岩心刻度测井在建立测井解释模型中的应用

利用岩心刻度测井建立测井解释模型以提高测井解释精度,对于储集层描述和油气层评价具有重要意义。首先讨论了岩心刻度测井方法中的关键技术问题,如岩心分析数据深度归位、数据滤波处理及标准化处理方法等。在此基础上,建立相应物性参数的测井解释模型,并从A油田的具体地质条件出发,根据密度、补偿中子、声波测井曲线和自然伽马曲线分别建立了孔隙度、泥质含量和渗透率解释模型;选用阿尔奇公式作为储集层的饱和度解释模型。应用上述模型对A油田的代表井进行了处理并与试油资料对比,符合率达89.2%。

原油中烷基酚类化合物研究述评

烷基酚类是原油中常见的一类非烃化合物。通过对国内外文献的调研,综述了原油中烷基酚类可能的来源和分布控制因素;提出了烷基酚类有效分子运移参数的筛选方法;探讨了烷基酚类在油藏开发中的可能应用,如利用烷基酚类的含量和变化特征对注水开发效果进行评价、对剩余油分布进行预测以及对运移体积进行估算。系统总结了烷基酚类在油气勘探开发中的研究现状与存在问题的同时,还对其应用前景进行了展望。

Inversion-based data-driven time-space domain random noise attenuation method

Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter （PEF）, is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data＇s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.

Hybrid absorbing boundary condition for threedimensional elastic wave modeling

Edge reflections are inevitable in numerical modeling of seismic wavefields, and they are usually attenuated by absorbing boundary conditions. However, the commonly used perfectly matched layer （PML） boundary condition requires special treatment for the absorbing zone, and in three-dimensional （3D） modeling, it has to split each variable into three corresponding variables, which increases the computing time and memory storage. In contrast, the hybrid absorbing boundary condition （HABC） has the advantages such as ease of implementation, less computation time, and near-perfect absorption; it is thus able to enhance the computational efficiency of 3D elastic wave modeling. In this study, a HABC is developed from two-dimensional （2D） modeling into 3D modeling based on the I st Higdon one way wave equations, and a HABC is proposed that is suitable for a 3D elastic wave numerical simulation. Numerical simulation results for a homogenous model and a complex model indicate that the proposed HABC method is more effective and has better absorption than the traditional PML method.

lp norm inverse spectral decomposition and its multi-sparsity fusion interpretation

Spectral decomposition has been widely used in the detection and identifi cation of underground anomalous features(such as faults,river channels,and karst caves).However,the conventional spectral decomposition method is restrained by the window function,and hence,it mostly has low time–frequency focusing and resolution,thereby hampering the fi ne interpretation of seismic targets.To solve this problem,we investigated the sparse inverse spectral decomposition constrained by the lp norm(0<p≤1).Using a numerical model,we demonstrated the higher time–frequency resolution of this method and its capability for improving the seismic interpretation for thin layers.Moreover,given the actual underground geology that can be often complex,we further propose a p-norm constrained inverse spectral attribute interpretation method based on multiresolution time–frequency feature fusion.By comprehensively analyzing the time–frequency spectrum results constrained by the diff erent p-norms,we can obtain more refined interpretation results than those obtained by the traditional strategy,which incorporates a single norm constraint.Finally,the proposed strategy was applied to the processing and interpretation of actual three-dimensional seismic data for a study area covering about 230 km^(2) in western China.The results reveal that the surface water system in this area is characterized by stepwise convergence from a higher position in the north(a buried hill)toward the south and by the development of faults.We thus demonstrated that the proposed method has huge application potential in seismic interpretation.

Effects of micrite microtextures on the elastic and petrophysical properties of carbonate reservoirs

Apparent differences in sedimentation and diagenesis exist between carbonate reservoirs in different areas and affect their petrophysical and elastic properties.To elucidate the relevant mechanism,we study and analyze the characteristics of rock microstructure and elastic properties of carbonates and their variation regularity using 89 carbonate samples from the different areas The results show that the overall variation regularities of the physical and elastic properties of the carbonate rocks are controlled by the microtextures of the microcrystalline calcite,whereas the traditional classification of rock-and pore-structures is no longer applicable.The micrite microtextures can be divided,with respect to their morphological features,into porous micrite,compact micrite,and tight micrite.As the micrites evolves from the first to the last type,crystal boundaries are observed with increasingly close coalescence,the micritic intercrystalline porosity and pore-throat radius gradually decrease;meanwhile,the rigidity of the calcite microcrystalline particle boundary and elastic homogeneity are enhanced.As a result,the seismic elastic characteristics,such as permeability and velocity of samples,show a general trend of decreasing with the increase of porosity.For low-porosity rock samples(φ<5%)dominated by tight micrite,the micritic pores have limited contributions to porosity and permeability and the micrite elastic properties are similar to those of the rock matrix.In such cases,the macroscopic physical and elastic properties are more susceptible to the formation of cracks and dissolution pores,but these features are controlled by the pore structure.The pore aspect ratio can be used as a good indication of pore types.The bulk modulus aspect ratio for dissolution pores is greater than 0.2,whereas that of the intergranular pores ranges from 0.1 to 0.2.The porous and compact micrites are observed to have a bulk modulus aspect ratio less than 0.1,whereas the ratio of the tight micrite approaches 0.2。

Multichannel deconvolution with spatial refl ection regularization

Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement.However,the trace-by-trace processing strategy is applied and ignores the spatial connection along seismic traces,which gives the deconvolved result strong ambiguity and poor spatial continuity.To alleviate this issue,we developed a structurally constrained deconvolution algorithm.The proposed method extracts the refl ection structure characterization from the raw seismic data and introduces it to the multichannel deconvolution algorithm as a spatial refl ection regularization.Benefi ting from the introduction of the reflection regularization,the proposed method enhances the stability and spatial continuity of conventional deconvolution methods.Synthetic and field data examples confi rm the correctness and feasibility of the proposed method.

Incremental semi-supervised learning for intelligent seismic facies identification

Intelligent seismic facies identification based on deep learning can alleviate the time-consuming and labor-intensive problem of manual interpretation,which has been widely applied.Supervised learning can realize facies identification with high efficiency and accuracy;however,it depends on the usage of a large amount of well-labeled data.To solve this issue,we propose herein an incremental semi-supervised method for intelligent facies identification.Our method considers the continuity of the lateral variation of strata and uses cosine similarity to quantify the similarity of the seismic data feature domain.The maximum-diff erence sample in the neighborhood of the currently used training data is then found to reasonably expand the training sets.This process continuously increases the amount of training data and learns its distribution.We integrate old knowledge while absorbing new ones to realize incremental semi-supervised learning and achieve the purpose of evolving the network models.In this work,accuracy and confusion matrix are employed to jointly control the predicted results of the model from both overall and partial aspects.The obtained values are then applied to a three-dimensional(3D)real dataset and used to quantitatively evaluate the results.Using unlabeled data,our proposed method acquires more accurate and stable testing results compared to conventional supervised learning algorithms that only use well-labeled data.A considerable improvement for small-sample categories is also observed.Using less than 1%of the training data,the proposed method can achieve an average accuracy of over 95%on the 3D dataset.In contrast,the conventional supervised learning algorithm achieved only approximately 85%.

Seismic wavefield modeling based on time-domain symplectic and Fourier finite-difference method

Seismic wavefield modeling is important for improving seismic data processing and interpretation. Calculations of wavefield propagation are sometimes not stable when forward modeling of seismic wave uses large time steps for long times. Based on the Hamiltonian expression of the acoustic wave equation, we propose a structure-preserving method for seismic wavefield modeling by applying the symplectic finite-difference method on time grids and the Fourier finite-difference method on space grids to solve the acoustic wave equation. The proposed method is called the symplectic Fourier finite-difference （symplectic FFD） method, and offers high computational accuracy and improves the computational stability. Using acoustic approximation, we extend the method to anisotropic media. We discuss the calculations in the symplectic FFD method for seismic wavefield modeling of isotropic and anisotropic media, and use the BP salt model and BP TTI model to test the proposed method. The numerical examples suggest that the proposed method can be used in seismic modeling of strongly variable velocities, offering high computational accuracy and low numerical dispersion. The symplectic FFD method overcomes the residual qSV wave of seismic modeling in anisotropic media and maintains the stability of the wavefield propagation for large time steps.

Model-data-driven AVO inversion method based on multiple objective functions

The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted parameters because it primarily employs medium-frequency information from seismic data.The latter can predict parameters with high resolution,but it require a signifi cant number of accurate training samples,which are typically in limited supply.To solve the problems mentioned for these two methods,we propose a model-data-driven AVO inversion method based on multiple objective functions.The proposed method implements network training,network optimization,and network inversion by using three independent objective functions.Tests on synthetic and fi eld data show that the proposed method can invert high-accuracy and high-resolution velocity and density with a few training samples.

Experimental study on the multiscale scattering of high-velocity heterogeneous bodies

There are complex heterogeneous entities in the underground medium,and the heterogeneous scale has a substantial impact on wave propagation.In this study,we used a set of 11 samples of glass beads as high-velocity heterogeneous bodies to evaluate the impact of such heterogeneous bodies on the propagation of P-wave.We vary the heterogeneous scale by changing the diameter of the glass beads from 0.18 to 11 mm while keeping the same volume proportion(10%)of the beads for the set of 11 samples.The pulse transmission method was used to record measurements at the ultrasonic frequencies of 0.34,0.61,and 0.84 MHz in the homogeneous matrix.The relationship between P-wave fi eld features and heterogeneity scale,P-wave velocity,and the multiple of the wave number and heterogeneous scale(ka)was observed in the laboratory,which has sparked widespread interest and research.Heterogeneous scale affects P-wave propagation,and its wave field changes are complex.The waveform,amplitude,and velocity of the recorded P-waves correlate with the heterogeneous scale.For the forward scattering while large-scale heterogeneities,noticeable direct and diff racted waves are observed in the laboratory,which indicates that the infl uence of direct and diff racted waves cannot be ignored for large-scale heterogeneities.The relationship between velocity and ka shows frequency dependence;the reason is that the magnitude of change in velocity caused by wave number is diff erent from that caused by heterogeneous scale.According to the change in the recorded waveform,amplitude variation,or the relationship between the velocity measured at diff erent frequencies and the heterogeneous scale,the identifi ed turning points of the ray approximation are all around ka=10.When ka is less than 1,the velocity changes slowly and gradually approaches the eff ective medium velocity.The ray velocity measured for heterogeneous media with large velocity perturbations in the laboratory is signifi cantly smaller than the velocity predicted by the perturbation theory.

Self-adaptive inversion method of electromagnetic-propagation resistivity logging while drilling data

Because of the constraint mode of the inversion objective function in the traditional resistivity-inversion method of electromagnetic-propagation resistivity logging while drilling(EPR-LWD),obvious differences appear in the radial and vertical investigation characteristics between the amplitude-ratio and phase difference,which affect the practical application of EPR-LWD data.In this paper,according to the EPR-LWD data,a self-adaptive constraint resistivity-inversion method,which adopts a self-adaptive constraint weighted expression in the objective function to balance the contributions of the phase difference and amplitude attenuation,is proposed.A particle swarm optimization algorithm is also introduced to eliminate the dependence of the accuracy and convergence on the initial value of the inversion.According to the inversion results of multiple classical formation models for EPR-LWD,the differences between the adaptive constraint inversion-resistivity logs with the traditional amplitude-ratio and the phase difference of the resistivity logs are discussed in detail.The results demonstrate that the adaptive resistivity logs take into account the advantages of the amplitude-ratio logs in the radial investigation and phase difference logs in the vertical resolution.Further,it is superior in thin-layer identification and invasion-effect appraisal compared with the single-amplitude-ratio and phase difference logs.The inversion results can provide a theoretical reference for research on the resistivity-inversion method of electromagnetic wave LWD.

An improved fast converted-wave imaging method

The conventional fast converted-wave imaging method directly uses backward Pand converted S-wavefield to produce joint images. However, this image is accompanied by strong background noises, because the wavefi elds in all propagation directions contribute to it. Given this issue, we improve the conventional imaging method in the two aspects. First, the amplitude-preserved P-and S-wavef ield are obtained by using an improved space-domain wavef ield separation scheme to decouple the original elastic wavef ield. Second, a convertedwave imaging condition is constructed based on the directional-wavefield separation and only the wavefields propagating in the same directions used for cross-correlation imaging, resulting in effectively eliminating the imaging artifacts of the wavefields with different directions;Complex-wavefi eld extrapolation is adopted to decompose the decoupled P-and S-wavefield into directional-wavefields during backward propagation, this improves the eff iciency of the directional-wavef ield separation. Experiments on synthetic data show that the improved method generates more accurate converted-wave images than the conventional one. Moreover, the improved method has application potential in micro-seismic and passive-source exploration due to its source-independent characteristic.