The optimization design of geometry based on forward modeling is an important means to improve the quality of field data acquisition and reduce acquisition costs.Based on the conventional Gaussian beam forward simulation theory,the Fresnel band constrained Gaussian beam(Fresnel beam)forward simulation theory is proposed.Compared with the conventional Gaussian beam,the Fresnel Gaussian beam is constrained by the first Fresnel band on its propagation path and has a smaller effective half width,making the travel time and amplitude calculation of the wave field near the central ray more accurate,thus improving the accuracy of forward simulation.At the endpoint of the central ray,extract the received energy of the target layer,establish the energy mean square error and average power analysis functions,quantitatively analyze and evaluate the energy distribution of the target layers under different geometry,predict the later migration imaging effect,and optimize the shot points encryption scheme based on the received energy of the target layer,thus realizing the optimal design of the geometry for complex geological targets.This method has been applied in the G94 northern block of Shengli exploration area,effectively compensating for the energy of illumination shadow areas on steep slope zones and improving the migration imaging effect of data acquisition.
Conventional logging interpretation methods qualitatively identify shale reservoirs using shale attribute parameters and interpretation templates. However, improving the identification accuracy of complex shale reservoirs is challenging due to the numerous evaluation parameters and the complexity of model calculations. To quantitatively characterize high-quality shale reservoirs effectively, this study utilizes two wells in the Fuling shale gas field as examples and establishes a comprehensive evaluation method for identifying high-quality shale gas reservoirs utilizing multi-fractal spectral analysis of well logs. First, the conventional well logs are qualitatively analyzed and evaluated via multiple fractals and R/S analysis. Subsequently, a gray relational analysis is employed to combine the production well logging, which reflects dimensionless productivity contributions, with the fractal characteristics of conventional well logs to obtain the corrected weight multifractal spectrum width ∆α' and fractal dimension D’. Comprehensive fractal evaluation indices λ and γ are introduced, forming three categories of productivity evaluation standards for shale gas reservoirs characterized by fractals. Finally, a validation well is employed to demonstrate the effectiveness of the evaluation method. The results indicate that the identification of high-quality shale gas reservoirs based on the above comprehensive fractal evaluation method can reflect the productivity classification level of fractured well sections, simplify the calculation of formation evaluation parameters, and avoid the problem of poor correlation between predicted sweet spot zones and gas production. This approach has wide applicability and value for identifying high-quality reservoir areas in shale gas reservoirs and provides technical support for the effective large-scale development of shale reservoirs.
In the vast realm of optical fiber sensing, where precision and innovation converge, Fiber Bragg Gratings (FBGs) stand as luminaries, casting their influence across myriad applications [...].
This book presents an easy-to-use tool for time series analysis and allows the user to concentrate upon studying time series properties.
TOC(Total Organic Carbon,总有机碳)含量所表征的有机质丰度是评价烃源岩生烃潜力的重要指标之一.地震弹性参数与岩石的TOC含量之间存在一定的相关性,而且这种关系可能是非线性的.由于线性回归和 XGBoost(Extreme Gradient Boosting,极端梯度提升)方法的复杂映射能力有限,以致TOC含量预测精度有限.本文提出了一种基于ConvLSTM(Convolutional Long Short-Term Memory,卷积长短时记忆)神经网络的TOC含量地震预测方法,主要包括网络搭建、数据预处理、网络训练和测试四个部分.通过训练具有强复杂映射能力的ConvLSTM神经网络来建立TOC含量与地震弹性参数(纵波阻抗、速度比以及泊松比)之间的非线性关系,以有效地预测TOC含量的区域性分布.L区的数据试算结果表明,相比于线性回归和XGBoost方法,本文方法具有更高的TOC含量预测精度.
