A prediction method of formation pore pressure 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 unsatisfactory effect and low accuracy of prediction results, and achieves the effects of high reliability, saving drilling cost and wide application prospect.

Active Publication Date: 2021-11-26
SOUTHWEST PETROLEUM UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to provide a method for predicting formation pore pressure based on machine learning, which solves the problem that the accuracy of the prediction results of the existing prediction method is low and the effect is not ideal

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  • A prediction method of formation pore pressure based on machine learning
  • A prediction method of formation pore pressure based on machine learning
  • A prediction method of formation pore pressure based on machine learning

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Embodiment Construction

[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] Such as 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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Abstract

The invention relates to the technical field of well logging engineering, and aims to provide a method for predicting formation pore pressure based on machine learning, so as to solve the problems of low prediction result accuracy and unsatisfactory effect of existing prediction methods. The technical solution adopted is: a method for predicting formation pore pressure based on machine learning, including the following prediction steps: a. Data processing and preparation: collecting relevant logging data and related petrophysical parameters; b. Determining the sensitive curve: preparing gray The reference sequence and comparison sequence of the correlation degree method determine the sensitive logging curve; c. Model training and testing: the original data set is divided into a training set and a test set, and the training set is input into the gradient lifting regression tree model to obtain the optimal Model, d. Prediction of formation pore pressure: the sensitive well log curve is used as the input eigenvector of the optimal model to predict the formation pressure of the reservoir. The invention has the advantages of good prediction precision, wide prediction range, high reliability and the like.

Description

technical field [0001] The invention relates to the technical field of well logging engineering, in particular to a method for predicting formation pore pressure based on machine learning. Background technique [0002] In the field of geophysical logging, formation pore pressure refers to the force shared by gas and liquid in formation pores, also known as pore pressure or formation pressure. Formation pressure provides important information for the distribution, migration and accumulation of oil and gas, and is one of the basic data in petroleum exploration and development. In drilling engineering, the formation pressure is not only the basis for determining the density of drilling fluid and the structure of the well body, but also related to whether the drilling work can be carried out safely, quickly and economically. Oil and gas exploration practices have shown that abnormal pressure is closely related to the generation, migration and accumulation of oil and gas. Overpr...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06K9/62G06N20/00G06F119/14
CPCG06F30/27G06N20/00G06F2119/14G06F18/24323
Inventor 徐云贵李春茂黄旭日张荣虎曹卫平廖建平
Owner SOUTHWEST PETROLEUM UNIV
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