| Oil and natural gas is long known as the industrial blood, which is an important strategic resource of a country and related to the economic and social development. In recent years, as easy to develop unexplored areas gradually reduce, geological exploration researchers have taken the focus of the oil and natural gas exploration target to the hidden reservoir and complex reservoir construction, those technical requirements are higher. So just rely on the physical or petrophysical parameters of reservoir could not fully meet the needs of oil and natural gas exploration, and using the pre-stack seismic data to carry out high resolution seismic joint inversion methods for estimating physical and petrophysical parameters of reservoir has become the hot topic in academia and industry.Based on the review of the background and significance of high resolution seismic data processing and seismic inversion, we summarized out two unanswered questions of the pre-stack seismic inversion:1) how to get the high resolution seismic data; 2) how to realize the high resolution pre-stack seismic joint inversion with deterministic optimization method. In order to solve above two problems, this paper did the specific works of the innovation as follows:1. For the question 1), this paper proposed a new high resolution seismic data processing method based on BP artificial neural network method. Firstly, we used BP artificial neural network to establish the mapping relationship between the amplitude spectrum of the practical seismic information of nearby the well and the compensation coefficient. Secondly, we used the relationship to calculate each compensation coefficient of amplitude spectrum of seismic records without compensation. Thirdly, we processed the compensation coefficient with weighted smoothing and adaptive compensation location selection method respectively. Finally it acted on the amplitude spectrum to get the high resolution seismic records. This method takes the spectrum width of logging data as the compensation standard, which overcomes the shortcomings of those general spectrum methods which lack of quantitative compensation standard and enhance the compensation basis.2. For the question 2), this paper proposed a new method for estimating physical and petrophysical parameters of reservoir based on double parameters elastic velocity model with deterministic seismic pre-stack inversion. This method used double parameters elastic velocity model to build up the relationship between pre-stack data and petrophysical parameters We treated the joint posterior probability of physical and petrophysical parameters as the objective function under the Bayesian architecture, and by using the high resolution seismic data processing methods to improve the resolution of the initial value of joint inversion. we used the adaptive variable step length optimization method to get higher resolution physical and petrophysical parameters of reservoir in the end. On the one hand, this method adopts double parameters elastic velocity model, compared with the other rock physical model, this model can better establish the relationship between physical parameters and the pore shape parameter, help to know the influence of pore shape on the reservoir elastic properties. On the other hand, this method adopts the deterministic optimization method to establish the inversion framework and solve it, compared with the stochastic optimization method, it has faster inversion speed and higher precision.The proposed methods were tested by using the real data respectively, from the testing results, the new high resolution seismic data processing method has the obvious effect in raising the resolution of seismic data. The new joint inversion of deterministic optimization method has good convergence speed and steady effect, and the inversion results fit the well logging curve well. It proved that this method conform to the requirements of the pre-stack inversion. |
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