Cheng Yin

In the stage of biological evolution and ecosystem succession, water plays the dual role of life factor and environmental factor, and it is the material basis on which living things depend and the component of organisms. As an important carrier of terrestrial water storage, lakes play an important role in regional water balance. The study of lake ecosystem health assessment is in the development stage, and the determination of assessment index weight in most studies is extremely subjective, which makes the assessment method unable to be popularized. In this paper, a dynamic monitoring model of lake ecosystem based on geographic information system (GIS) was established. Combined with water quality monitoring data and biological monitoring data, probabilistic neural network (PNN) was used to assess the health of lake ecosystem, and the results of lake ecosystem health assessment at different monitoring time points were obtained. The experimental results show that it is feasible to use PNN to evaluate the health status of lake ecosystem. Compared with traditional assessment methods, it has the advantages of short training time, objective weight determination and stable output results, and can be applied to more related fields. In the simulation results of improving ecological efficiency to cultivate the environmental carrying capacity of ecosystem, it can be seen that although the evaluation of environmental carrying capacity of lake ecosystem did not change obviously in the early stage, the score showed an obvious acceleration trend after the cycle was extended.

High-precision time-frequency (TF) analysis (TFA) can accurately detect anomalies in oil and gas reservoirs. However, conventional TFA methods cannot effectively identify fractured-vuggy carbonate reservoirs, which are deep and strongly heterogeneous. In this paper, a TFA method based on analytical mode decomposition (AMD) and the Hilbert-Huang transform (HHT) is proposed. Specifically, seismic signals are decomposed by empirical mode decomposition (EMD) and then by AMD. Finally, the TF distribution of the seismic signals is obtained after Hilbert spectrum analysis (HSA). Synthetic seismic data demonstrate that the AMD-HHT method can effectively eliminate mode mixing and provide higher time and frequency resolutions and energy focus than conventional TFA methods. In addition, model and field seismic data also verify that the AMD-HHT method can effectively detect hydrocarbon-related energy anomalies hidden in broadband seismic data.

In exploration geophysics, seismic impedance is a physical characteristic parameter of underground formations. It can mark rock characteristics and help stratigraphic analysis. Hence, seismic data inversion for impedance is a key technology in oil and gas reservoir prediction. To invert impedance from seismic data, one can perform reflectivity series inversion first. Then, under a simple exponential integration transformation, the inverted reflectivity series can give the final inverted impedance. The quality of the inverted reflectivity series directly affects the quality of impedance. Sparse-spike inversion is the most common method to obtain reflectivity series with high resolution. It adopts a sparse regularization to impose sparsity on the inverted reflectivity series. However, the high resolution of sparse-spike-like reflectivity series is obtained at the cost of sacrificing small reflectivity. This is the inherent problem of sparse regularization. In fact, the reflectivity series from the actual impedance well log is not strictly sparse. It contains not only the sparse major large reflectivity, but also small reflectivity between major reflectivity. That is to say, the large reflectivity is sparse, but the small reflectivity is dense. To combat this issue, we adopt elastic-net regularization to replace sparse regularization in seismic impedance inversion. The elastic net is a hybrid regularization that combines sparse regularization and dense regularization. The proposed inversion method was performed on a synthetic seismic trace, which is created from an actual well log. Then, a real seismic data profile was used to test the practice application. The inversion results showed that it provides an effective new alternative method to invert impedance.

Elastic impedance (EI) inversion for partial angle stack seismic data is a key technology in seismic reservoir prediction within the oil and gas industry. EI inversion provides a consistent framework to invert partial angle stack seismic data, just as the AI inversion does for post-stack data. The commonly used EI inversion process is angle by angle. Hence, the inverted EI for different angles may be nonconforming, especially for the seismic data with a low signal-to-noise ratio. This paper proposes to simultaneously invert multiple partial angle stack seismic data to obtain EI for different angles at once. To obtain conformable EI, we used the joint sparse constraint on the reflection coefficients for different angles. Then, the objective function for simultaneous EI inversion was constructed. Next, synthetic seismic data profiles with three different angles were used to show the superiority of the proposed EI inversion method compared to the conventional method. At last, a real seismic data line was used to test the feasibility of the proposed method in practice. The inversion results of synthetic data and real data showed that it provides an effective new alternative method to estimate EI from partial stack seismic data.

In the process of fluvial sedimentary evolution, mudstone interlayer is frequently developed in the interval of sandstone deposition, forming a reservoir of sandstone compartment. Some generally nonpermeable or semipermeable interlayers become discontinuity interfaces of a reservoir, which is one of the causes for the complexity in the distribution of remaining oil. The degree of the impermeability of the discontinuity interface on fluid flow is critical to the development of hydrocarbons. The discontinuity interface, a small-scale reservoir discontinuity comparing with the fault, is generally below the seismic vertical resolution (λ/4). The key to the study of the discontinuity interface is to characterize the variation of plane characteristics of sediments between isochronous seismic horizons. Therefore, we can extract seismic attributes between isochronous seismic horizons to analyze the subtle changes of seismic data, which reflect the distribution of the discontinuity interfaces. In the experiment of gradient vector to detect discontinuity interface in seismic attribute, the authors find that the width of the discontinuity interface is caused by the “lens” shape of the sandstone itself. This phenomenon is an indicator of the impermeability of the discontinuity interface. Here, we have developed a two-step method. First, we use the erosion operator of mathematical morphology to locate the skeleton of the discontinuity interface with the steering of the gradient direction. Second, we measured the width of the discontinuity interface to indicate its impermeability. The application to a field data set demonstrates that the results of this proposed method are consistent with the production data of the wells. The proposed method characterizes the reservoir heterogeneity refined from sandstone sets to a single sandstone bed, which improves the resolution of seismic geologic interpretation.

Reservoir discontinuity is a practical representation of reservoir heterogeneity, which leads to the non-uniformed flow of hydrocarbons during production and to the increase in the difficulties of producing the remaining oil in clastic reservoirs. A feasible solution is to detect the internal bounding surfaces of the reservoir based on infill drilling data. However, for offshore oilfields, reservoir discontinuity analysis will have to rely on seismic data due to their sparse well spacings. Moreover, from the interpretation of a fluvial reservoir system in Chinaapos;s Bohai Bay, it turns out that the thickness of the sandstone is usually smaller than the seismic tuning thickness. In order to interpret a fluvial reservoir architecture which is below the seismic resolution, we elaborated a hierarchy of the fluvial reservoir architectures and classified the compound sandstones as different architectural elements according to their sedimentary periods. Forward models were designed to analyze the seismic responses of the architectures. The results demonstrate that amplitude-related seismic attributes could be sensitive to different reservoir architectures below the seismic resolution. The amplitude-related seismic attributes can be extracted through a time window guided by the horizons. The horizon-based attributes can be treated as digital images which may contain the information of compound sandstones with hidden discontinuity boundaries. We propose a direction-adaptive mathematical morphology gradient algorithm that can detect the boundaries of reservoir architectures in different directions on horizon-based seismic attributes. The application to field data demonstrates that the boundaries detected by the proposed algorithm have a good consistency with well logs. This method could enhance our capability to visualize and understand the complexity of reservoir heterogeneity.

If there are some erratic data (e.g. outliers), which may arise from measurement error, or other reasons, in seismic data, the seismic deconvolution and inversion need to be implemented in a way that minimizes their effects. However, the deconvolution and inversion methods based on L2‐norm misfit function are highly sensitive to these erratic seismic observations. As an alternative, L1‐norm misfit functions are more robust and erratic‐resistant. In order to find the solution of the inverse problem constrained by an L1‐norm misfit function, an iteratively re‐weighted least squares algorithm is used frequently. However, it is relatively time consuming. In this paper, we propose a new method based on the sparse signal representation theory. The overcomplete dictionary used for the sparse representation of seismic data with erratic data is composed of two bases: a wavelet basis used for representing the seismic data to implement deconvolution and a Dirac basis used for representing the erratic data. In addition, at the stage of seismic inversion after deconvolution, total variation and a priori model are used as the regularization constraint terms to estimate inversion results with a blocky and laterally continuous structure. The new method is successfully tested on the noisy synthetic seismic data with erratic data. Finally, the proposed method is performed on a real seismic data section, and the inversion results are reasonable, i.e. consistent with the geologic structure of the original seismic data. Compared to the conventional sparse deconvolution and inversion method, the proposed method not only eliminates the effect of outliers, but also has highly improved computational efficiency.