Abstract
Research on the modeling of rock mechanics parameters is of great significance to the exploration of oil and gas. The use of logging data with the Kriging interpolation to study rock mechanics parameters has been proven to be effective in reservoir prediction and other oilfield applications and can provide additional data. However, there will sometimes be a great deviation due to the limited samples and the strong heterogeneity of a layer. To solve this problem, a new approach was proposed to calibrate rock mechanical models through the statistical analysis of logging data. A module was developed to calibrate rock mechanics parameters automatically, which was then applied to the Wangyao area of the Ansai oilfield. This method significantly improved the accuracy of rock mechanics modeling.
Highlights
In the exploration and development of lithological oil-gas reservoirs, rock mechanics parameters, such as Young’s modulus, Poisson’s ratio, the effective stress coefficient, and the angle of internal friction, are crucial to geo-stress simulation, wellbore stability analysis, sweet spot prediction, and simulation techniques
The module was developed to constrain the rock mechanics parameters. This proposed method was applied successfully to the Wangyao area of the Ansai oilfield, and the results showed that the accuracy of rock mechanics modeling improved significantly
Through analysis of the rock mechanics parameters of the wellbore and initial model, the optimal coefficients were calculated, which were important in building the constraint model
Summary
In the exploration and development of lithological oil-gas reservoirs, rock mechanics parameters, such as Young’s modulus, Poisson’s ratio, the effective stress coefficient, and the angle of internal friction, are crucial to geo-stress simulation, wellbore stability analysis, sweet spot prediction, and simulation techniques. As the use of unconventional resources have developed rapidly, high-accuracy rock mechanics modeling has drawn a great degree of attention and been studied by experts worldwide, and many research results have been put into field application. Considering the unbiased optimal advantages, the Kriging method is one of the most widely used methods in rock mechanics modeling. Roustant optimized the method to solve a covariance function by proposing a nonnegative solution of linear equations to eliminate part of the subjective impacts[5]. Hu improved the results of Kriging, which can be impacted by scaling, with the Bayesian-based collocated co-Kriging method [7]. The above methods can improve the accuracy mathematically but fail to consider the spatial
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