改进的贝叶斯迭代反演方法及其在白云岩致密储层识别的应用

doi:10.11720/wtyht.2019.1354

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Abstract

It is difficult to accurately predict the dolomite tight oil reservoir which has the characteristics of low porosity and low permeability by using the post-stack inversion,due to the small difference in acoustic impedance between the reservoir and its surrounding rock.Therefore,more abundant elastic information is needed.AVO inversion is an effective means to extract elastic information from pre-stack data.However,due to the noise and other factors,the pre-stack inversion equation has a strong ill-posed problem.Bayesian theory allows the construction of a regularization term by introducing a priori information about the model parameters,thereby effectively reducing the ill-posed problem of the inversion.Therefore,the modified Trivariate Cauchy constraint and the modified low-frequency constraint factor is introduced into the objective function,which can improve the ill-posed problem of the inversion,thus upgrading the accuracy of the inversion results.The iterative idea is used to address the non-linear nature of the proposed inverse operator.The P and S-wave velocity is updated in the iterations,which leads to more reliable results when applied to real data.Both the model data tests and the field data applications prove the validity and stability of the proposed method.Statistics show that,by using the proposed inversion method,the prediction accuracy rate of the reservoir thickness is as high as 89.5%.Therefore,this method has important reference significance for the exploration of similar siliceous reservoirs.

Key words： Bayesian inversion tight oil reservoir modified constraints S-wave data optimization reservoirs identification

Received: 27 September 2018 Published: 10 April 2019

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	Articles by authors
	Qi-Qi MA
	Zan-Dong SUN
	Liu-Xin YANG

Cite this article:

Qi-Qi MA,Zan-Dong SUN,Liu-Xin YANG. Modified Bayesian iterative inversion method and its application to dolomite tight oil reservoirs prediction[J]. Geophysical and Geochemical Exploration, 2019, 43(2): 234-243.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2019.1354 OR https://www.wutanyuhuatan.com/EN/Y2019/V43/I2/234

Comparison of Cauchy distribution constraint and modified Cauchy distribution constraint

Comparison of inverted results of R_s in different iterations and theoretical data
a—theory R_s;b—R_s obtained from the first iteration;c—the difference between the R_s obtained from the first iteration and the theoretical value;d—R_s obtained from the second iteration;e—the difference between the R_s obtained from the second iteration and the theoretical value;f—R_s obtained from the third iteration;g—the difference between the R_s obtained from the second iteration and the theoretical value

Synthetic gathers
a—Noise-free;b—S/N=2

Comparison of inversion results with real logging data
a—inverted P-impedance results (noise free);b—inverted S-impedance results (noise free);c—inverted density results;d—inverted P-impedance results (S/N=2);e—inverted S-impedance results (S/N=2);f—inverted density results (S/N=2)

Comparison of the correlation coefficients of inversion results

Cross-plot of P-impedance versus S-impedance of dolomite tight oil reservoirs

Comparison of inverted results of target layer
a—inverted I_p by conventional method;b—inverted I_s by conventional method;c—inverted I_p by new method;d—inverted I_s by new method

Comparison of inverted results of pseudo and well-logging data
a—P-impedance;b—S-impedance

Flow chart of reservoir thickness prediction

The prediction thickness of high-quality reservoirs of target layer

Statistics of actual dolomite thickness and predicted thickness in the study area

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