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A method and a device for distinguishing exploratory well types by a weighted mahalanobis distance with optimized weight

A Mahalanobis distance and discriminant model technology, applied in character and pattern recognition, instruments, data processing applications, etc., can solve problems such as inconvenience in wide application and complex operation process, and achieve the effect of improving discrimination accuracy and use efficiency

Pending Publication Date: 2021-06-18
PETROCHINA CO LTD
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Problems solved by technology

[0005] The existing weighted Mahalanobis distance discriminant method fixes the weight of each factor, causing different samples to modify the weight, which is not easy to be widely used, and the operation process is too complicated

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  • A method and a device for distinguishing exploratory well types by a weighted mahalanobis distance with optimized weight
  • A method and a device for distinguishing exploratory well types by a weighted mahalanobis distance with optimized weight
  • A method and a device for distinguishing exploratory well types by a weighted mahalanobis distance with optimized weight

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Embodiment Construction

[0048] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0049] Those skilled in the art should understand that the embodiments of the present invention may be provided as methods, systems, or computer program products. Accordingly, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware ...

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Abstract

The invention discloses a method and a device for distinguishing exploratory well types by a weighted mahalanobis distance with optimized weight. The method comprises the following steps: respectively acquiring geological factor parameters of each well in an oil-gas well set and a dry well set; calculating an oil and gas well parameter mean vector and an oil and gas well parameter covariance matrix, and calculating a dry well parameter mean vector and a dry well parameter covariance matrix; generating a weighted mahalanobis distance discrimination model according to the oil and gas well parameter mean vector, the oil and gas well parameter covariance matrix, the dry well parameter mean vector, the dry well parameter covariance matrix and a preset weight diagonal matrix; optimizing and adjusting the weight of each geological factor parameter in the weight diagonal matrix continuously, so that the discriminating error rate of the generated weighted mahalanobis distance discriminating model on the oil and gas well set and the dry well set is the lowest, and the weighted mahalanobis distance discriminating model with the optimized weight is obtained. According to the invention, the exploratory well type discrimination precision and the use efficiency are improved.

Description

technical field [0001] The present invention relates to the type discrimination of exploratory wells, in particular to a method and device for discriminating the types of exploratory wells by weighted Mahalanobis distance with optimized weights. Background technique [0002] Oil and gas exploration has the characteristics of high investment and high output. However, due to the particularities of long investment cycle, slow recovery, and high risk, pre-drilling risk assessment, that is, identification of exploration well types, has become an indispensable prerequisite for oil and gas exploration. [0003] In 2007, Suyun Hu et al. proposed a method to predict the spatial distribution of oil and gas by using multivariate statistics and information processing technology. This method uses the Mahalanobis distance discriminant method to integrate information, uses the Bayesian formula to calculate the oil and gas probability of known samples, and thus establishes the oil and gas p...

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Application Information

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IPC IPC(8): G06K9/62G06Q50/02
CPCG06Q50/02G06F18/22G06F18/24G06F18/214
Inventor 郭秋麟胡素云刘继丰
Owner PETROCHINA CO LTD
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