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New-drilled well workload prediction method based on ensemble learning

A workload and drilling technology, which is applied in the fields of geophysical exploration and artificial intelligence, can solve problems such as one-sided mapping of new drilling workload, fixed parameters, and lack of universality, so as to improve stability and operating speed, improve accuracy, The effect of reducing negative effects

Inactive Publication Date: 2021-02-05
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

[0004] In order to overcome the problem that the traditional empirical formula method involves fixed parameters, the mapping relationship with the new drilling workload is relatively one-sided, and lacks universality, the present invention proposes a new drilling workload prediction method based on ensemble learning

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Embodiment Construction

[0022]Combine belowfigure 1 To further describe the present invention in detail:

[0023]A. Construct historical data set of reservoir development:

[0024]Extract oil field geological data and real-time oil well reserve data from seismic database and oil reservoir history database, map the oil field geological data and real-time oil well reserve data to the same data source, and use the five-year number of new wells adjusted by the history of the oil field block as the new drilling Workload prediction standard, after preprocessing, obtain the historical data set of reservoir development.

[0025]B. Using particle swarm optimization method to optimize the key hyperparameter combination:

[0026](1) Initial model construction

[0027]Build a new drilling workload prediction initial model based on random forest, select the maximum feature number of a single decision tree, the number of subtrees and the minimum sample leaf size as key hyperparameter combinations, and randomly select appropriate data ...

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Abstract

The invention discloses a new-drilled well workload prediction method based on ensemble learning. The method is characterized in that a new-drilled well workload prediction model based on a random forest is built, a key hyper-parameter combination of the new-drilled well workload prediction model is optimized through a particle swarm optimization method, and a weighted voting mechanism is added ina decision stage; by adjusting the weight value of the weak classification decision tree, the generalization error of new-drilled well workload prediction is reduced, and the precision of the new-drilled well workload prediction model is improved. The key hyper-parameter combination in the new-drilled well workload prediction model is optimized by adopting the particle swarm optimization method,the influence of noise data in oil reservoir development historical data is reduced, and the stability and the operation speed of the random forest method are improved; a weighted voting mechanism isadded in the decision stage, wherein the weight proportion of a high-score decision tree is increased and meawhile the negative influence of a low-score decision tree on a prediction result is reduced, and thereby the precision of a new-drilled well workload prediction model is improved.

Description

Technical field[0001]The invention belongs to the field of geophysical exploration and artificial intelligence, and specifically relates to a new drilling workload prediction method based on integrated learning.Background technique[0002]The drilling workload that can be implemented is the main manifestation of the development potential of the old oilfield. Appropriate adjustment of the new drilling workload is of great significance for the oilfield to further develop the fine potential work and improve the recovery rate of the old oilfield. Traditional new drilling workload prediction generally uses empirical formulas for quantitative evaluation, which has certain guiding significance for new drilling workload prediction. However, the empirical formula involves fixed parameters, and it is easy to ignore some potential mapping relationships. When applied to oilfields in the ultra-high water-cut development stage, the adaptability is poor, and there are major limitations.[0003]With th...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/02G06N3/00G06N20/20
CPCG06Q10/04G06Q50/02G06N3/006G06N20/20
Inventor 李克文柯翠虹张震涛雷永秀李绍辉
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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