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| Prestack seismic stochastic inversion method based on spatial co-simulation |
| CAO Yamei1,2, ZHOU Hui1,2, YU Bo3, ZHANG Yuangao4, DAI Shili4 |
1. National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum(Beijing), Beijing 102249, China;
2. CNPC Key Laboratory of Geophysical Exploration, China University of Petroleum(Beijing), Beijing 102249, China;
3. School of Earth Sciences, Northeast Petroleum University, Daqing, Heilongjiang 163318, China;
4. Exploration Department of Daqing Oilfield Company Ltd, Daqing, Heilongjiang 163453, China |
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Abstract Sequential stochastic simulation is generally used to characterize reservoir heterogeneity both in the iterative stochastic inversion and linear Bayesian stochastic inversion. Most sequential simulation methods rely on variograms or training images to describe the spatial correlation of model parameters. In addition， the simulation results are required to be calculated point by point， which makes parallel computing difficult and reduces computational efficiency. Therefore， a conditioned fast Fourier transform moving average （FFT-MA） is introduced into the linear inversion framework， and a prestack seismic stochastic inversion method based on spatial co-simulation is proposed. Firstly， the posterior probability distribution of elastic parameters is obtained by integrating seismic data and low-frequency well logging information under the Bayesian framework. Then， the probability field is generated according to the FFT-MA algorithm. Well logging data is taken as conditional data to conduct Bayesian posterior probability field co-simulation. High-resolution prestack stochastic inversion results of elastic parameters constrained by well logging and seismic data are thus obtained. No iteration and update of model parameters is required by the method， which greatly improves the computational efficiency of stochastic inversion. Finally， the validity of the proposed method is demonstrated by numerical model examples and practical data application cases. The numerical model examples show that the proposed method has significant advantages over conventional methods in terms of high-resolution reservoir prediction and computational efficiency. Small-scale reservoir characterizations can be explored stably and accurately. The practical data application cases show that the high-resolution inversion results obtained by the proposed method match well with well logging data. The practicality of stochastic inversion in characterizing quantitatively thin reservoirs is greatly improved.
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Received: 28 March 2023
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2024, Vol. 59 
