The Well Productivity Index Determination Based on Machine Learning Approaches

Autor:	A. Kosarev, Artyom Semenikhin, Arseniy Gruzdev, Igor Simon, V. Babov, Vitaly Koryabkin, Y. Simonov
Rok vydání:	2020
Předmět:	Index (economics) business.industry Computer science Process (engineering) Petrophysics Machine learning computer.software_genre Interpretation (model theory) Approximation error Artificial intelligence business Productivity computer Digital signal processing
Zdroj:	First EAGE Digitalization Conference and Exhibition.
DOI:	10.3997/2214-4609.202032094
Popis:	Summary In this paper, we presented an approach to building a machine learning model for predicting well productivity index. The proposed approach is based mainly on LWD data and well log interpretation results, based on the petrophysical model of the oilfield and digital signal processing approaches. The proposed approach was tested on historical data from the Novoportovskoye oilfield. The model was tested based on the LOOCV cross-validation process. As a result, the median relative error over wells is less than 20%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5a4f5d7302730717b454a9f686386cd7 https://doi.org/10.3997/2214-4609.202032094 Zobrazit plný text záznamu