介观尺度(mesoscale)波致流体流动(wave-induced fluid flow)是导致孔隙介质中地震波本征衰减的主要原因,因此正确地表征其衰减特性尤为重要.受限于模拟方法的稳定性与岩石物理参数复杂关系式带来的计算成本,本文提出利用复合矩阵法推导一维黏弹波动方程频率域解析表达式,从而定量表征孔隙介质中地震波的衰减以及速度频散特征.针对常规方法仅讨论传播界面对反射系数随频率变化的影响情况,基于斑块饱和模型,本文利用新方法对比传播过程和传播界面的对其影响.在考虑传播过程的影响下,重新建立孔隙度、含气饱和度、渗透率与地震响应的联系.最后,基于黏弹介质理论,提出利用Kolsky-Futterman衰减模型定量表征斑块饱和模型的黏弹特性,并进一步通过新方法验证等效表征结果,从而扩展频变AVO技术的适用范围.研究结果表明:反射系数频率依赖影响中,传播过程远远大于传播界面;储层物性参数中,地震波对孔隙度变化的敏感度最高;在地震响应上,黏弹介质模型与斑块饱和模型的衰减特征具有良好的一致性,为后续频变AVO反演提供坚实的理论依据.

Circular-RNA friend leukemia virus integration 1 (circ-FLI1; hsa_circ_0000370) is a noninvasive biomarker for the diagnosis of colon carcinoma (CC). Herein, we intended to investigate its functions and competing endogenous RNA (ceRNA) mechanisms in CC cells. In terms of expression status, circ-FLI1 was abnormally upregulated in CC patients' tumors and cells, paralleled with DKC1 upregulation and miR-197-3p downregulation. Most strikingly, there was a direct target relationship between miR-197-3p and circ-FLI1 or DKC1 based on the starbase database, dual-luciferase reporter assay, and RNA immunoprecipitation. Functionally, the colony formation assay, MTS method, fluorescence-activated cell sorting method, cell cycle and apoptosis assays, and transwell assays were performed, and the results revealed that interfering circ-FLI1 and re-expressing miR-197-3p could restrict colony formation, cell viability, cell cycle progression, and migration/invasion of CC cells with apoptosis rate elevation; besides, they promoted oxaliplatin (L-OHP)-induced cell viability inhibition. Furthermore, there were counteractive effects between circ-FLI1 silencing and miR-197-3p depletion, miR-197-3p overexpression and DKC1 restoration on regulating CC cell functions and L-OHP resistance. With a xenograft tumor model, the anti-growth role of circ-FLI1 silencing was also found in vivo with or without L-OHP treatment. Collectively, we demonstrated that circ-FLI1 might confer L-OHP resistance and malignant progression of CC presumably through the circ-FLI1/miR-197-3p/DKC1 ceRNA axis.

Prediction of lithology/fluid (LF) characteristics is always the bottleneck problem and difficulty of reservoir characterization. Deep-learning-based data-driven methods can review data and find specific trends and patterns that would not be apparent to humans, and have been successfully used in many geophysical applications including LF prediction (LFP). However, the above methods mostly predict LF point-by-point, which means that the spatial correlation of LF is not considered. When the predicted LF results are combined to form a 2-D/3-D image, the resulting image will be noisy or even geologically unreliable. To overcome these issues, we proposed a spatially coupled data-driven (convolutional neural network, CNN) approach for LFP from the poststack seismic data and well observations. Here, the vertical couplings of the LF are modeled by a Markov chain (MC) prior and the lateral continuity of the LF is further defined by a Markov random field (MRF) prior. We also proposed to perform spectral decomposition via inversion strategies (ISD) to get a timex2013;frequency (TF) spectrum as the input of CNN. ISD helps make full use of the information hidden in the frequency domain of the poststack seismic data. Well-logs and poststack seismic data are integrated in a consistent manner to obtain predictions of the LF classes with the associated uncertainty statements. The LFP results of the proposed approach are more laterally continuous and geologically reliable than the LFP results of the point-by-point. We determined the effectiveness of this methodology on a 2-D synthetic model and a 3-D field seismic data set.

Seismic inversion is one of the most commonly used methods in the oil and gas industry for reservoir characterization from observed seismic data. Deep learning (DL) is emerging as a data-driven approach that can effectively solve the inverse problem. However, existing DL-based methods for seismic inversion utilize only seismic data as input, which often leads to poor stability of the inversion results. Besides, it has always been challenging to train a robust network since the real survey has limited labelled data pairs. To partially overcome these issues, we develop a neural network framework with a priori initial model constraint to perform seismic inversion. Our network uses two parts as one input for training. One is the seismic data, and the other is the subsurface background model. The labels for each input are the actual model. The proposed method is performed by log-to-log strategy. The training data set is first generated based on forward modelling. The network is then pre-trained using the synthetic training data set, which is further validated using synthetic data that have not been used in the training step. After obtaining the pre-trained network, we introduce the transfer learning strategy to fine-tune the pre-trained network using labelled data pairs from a real survey to acquire better inversion results in the real survey. The validity of the proposed framework is demonstrated using synthetic 2-D data including both post-stack and pre-stack examples, as well as a real 3-D post-stack seismic data set from the western Canadian sedimentary basin.

Seismic inversion is a common method for hydrocarbon reservoir characterization, as it consists of a proven and effective approach to derive elastic properties from reflectivity seismic data. Markov Chain Monte Carlo (MCMC) based seismic inversion approach is a suitable choice to numerically evaluate the posterior uncertainties associated with the inverse solution without assuming linear forward operators, Gaussian, or generalized Gaussian prior models. However, the existing MCMC based seismic inversion approaches are mostly performed trace-by-trace, which means that the spatial coupling of model parameters is not considered. When the results of trace-by-trace based inversion are combined to generate a 2D profile, the final results will be laterally discontinuous. Moreover, the large dimension of the model space causes low convergence efficiency of MCMC-based seismic inversion. To overcome these issues, a geological structure-guided hybrid MCMC and Bayesian linearized inversion (BLI) methodology for seismic inversion is implemented. The geological structure information obtained using plane wave destruction (PWD) is incorporated to the MCMC based inversion algorithm in the form of dips yields more geologically meaningful results. The hybrid MCMC and BLI strategy, which takes advantage of BLI's high efficiency to provide initial configuration for MCMC, is used to improve the convergence of MCMC-based inversion. Additionally, the block coordinate descent (BCD) algorithm is introduced to replace the large-scale matrix solution in geological structure-guided, and consequently reduce memory consumption and time cost. This methodology is validated on a synthetic seismic dataset, as well as on a real case. It has proven to be a reliable approach to obtain acoustic impedance (AI) from post-stack seismic data in an efficient way. It also addresses the uncertainty related with the ill-posed characteristics of the inversion methodology itself.

Time-lapse (TL) seismic has great potential in monitoring and interpreting time-varying variations in reservoir fluid properties during hydrocarbon exploitation. Obtaining the disparities of reservoir elastic parameters by inversion is essential for TL reservoir monitoring. Conventional TL inversion is carried out by stages without coupling processing and leads to inaccurate results. We directly use the differences in seismic data responses from different vintages, namely, difference inversion, to improve the results’ credibility. It may reduce the deviations of the subtraction of base and monitor inversions in traditional methods. Moreover, most existing studies involving prestack inversion methods use the Zoeppritz equation or its approximants, which failed to consider the wave-propagation effects. We have developed a new TL difference inversion based on the modified reflectivity method (MRM), in which the internal multiples and transmission losses are taken into consideration to fine-tune the characterization of the seismic wave propagating underground. The new method is modified on the basis of the reflectivity method making it feasible in TL difference inversion, and it is derived from the Bayesian theorem. For further delineating the boundaries between layers, the differentiable hyper-Laplacian blocky constraint (DHLBC) is introduced into the prior information of a Bayesian framework, which heightens the sparseness in the vertical gradients of the inversion results and leads to sharp interlayer boundaries of the difference parameters. Synthetic and field data experiments demonstrate that our TL difference inversion method has clear advantages in accuracy and resolution compared with the Zoeppritz method and MRM without DHLBC.

Purpose Non-coding RNA activated by DNA damage (NORAD) has been reported to be a cancer-related long non-coding RNA (lncRNA) implicated in the progression of several cancers; however, its role in breast cancer (BC) has not yet been clarified. Methods Quantitative real-time polymerase chain reaction was used to examine NORAD, microRNA (miR)-155-5p, and suppressor of cytokine signaling 1 (SOCS1) mRNA expression levels. Western blotting was used to analyze SOCS1 protein expression. The malignancy of BC cells was assessed using the cell counting kit-8 (CCK-8), BrdU, and Transwell assays. Bioinformatics analysis, RNA immunoprecipitation assay, and dual-luciferase reporter gene assays were used to verify the targeted relationship between NORAD and miR-155-5p. Additionally, the regulatory effects of NORAD and miR-155-5p on SOCS1 expression were determined by western blotting. Results NORAD expression was significantly reduced in BC cell lines and tissues, and its low expression was associated with poor tumor tissue differentiation. NORAD overexpression repressed BC cell proliferation, migration, and invasion, whereas its knockdown produced the opposite effects. Additionally, miR-155-5p was found to be a target of NORAD, and the biological functions of miR-155-5p and NORAD were counteractive. MiR-155-5p was confirmed to target SOCS1, and SOCS1 was found to be positively regulated by NORAD. Conclusion NORAD suppresses miR-155-5p to upregulate SOCS1, thereby repressing the proliferation, migration, and invasion of BC cells.

纵波衰减与频散是PP波地震记录衰减的主要原因,因此理论上只需利用PP波叠后地震资料即可反演振幅随频率的变化关系(AVF),以获取纵波频散因子指示流体.但基于传统单界面的AVF反演方法并不令人满意且在很多方面仍然存在争议.为此,提出基于零炮检距黏滞声波方程解析解的AVF反演方法,其流程为:①利用时频谱方法等计算地震记录的时频谱;②基于地震记录提取子波,消除地震数据中子波叠印获取反射系数的时频谱;③基于黏滞声波方程进行波阻抗反演,获得更准确的阻抗参数计算Fréchet导数;④根据导数矩阵建立AVF反演方程,选取合适的参考频率点以及参与计算的频率点反演高精度频散属性.数值模拟和实际数据测试表明:界面频散对地震记录的影响很小,且传播过程的AVF效应远大于界面频散造成的AVF效应;新方法的精度和分辨率明显高于传统单界面AVF反演.

对于油藏参数预测及其不确定性评价,前人的方法均为多步骤反演,很难考虑各个环节的不确定性.为此,提出基于构造约束联合概率反演的油藏参数表征方法.首先通过统计井资料得到岩相依赖储层弹性参数和物性参数混合高斯联合先验分布,在岩石物理参数敏感性分析基础上建立储层弹性参数和物性参数高斯联合先验分布;利用地质构造约束最小二乘井插值将构造信息和井信息整合到反演框架,基于贝叶斯理论推导得到同时表征储层弹性参数、物性参数、岩相后验概率分布的解析表达式.与传统方法相比,新方法通过同时反演策略降低误差累积,提高了储层参数及其不确定性信息预测的准确性;另外,新方法引入构造信息和井信息提高了反演结果的横向连续性及分辨率.为验证新方法的有效性,对M区实际数据集通过条件井和盲井测试,对比、分析了无构造约束多步方法与新方法的反演结果.结果 表明:基于线性化模型且服从高斯分布假设,新方法获得了较好的反演效果,得到的岩相后验概率较无构造约束多步方法更准确,客观表征了不确定性,为油藏表征、评价提供了有利依据.

Prediction of lithology/fluid (LF) properties from seismic data can be very valuable in all phases of oil and gas exploration and production, but the resolution and accuracy of predicted results are reduced due to band-limited wavelet and noise of seismic data. Deep learning can review data, discover specific trends and patterns that would not be apparent to humans, and has been successfully used in many applications, including geophysics. Also, time-frequency (T-F) analysis tools can show how the energy of the signal is distributed over the 2-D T-F space, which helps to exploit the features produced by the concentration of signal energy. In this letter, we propose a novel hybrid approach for predicting LF properties, including oil-sand, brine-sand, and shale and evaluating their uncertainty, which aims at combining the benefits of T-F analysis method based on inverse spectral decomposition (ISD) and one-dimensional convolutional neural network (1D-CNN). The proposed method can provide more details about thinner layers and suppress noise to some extent using T-F spectrum obtained by ISD, and capture more relevant features from the input using 1D-CNN at different levels similar to a human brain, and thus, can significantly improve the resolution and accuracy of the predicted results. The proposed method was applied to a real 3-D post-stack seismic data and validated through a blind well test and comparison with the conventional methods.

由于前期处理过程多数情况下基于声学介质假设,叠前地震道集更趋向于声学AVO特征.密度作为一种非常可靠的弹性参数在储层描述中起着关键作用,但反演过程不稳定.为此,提出基于声波方程解析解的块约束广义波阻抗反演方法.该方法通过部分叠加剖面反演随入射角变化的广义声阻抗,在此基础上提取稳定的速度和密度参数.针对常规阻抗反演方法忽略透射损失、层间多次波问题,基于推导的递归公式,对一维声波方程进行解析求解,获取不同入射角的全波场响应,并利用链式法则推导了对应的模型导数用于梯度类反演算法.为提高反演结果的分辨率,在贝叶斯理论框架下引入块约束,以获取稳定且边界清晰的反演结果.模型数据验证了所提出的正、反演方法的有效性;通过噪声测试验证了块约束反演方法的抗噪能力以及边界刻画能力;实际资料反演结果表明,新方法的反演结果分辨率高,边界刻画清晰,提取的速度、密度参数稳定且准确.

ABSTRACTThe conventional impedance inversion method ignores the attenuation effect, transmission loss and inter‐layer multiple waves; the smooth‐like regularization approach makes the corresponding impedance solution excessively smooth. Both fundamentally limit the resolution of impedance result and lead to the inadequate ability of boundary characterization. Therefore, a post‐stack impedance blocky inversion method based on the analytic solution of viscous acoustic equation is proposed. Based on the derived recursive formula of reflections, the 1D viscous acoustic wave equation is solved analytically to obtain zero‐offset full‐wave field response. Applying chain rule, the analytical expression of the Fréchet derivative is derived for gradient‐descent non‐linear inversion. Combined with smooth constraints, the blocky constraints can be introduced into the Bayesian inference framework to obtain stable and well‐defined inversion results. According to the above theory, we firstly use model data to analyse the influence of incompleteness of forward method on seismic response, and further verify the effectiveness of the proposed method. Then the Q‐value sensitivity analysis of seismic trace is carried out to reduce the difficulty of Q‐value estimation. Finally, the real data from Lower Congo Basin in West Africa indicate that the proposed approach provide the high‐resolution and well‐defined impedance result. As a supplement and development of linear impedance inversion method, the non‐linear viscous inversion could recover more realistic and reliable impedance profiles.

Amplitude-variation-with-offset (AVO) inversion is based on single interface reflectivity equations. It involves some restrictions, such as the small-angle approximation, including only primary reflections, and ignoring attenuation. To address these shortcomings, the analytical solution of the 1D viscoelastic wave equation is used as the forward modeling engine for prestack inversion. This method can conveniently handle the attenuation and generate the full wavefield response of a layered medium. To avoid numerical difficulties in the analytical solution, the compound matrix method is applied to rapidly obtain the analytical solution by loop vectorization. Unlike full-waveform inversion, the proposed prestack waveform inversion (PWI) can be performed in a target-oriented way and can be applied in reservoir study. Assuming that a Q value is known, PWI is applied to synthetic data to estimate elastic parameters including compressional wave (P-wave) and shear wave (S-wave) velocities and density. After validating our method on synthetic data, this method is applied to a reservoir characterization case study. The results indicate that the reflectivity calculated by our approach is more realistic than that computed by using single interface reflectivity equations. Attenuation is an integral effect on seismic reflection; therefore, the sensitivity of seismic reflection to P-and S-wave velocities and density is significantly greater than that to Q, and the seismic records are sensitive to the low-frequency trend of Q. Thus, we can invert for the three elastic parameters by applying the fixed low-frequency trend of Q. In terms of resolution and accuracy of synthetic and real inversion results, our approach performs superiorly compared to AVO inversion.

Wave-induced fluid flow is the main cause of seismic attenuation and dispersion. So the estimated velocity dispersion information can be used to identify reservoir fluid and effectively reduce the risk of reservoir drilling. Using equivalence of dispersion and attenuation between poroelastic and viscoelastic media, we developed the method of FAVO (frequency-dependent amplitude variation with offset) dispersion quantitative estimation based on the analytical solution of 1D viscoelastic wave equation. Compared with the current single-interface velocity dispersion estimation method, the new nonlinear approach uses the analytical solution of 1D viscoelastic wave equation as the forward modeling engine. This method can conveniently handle the attenuation and generate the full-wave field response of a layered medium. First, the compound matrix method (CMM) was applied to rapidly obtain the analytical solution by vectorization. Further, we analyzed the seismic response characteristics through the model data to clarify the effectiveness of the forward modeling method. Then, the more reliable P-wave velocity, S-wave velocity, and density were recovered based on prestack viscoelastic waveform inversion (PVWI). Combining with the inversion results, the derivative matrix was calculated to perform nonlinear velocity dispersion estimation. Finally, the new estimation method was tested with the model and actual data. The experiments show that the developed method is clearly superior to the single-interface dispersion estimation method in accuracy and resolution. This approach can be used as a new choice reservoir fluid identification.

We propose a nonlinear impedance inversion method based on the Bayesian theory that takes into account internal multiples, transmission and attenuation. First, we derive the total reflectivity recursive equation based on acoustic multiplication matrix, and further analyze how internal multiples, transmission and attenuation affect the amplitude and phase of seismic records. Then, we obtain the recursive expression for the Fréchet derivatives of the synthetics with respect to the model parameters, and compare the difference between the conventional method and the analytical method by the derivative matrix. Finally, we use the synthetic data demonstrate the effectiveness of the proposed method and apply it to a real seismic data. Comparing inversion results, the result of the proposed is more accurate and possesses the higher resolution. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 9:20 AM Presentation Time: 10:10 AM Location: Poster Station 7 Presentation Type: Poster

A linearized Q-compensate AVO inversion approach is proposed. First, we derive the forward modeling matrix in frequency domain and transformed to time domain by Fourier transform. Then, by defining the complex traveltime in frequency domain, we can model AVO gathers with attenuation using linearized matrix. Further, we analyze the difference of linear matrix between with attenuation and without attenuation and estimate elastic parameters based on the combination amplitude compensation together with inversion. Finally we carry out a numerical example with synthetic data demonstrating the effectiveness of the proposed method and apply it to a real seismic data. The examples demonstrate that the Q-compensate inversion method recovers a more accurate inversion result and have higher resolution than conventional method. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Time: 3:30 PM Location: Poster Station 8 Presentation Type: Poster