Neural Network Identification Method And Software Development For Natural Gas Hydrate (Physical Geographical Property)

Currently, natural gas hydrate has been recognized as an alternative energy in the future. There is a huge potential economic and strategic significance, so governments and research institutions in many countries invested huge funds for this hydrate research and have achieved certain results. In gas hydrate research, the identification occupies an extremely important position, and it can provide scientific references for estimating gas hydrate reserves, drilling and mining. To this end, exploring a new method for identification of gas hydrate is undoubtedly of great theoretical and practical significance.The thesis discusses the identification of the natural gas hydrate through the application of neural networks and processing of logging and seismic data. This research was funded by the sub-project "high-precision seismic quantitative evaluation software development of gas hydrate ore " under national 863 high-tech research and development project "key technologies of natural gas hydrate exploration and development" (No.2006AA09202), focusing on neural network necognition of gas hydrates and software development.In this thesis, a platform integrating analysis, processing, interpretation and forecast was built and a neural network system model was designed, through establishment of neural network identification architecture and with neural network algorithm as the main algorithm for the identification and prediction of gas hydrate. Based on above work, a practical natural gas hydrate neural network recognition system was developed. The research logic is selecting self-organizing neural network for classification based on the pre-processing of logging & seismic data and extraction of seismic attributes, to identify the lithologic and ore body boundary identification; selecting BP neural network to estimate and predict parameters of hydrate reserves; then comparing the results of three identifications from log presentation, surface, geological bodies as to provide scientific data for future exploration research.Gas hydrates can exist only in specific environment. This thesis presents three prerequisites for the existence of gas hydrate:sufficient hydrocarbons, temperature and pressure conditions and the stability of hydrate accumulation and moving space (geological environment). The main methods for hydrate identification research currently focus on seismic features, geochemistry and landscape signs. Based on these identification methods, the thesis provides a brief description on undergoing natural gas hydrate researches both at home and abroad, and analyzes the primary challenges on hydrate research.Neural network can effectively deal with the complex relationship and non-linear equation of geological data. The thesis discusses the concept, characteristics, structure and basic principles of neural networks with focus on the BP and self-organizing neural networks which have different network structures and algorithm descriptions. BP neural network is instructed learning, which includes the comparison of network output with expected output, while self-organizing neural network is unsupervised learning, which mainly completes clustering operation according to automatic input adjustment. After repeated study on their advantages and disadvantages, analysis, we believe that BP neural network is more suitable for forecasting and estimating reserves parameters, while self-organizing neural network is more suitable for rock classification, ore body identification.Logging data contains a wealth of longitudinal geological information, which can better reflect the discrepancy among stratum, lithology and reserve parameters. Different methods are applied when logging data is used to estimate and predict reserve parameters. To estimate reserve parameters needs build a model based on the measured logging data, then make estimation of reserve parameters located on log presentation; while to predict reserve parameters needs build a model based on network training sample data which is obtained from the measurement of model well, then input the log presentation of other wells into the newly-established model and make prediction of reserve parameters. Lithological classification uses self-organizing neural network method:input the sample data on the log presentation into the model to get classification results. The number of classification shall be preset by the user.Seismic data is pre-processed to extract seismic attribute data which can better reflect the geological information as to facilitate the identification of hydrate BSR and amplitude blanking belts. Seismic attribute data is analyzed for its principal ingredients and partial attributes are selected, then the self-organizing neural network is used to classify the selected data. Finally gas hydrate ore body boundary maps and carving figures are obtained by repeated comparison of classification results with logging results.Seismic—logging joint inversion can combine the frequency bandwidth of log data and horizontal information of seismic data. Then appropriate wavelet is calculated and selected, and synthetic seismogram is compared with the seismic data repeatedly to finally determine the layer. BP neural network is used to set up the wave impedance model with well seismic data as input and logging as output, and the entire seismic volume data is input into the model to get wave impedance data volume. The same method is used to predict reserve parameter results in Shenhu sea area by taking seismic attribute data as input and reserve parameter as output.Neural network recognition system of natural gas hydrate (SNET) takes QT 4.3 as its development platform and integrates INT company's graphics processing plug-in module. QT is a cross-platform C++graphical user interface application framework that enables easy portability after program development. It is fully object-oriented, easily extensible and allows true component programming. INT's graphics plug-in software CarnacGeo is a good solution to the display problems for seismic data and well log data.The software is tested with seismic and logging data from Shenhu area of China South Sea, which contains three SEGY files, one seismic 3D body data and eight stations logging data. The results are eight logging classification maps, forty logging forecast figure, thirty seismic attributes figures, five seismic classification figure, one engraving figure, two seismic surface storage parameter figures and one wave impedance figure. The research results play an important role in the acceptance inspection of "863" project, which indicates that the method is a practical way to identify gas hydrate. Geophysical exploration and neural network algorithm for gas hydrate are under development, and many areas still need improvement. Hydrate recognition system incorporates three methods:well logging, seismic identification and seismic—logging joint identification. The results can be displayed both in profile and space plane.