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.

Active Publication Date: 2021-10-26
SOUTHWEST PETROLEUM UNIV
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[0004] The purpose of the present invention is to provide a method for predicting formation pore pressure based on machine learning, wh

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  • Formation pore pressure prediction method based on machine learning
  • Formation pore pressure prediction method based on machine learning
  • Formation pore pressure prediction method based on machine learning

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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;

[005...

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Abstract

The invention relates to the technical field of logging engineering, aims to provide a formation pore pressure prediction method based on machine learning, and solves the problems that an existing prediction method is lower in prediction result accuracy and not ideal in effect. According to the technical scheme, the formation pore pressure prediction method based on machine learning comprises the following prediction steps of a, processing and preparing data, namely collecting the related logging data and the related rock physical property parameters; b, determining a sensitive curve, namely preparing a reference sequence and a comparison sequence of a grey relational degree method, and determining a sensitive logging curve; c, training and testing a model, namely dividing an original data set into a training set and a testing set, and inputting the training set into a gradient boosting regression tree model to obtain an optimal model; and d, predicting the formation pore pressure, namely taking the sensitive logging curve as an input feature vector of the optimal model to predict the reservoir formation pressure. The method has the advantages of better 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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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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