Pre-stack inversion thin layer oil/gas-bearing possibility identifying method

Abstract

The invention provides a pre-stack inversion thin layer oil / gas-bearing possibility identifying method. The pre-stack inversion thin layer oil / gas-bearing possibility identifying method comprises the following steps of: (1) constructing interpreting data and logging data by utilizing earthquake, and establishing an initial elastic parameter model based on a deposition mode; (2) obtaining an initial model and a model restrain range of random inversion through determination inversion based on a Baysian principle; (3) carrying out random sampling on three-parameter-related Monte Carlo simulationestablished by utilizing rock physical diagnosis and regression analysis so as to obtain a logging scale model; (4) calculating to obtain an elastic parameter model under an earthquake scale by utilizing a Hash in-Shtrikman boundary average method; (5) simulating forwards an angular domain pre-stack earthquake record and a actual earthquake record to calculate a cost function and calculate acceptance probability of the elastic parameter model, and determining a new logging scale elastic parameter model according to the acceptance probability; (6) iterating repeatedly the steps of (3), (4) and(5) and determining an optimal logging scale elastic parameter mode as a final result of earthquake three-parameter pre-stack inversion; and (7) carrying out thin layer oil / gas-bearing possibility identification of an oil reservoir by utilizing a three-parameter three-dimensional space intersection method.

Description

technical field [0001] The invention relates to oil and gas field seismic exploration and oil and gas reservoir prediction and description methods, in particular to a pre-stack inversion thin-layer oil-gas identification method. Background technique [0002] Seismic inversion is an effective method to obtain internal images of subsurface media and describe reservoirs in detail, and it is also the ultimate form of high-resolution seismic exploration. Seismic data inversion greatly increases the value of reservoir characterization. With the increase of exploration degree of oil and gas reservoirs, it is very difficult to find large-scale structural oil and gas reservoirs in oil and gas areas with relatively high degree of exploration and development. The oil and gas reservoir exploration and development workers are more interested in deepening the understanding of the explored and developed oil and gas reservoirs, making a more detailed description and characterization of the...

AI Technical Summary

Problems solved by technology

However, high-frequency changes in elastic properties do not affect seismic amplitudes, so estimates of the high-frequency part of the model parameters are highly non-unique

[0003] In short, the main problems in the current research on the method of identifying thin layers of oil and gas based on seismic prestack inversion are: 1. The structure of heterogeneous mudstone reservoirs is complex, and the thickness of thin mudstone and sandstone layers exceeds that based on seismic data. The current seismic inversion method cannot realize the fine description of a single thin layer in the reservoir, and it is even more difficult to realize the quantitative analysis and evaluation of a single thin layer based on seismic data

2. The measurement scale difference between logging data and seismic data hinders the combination of the two

Therefore, the relationship between reservoir parameters and rock elastic parameters established based on well logging data and laboratory core measurement data cannot be directly applied to conventional seismic wave impedance inversion data interpretation and inversion process parameter constraints.

3. Geophysical data of different scales cannot be directly and effectively converted to each other, resulting in that geophysical data of different scales cannot be directly combined to obtain high-resolution elastic parameter inversion results. layer reservoir

4. Stochastic inversion has strong non-uniqueness and a huge amount of calculation, but the constraint method to reduce the multi-solution of inversion results is not ideal, and needs to be further improved

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