils in the X- and Z-directions, and a relatively large absolute sensitivity is observed at the receiving coils in the Y-direction. The three-component signals are most sensitive to high-resistance anomalies. Through an experiment that involved moving an anomalous conductivity disturbance, comparison plots of dynamic residual hydrocarbon monitoring between wells and depth determinations of anomalous conductivities are provided. The optimal acquisition depth interval of the receiving coil in the homogeneous medium is given, and the effective exploration area and optimal modeling region of the crosswell EM system are obtained.

We analyze the sensitivity of a three-dimensional crosswell electromagnetic (EM) system based on finite-difference time-domain modeling. To reinforce the simulation accuracy of low-frequency electromagnetic propagation in a small space, we limit the boundary reflection energy by suppressing the numerical dispersion of convolutional perfectly matched layers. The numerical simulation results for an isotropic medium indicate that an anomalous conductivity will induce a large magnetic field strength disturbance at the receiving coils in the X- and Z-directions, and a relatively large absolute sensitivity is observed at the receiving coils in the Y-direction. The three-component signals are most sensitive to high-resistance anomalies. Through an experiment that involved moving an anomalous conductivity disturbance, comparison plots of dynamic residual hydrocarbon monitoring between wells and depth determinations of anomalous conductivities are provided. The optimal acquisition depth interval of the receiving coil in the homogeneous medium is given, and the effective exploration area and optimal modeling region of the crosswell EM system are obtained.

煤体结构作为煤层勘探开发研究的重点参数之一,影响着煤层产能,有效识别煤层煤体结构至关重要.本文利用支持向量机算法,以地球物理测井资料为基础进行煤体结构识别,并以沁水煤田柿庄北区3号层为例,对该区块进行煤体结构类型分类,利用支持向量机的双二分类与"一对多"分类两种建模模式,建立基于测井曲线的煤体结构识别模型,再利用交叉验证评价模型的泛化性,并对该模型用未参与建模数据进行准确性评价.结果表明,应用支持向量机算法的两种模式能有效识别煤体结构,模型具有泛化性与准确性,且"一对多"分类模式精度更高,在对有利产出煤和不利产出煤的区分上效果突出,对有利产出煤的具体类型区分上具有准确性,可对后续压裂施工提供指导.总体上,基于支持向量机算法和地球物理测井资料建立的煤体结构识别模型对煤层气勘探开发有指导意义,具有实际应用价值.

阵列感应测井能采集径向3m左右的地层信息、具有0.3m的纵向分辨率,可以进行复杂的侵入层和薄层分析,对准确评价油气藏具有重要作用.为了深入挖掘阵列感应测井信息,我们利用VB和Fortran语言混合开发出阵列感应快速正演、反演和绘图软件,正演采用Gianzero几何因子理论,反演考虑多参数和多精度的选择、通过阻尼最小二乘算法和最优化变尺度算法实现,成图能插入到Forward等测井解释软件中.软件的反演速度快、精度高,能用于地层的渗透率、饱和度分析.

In reservoir evaluation, it is essential to estimate the saturation levels of natural gas hydrates. The conventional algorithms of gas hydrate saturation based on geological experience or physical equations are simple multi-physical models that often provide varying results in the absence of core testing, and more complicated is to consider the complexity and instability of gas hydrates reservoirs in permafrost areas. Based on Bayesian statistical theory and three reasonable assumptions of the ideal saturation model, we combined a Bayesian discriminant function and a hyperplane equation to derive and verify two new saturation algorithms: the Saturation algorithm from the Bayesian discriminant function considering a linear correlation (SBDF) and the Saturation algorithm from the Bayesian discriminant function considering the conventional saturation (SBDF-CS). In experiments based on theoretical model data and field logging data from the sandstone and mudstone layers in the Muli permafrost area, the SBDF and SBDF-CS algorithms yielded maximum average saturation error less than 11.1% and 12.2%, only through logging data without core test calibration. The SBDF algorithms rapidly and objectively utilize representative sample data with multi-physical signals. The SBDF algorithm can be used for the initial saturation estimation of gas hydrate in permafrost areas, and the SBDF-CS algorithm is more competent for the accurate calculation of saturation in the comprehensive research.

Accurate determination of the Principal Slip Zone (PSZ) of earthquake fault zones is a key task of earthquake Fault Scientific Drilling for future earthquake control. The fault zone structure of Wenchuan earthquake is complex, and there are many strong earthquakes recorded on the fault zone, which make determining the PSZ in the Wenchuan earthquake Fault Scientific Drilling project-hole 1 (WFSD-1) difficult. At present, core analysis of whole coring is the decisive method for determining PSZ depth, and the fresh fault gouge at 589.2 m is the PSZ in WFSD-1. Abundant and comprehensive logging data can only be used as evidence to judge the PSZ. Based on the discrimination function and hyperplane equation in Bayesian discriminant classification, we derive a new algorithm for computing the PSZ possibility using a Bayesian Discrimination function (PSZP-BDF) based on the simplified model, and set up a mode to determine the PSZ directly using machine learning of well logging. For the verification of WFSD-1, the fault gouges are successfully identified and the PSZ depth is accurately located. The algorithm objectively learns the sample data, which is naturally adaptive to the region. The calculation procedure is simple and does not require expensive coring data or heavy core tests in the well. The calculation speed is fast, using multiple physical data types. The PSZP-BDF algorithm is suitable for processing and interpreting earthquake fault scientific drilling data.

Taking Convolutional Perfectly Matched Layers (CPML) as the absorbing boundary conditions, this paper applied Finite-Difference Time-Domain (FDTD) into 3D modeling of cross well electromagnetic (CWEM). The study proved that it was the real stretch which induced numerical dispersion in CPML, given the anisotropy effect of phase velocity of electromagnetic wave from the conventional real stretch and grid interval, and verified numerical dispersion in CPML through wave field snapshot; further on, generalized the restriction between the dominant frequency of source and maximum of real stretch, maximum of grid interval in CPML., so as to improve optimal parameter settings in CPML. Based on optimal CPML, we utilized the vertical magnetic field and the traveling time of second field to describe the distribution of sensitivity, so as to designate the dominant area of cross-well electromagnetic prospecting, and proposed the logging suite for restraint and preferred plan for selecting well which were required in the process of the modeling and inversion of cross-well electromagnetic.

This study applied the finite-difference time-domain (FDTD) method to forward modeling of the low-frequency crosswell electromagnetic (EM) method. Specifically, we implemented impulse sources and convolutional perfectly matched layer (CPML). In the process to strengthen CPML, we observed that some dispersion was induced by the real stretch κ, together with an angular variation of the phase velocity of the transverse electric plane wave; the conclusion was that this dispersion was positively related to the real stretch and was little affected by grid interval. To suppress the dispersion in the CPML, we first derived the analytical solution for the radiation field of the magneto-dipole impulse source in the time domain. Then, a numerical simulation of CPML absorption with high-frequency pulses qualitatively amplified the dispersion laws through wave field snapshots. A numerical simulation using low-frequency pulses suggested an optimal parameter strategy for CPML from the established criteria. Based on its physical nature, the CPML method of simply warping space-time was predicted to be a promising approach to achieve ideal absorption, although it was still difficult to entirely remove the dispersion.