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Abstract

Summary

These days machine learning models are used on a regular basis for various complex oil and gas tasks. One of the most popular business problems is well log data autointerpretation. However, these models, in most cases, require large amount of data which can be obtained only from mature oil fields. It assumes high variability in data due to different tools, well and measurements record conditions. It leads to noisy datasets with number inconsistencies affecting accuracy of model prediction and resulting in many misclassifications and outliers. Application of spatial geological information from other wells can increase prediction robustness.

The main aim of presented research is to develop a method of spatial geological data incorporation into conventional machine learning net pay intervals autointerpretation in pipeline in order to improve quality of model prediction. Approach for a spatial geological features aggregation was proposed. They were used for developing a spatial ranking model allowing estimation of geological consistency for each predicted net pay interval. Capability of the proposed methodology was proven by mathematical metrics and expert blind test. It was clearly shown that incorporation of spatial data dramatically increases machine learning models prediction quality and eliminate inconsistent intervals produced by noisy data.

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/content/papers/10.3997/2214-4609.202032088
2020-11-30
2024-04-28

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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202032088
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Improvement of Well Logs Autointerpretation Robustness via Application of Spatial Geological Information
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202032088
/content/papers/10.3997/2214-4609.202032088
/content/papers/10.3997/2214-4609.202032088
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