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Oil well productivity main control factor analysis method and system, equipment and storage medium

A technology of main control factors and analysis methods, applied in the field of oil and gas field development, can solve problems such as machine learning algorithm deviation and inaccurate data, and achieve the effect of improving accuracy

Pending Publication Date: 2022-02-08
CHINA NATIONAL OFFSHORE OIL (CHINA) CO LTD +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

As a result, a large amount of inaccurate data is used, and the analysis results of machine learning algorithms deviate greatly from existing oilfield development theories

Method used

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  • Oil well productivity main control factor analysis method and system, equipment and storage medium
  • Oil well productivity main control factor analysis method and system, equipment and storage medium
  • Oil well productivity main control factor analysis method and system, equipment and storage medium

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Experimental program
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Effect test

Embodiment 1

[0047] like figure 1 As shown, a method for analyzing the main controlling factors of oil well productivity provided in this embodiment includes the following steps:

[0048] 1) Perform data cleaning on the production dynamic factors and static geological factors in the obtained oil well data;

[0049] 2) Independently analyze the production dynamic factors and static geological factors after data cleaning, and use machine learning algorithms to sort the main control factors of oil well productivity, and obtain the importance ranking of each production dynamic factor and static geological factors.

[0050] Preferably, in the above step 1), the obtained oil well data can be divided into two categories: production dynamic factors and static geological factors.

[0051] Specifically, production dynamic factors include: date, daily production time, daily oil production, water cut, gas-oil ratio, production pressure difference, wellhead pressure, casing pressure, pump frequency, n...

Embodiment 2

[0090] In this embodiment, 87 oil wells in the P oilfield are taken as an example, and the method of Embodiment 1 is used to analyze the main controlling factors of oil well productivity. details as follows:

[0091] (1) The data cleaning method combined with the prior knowledge of oilfield development theory is used to process the production dynamic and static geological data of oil wells, and W1 well is selected as a demonstration example.

[0092] According to the data cleaning method described in Example 1, the production dynamic data such as daily oil production, water cut, gas-oil ratio, production pressure difference, wellhead pressure, casing pressure, pump frequency, nozzle size, etc. are processed, and the processing results are as follows: Figure 2(a) ~ Figure 2(h) shown.

[0093] Table 1 shows the processing results of static geological data such as permeability, porosity, oil saturation, shale content, reservoir thickness, penetration thickness, crude oil densit...

Embodiment 3

[0104] Embodiment 1 above provides a method for analyzing main controlling factors of oil well productivity, and correspondingly, this embodiment provides a system for analyzing main controlling factors of oil well productivity. The recognition system provided in this embodiment can implement the method for analyzing the main controlling factors of oil well productivity in Embodiment 1, and the recognition system can be realized by software, hardware or a combination of software and hardware. For example, the system may include integrated or separate functional modules or functional units to execute corresponding steps in the methods of Embodiment 1. Since the identification system of this embodiment is basically similar to the method embodiment, the description process of this embodiment is relatively simple. For relevant parts, please refer to the part of the description of Embodiment 1. The embodiment of the system of this embodiment is only schematic .

[0105] A system f...

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Abstract

The invention relates to an oil well productivity main control factor analysis method and system, equipment and a storage medium. The method comprises the following steps that data cleaning is conducted on production dynamic factors and static geological factors in obtained oil well data; a variable control method is adopted, the cleaned production dynamic and static geological data are independently analyzed, the application modes of the data are respectively designed, a machine learning algorithm is used for sorting oil well productivity main control factors, and the importance sequence of each production dynamic factor and each static geological factor is obtained. According to the method and system, the applicability and the accuracy of a machine learning algorithm in the field of oil well productivity main control factor research can be improved by efficiently cleaning the production dynamic and static geological data and adopting a data use mode suitable for the characteristics of the production dynamic and static geological data. Therefore, the method and system can be widely applied to the field of oil-gas field development.

Description

technical field [0001] The invention relates to the field of oil and gas field development, in particular to a data-driven analysis method, system, equipment and storage medium for the main controlling factors of oil well productivity. Background technique [0002] The main controlling factors of oil well productivity are the basic research content of oil well productivity evaluation. The existing analysis methods of main controlling factors of oil well productivity include: theoretical formula, physical experiment, numerical simulation and data analysis methods. However, because there are many factors affecting oil well productivity, and there is a strong nonlinear relationship between oil well productivity and various influencing factors, the applicability of the above methods in complex real situations is limited. [0003] In order to more accurately analyze the nonlinear relationship between oil well productivity and various influencing factors, the machine learning meth...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/02G06F16/215G06N20/00
CPCG06Q10/0639G06Q50/02G06N20/00G06F16/215
Inventor 董银涛邱凌宋来明丁祖鹏卢川甘云雁陈冠中段锐杨烁
Owner CHINA NATIONAL OFFSHORE OIL (CHINA) CO LTD
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