Intelligent Fluid Identification Based on the AdaBoost Machine Learning Algorithm for Reservoirs in Daniudi Gas Field

HAN Yujiao

doi:10.11911/syztjs.2022018

Volume 50 Issue 1

Mar. 2022

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Petroleum Drilling Techniques > 2022 > 50(1): 112-118. > DOI: 10.11911/syztjs.2022018

HAN Yujiao. Intelligent Fluid Identification Based on the AdaBoost Machine Learning Algorithm for Reservoirs in Daniudi Gas Field[J]. Petroleum Drilling Techniques, 2022, 50(1): 112-118. DOI: 10.11911/syztjs.2022018

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Intelligent Fluid Identification Based on the AdaBoost Machine Learning Algorithm for Reservoirs in Daniudi Gas Field

HAN Yujiao^{1, 2,}

1.
State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing, 102206, China
2.
Sinopec Research Institute of Petroleum Engineering, Beijing, 102206, China

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Received Date: September 06, 2021
Revised Date: December 26, 2022
Accepted Date: February 08, 2022
Available Online: February 13, 2022

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Abstract

Abstract

Complex reservoirs in Daniudi Gas Field are characterized by diverse mineral components, complex reservoir space, and strong heterogeneity, which make fluid identification difficult. To improve the accuracy rate and interpretation efficiency of fluid identification in complex reservoirs, Daniudi Gas Field, with its extensively developed low-resistance gas reservoirs, was taken as the main research object. Then, four strong classifier models were formed by the Adaboost machine learning algorithm with mainstream intelligent algorithms (such as logical classification and decision tree) as weak classifiers. The input parameters of the model were optimized based on the analysis of the genesis mechanism of the low-resistance gas reservoir, the parameters were optimized on the basis of conventional well logging, oil testing and production testing data, etc. The above model was applied to the data of 6 actual wells. The results showed that the strong classifier achieved the best identification effect by using the decision tree algorithm as the weak classifier, with the fluid identification accuracy of 86.5% and the F1 value up to 86.6%. The results indicates that this method is effective for identifying fluid with conventional logging data for low-resistance gas reservoirs, and providing new ideas for fluid evaluation.
- complex reservoirs,
- fluid identification,
- machine learning,
- intelligent identification; DaniudiGas Filed

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Intelligent Fluid Identification Based on the AdaBoost Machine Learning Algorithm for Reservoirs in Daniudi Gas Field

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