Improved Prior Construction for Probabilistic Seismic Prediction

Seismic inversion is an effective way to investigate the lithology and fluid in hydrocarbon-bearing reservoirs. In addition to obtaining inversion results, probabilistic seismic prediction can be used for uncertainty evaluation. Its prior probability is usually assumed to follow a specific distribution, which limits the prediction accuracy. By considering both the p-norm and total variation (TV) constraints, an improved prior is proposed. The p-norm constraint is first introduced into the probabilistic seismic prediction, which can be reduced to other probability distribution forms. With the p-norm constraint, the probability density function is updated. Then, the posterior probability (PP) with both the p-norm and TV constraints is re-derived. By analysis, the proposed approach helps to preserve the boundary and highlight the sparsity. Compared with a specific prior distribution, the proposed prior is more flexible and can effectively improve the prediction accuracy. The proposed approach is discussed in detail in terms of probabilistic prediction by using synthetic data. Both synthetic data and field data tests demonstrate the superiority of the proposed prior. The construction of the prior with p-norm and TV constraints is of great help to improve probabilistic prediction.

Automated summary

Seismic inversion is an effective method for investigating lithology and fluid in hydrocarbon-bearing reservoirs. Probabilistic seismic prediction, along with an improved prior considering p-norm and total variation constraints, enhances the accuracy of predictions by updating the probability density function and preserving boundaries while highlighting sparsity.