The Format Criterion Of Doctoral Dissertation

Exploration and development of oil and gas fields are limited by the resolution of seismic data,making it difficult to accurately characterize the spatial distribution of reservoirs for complex oil and gas reservoirs.Therefore,the development of inversion and processing methods that can improve resolution has become a focus of current research.Conventional deterministic inversion methods have been developed for a long time and have mature algorithms.However,due to the limited resolution of seismic data,inversion cannot accurately predict underground reservoirs.On the other hand,stochastic inversion methods can provide multiple high-resolution inversion results with equal probabilities by combining observation data with seismic data.However,due to the lack of prior information constraints,the results of stochastic inversion are prone to multiple solutions and low reliability,especially for highly heterogeneous reservoirs and severe structural changes,where high-frequency components have large uncertainties.This paper proposes a model-constrained stochastic inversion method by combining various theoretical methods based on the two aforementioned methods.This method combines geological statistics,stochastic simulation,and Bayesian theory.By integrating geological data with well logging data and using sequential Gaussian simulation and mathematical optimization algorithms to establish a priori initial model,this method constrains the space of inversion models to improve the accuracy of inversion results.Combining seismic data with phase-controlled nonlinear stochastic inversion method enables a fine description of reservoirs.This method combines the advantages of model-based and stochastic-based methods,further restricts the candidate solution space of stochastic inversion,improves the accuracy of reservoir prediction,and achieves good prediction results.