Study On Seismic Reservoir Parameters Nonlinear Inversion And Prediction Methods

At present,the prediction of lithologic hydrocarbon reservoir using seismic exploration still is in accumulation period of exploratory and technology,and most of those technologies is in the exploratory investigation of theoretical model,too. However,seismic inversion technique has always been one of core techniques of seismic exploration,in which seismic reflection data has been used in extracting seismic attributes and derivating the distribution of wave impedance or velocity that have been used to estimate the reservoir parameters,realize reservoir prediction and description and apply the reliable basic data for hydrocarbon explorationUsing seismic attributes can extract the information that others methods have never obtained,which has been applied to predict the three-dimensional distribution of reservoir thickness,porosity,permeability,saturation and so on. However,these parameters can not be measured directly but well logging,which requires to build the relationship between various parameters obtained by logging and seismic data. The relationship of these parameters and seismic data is not one-to-one correspondence,it is hard to describe the relationship using an accurate algorithm precisely. Presently,there is one of methods in common use that extracts various seismic attributes from the seismic data using extracting technique,builds a relation between primary attributes and parameters obtained by logging,distributes the relation over the whole three-dimensional space by seismic data from point to surface,and finally predicts reservoir parameters.Based on investigating sufficiently to the present researching situation of reservoir prediction,Artificial Neural Network,Simulating Anneal Algorithm and Particle Swarm Optimization in home and abroad,the paper arms at the research contents of reservoir prediction,demonstrates and analyzes seismic attributes extraction and optimization method,constrained wave impedance inversion technique and acoustic wave curve reconstruction technique etc,develops multi-parameters rebuilding acoustic wave curve method,SA-ANN technique based on Immune Programming,and Competition PSO-ANN technique,moreover,applies them to actual data.Armed at the limit in reconstruction methods existed,this paper proposes one method that uses multi-non-acoustic logging information that is sensitive to reservoir,and rebuilds the original acoustic wave curve. Because the method uses different logging information,saves all of the frequency information of acoustic wave curve,syncretizes all of the information of the curves participated in reconstruction into curve rebuilt which not only keeps the original time-depth relation of acoustic wave curve unchanging,but also realizes enhancing the difference between reservoir and surrounding rock,giving prominence to the property of reservoir and imaging the hairline of formation lithology,this technique can resolve the difficult problem about lithologic discrimination and thin bed resolution in reservoir quantitative forecast of the thin interbeds of sandstone and mudstone.After completely comparative analyzing the classification , extracting and optimization methods of seismic data attributes,the reasonable and scientific extracting and optimization methods of seismic data attributes are choose and used in extracting and optimization of attributes,offer reservoir prediction the attributes that are much more in accord with actual geology situation.Deeply studying and analyzing theory of Back Propagation Neural Network and SAA,the shortage that the velocity of searching and constringency is slow,at the same time,the searching space of the answer of SAA is limited in little area under per temperature so that it is likely to induce the global optimization answer not to be searched is known. Because Immune Programming is one of global searching algorithm based on Evolutionary Programming,the new algorithm that can effectively search global optimization answer will be gained and overcome the shortage of SAA if Immune Programming is combined with SAA. For conquering the flaw of BPNN and SAA,the paper researches and proposes a SAA based on Immune Programming,moreover,brings forward a NN technique in which network parameters are trained by replacing BP algorithm with SAA improved. At the same time,the study algorithm of this NN is researched and analyzed. The satisfactory effect is acquired when it is used in reservoir parameters prediction for seismic attributes optimized.The limitation of PSO is brought forth enough after the principle of PSO is studied. There are two primary limitations that the searching velocity will be slow down in the evening of evolutionary and PSO trend to over-constringency (prematurity) so that only the local optimization answer will be obtained. So the competition idea between filial generation and father generation in the Evolutionary Programming Algorithm is introduced into the searching and constringency course of PSO to form a competition PSO in order to enhance the velocity of searching and avoid the prematurity phenomenon. When appling competition PSO to train the network parameters can overcome the inherent shortcoming existed in BPNN,using this optimization algorithm optimizes the structure of network in order to improve the capability of network. The network trained is applied to actual data to predict reservoir parameters,the predicting result accords with actual logging data basically.The result and production of study in this paper have direction significance and applied value for oil exploration,exploitation and scientific research.