Formation pore pressure prediction method based on machine learning
A technology of formation pore pressure and prediction method, which is applied in the field of logging engineering, can solve the problems of low accuracy of prediction results and unsatisfactory effect, and achieves the effects of high reliability, reduced impact and wide application prospect.
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[0055] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0056] like figure 1 As shown, a machine learning-based prediction method for formation pore pressure includes the following prediction steps:
[0057] a. Data processing and preparation: collect relevant logging data and related petrophysical parameters; the data needs to be cleaned to screen out effective data; that is, ten kinds of logging curve data closely related to formation pore pressure in the logging data, Such as density (DEN), spontaneous potential (SP), natural gamma ray (GR), acoustic transit time (AC), borehole diameter (CAL), deep lateral resistivity (LLD), shallow lateral resistivity (LLS), porosity degree (POR), etc., in this embodiment, the above ten kinds of logging data are taken as examples;
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