ISSN 1673-8217 CN 41-1388/TE
主管:中国石油化工集团有限公司 主办:中国石油化工股份有限公司河南油田分公司
关振良, 谢丛娇, 杨坤. 1998: 神经网络模型预测采油指数. 石油地质与工程, 12(03): 22-24+60.
引用本文: 关振良, 谢丛娇, 杨坤. 1998: 神经网络模型预测采油指数. 石油地质与工程, 12(03): 22-24+60.
Guan Zhenliang, . 1998: An Approach for Productivity Index Prediction Using Neural Networks. Petroleum Geology and Engineering, 12(03): 22-24+60.
Citation: Guan Zhenliang, . 1998: An Approach for Productivity Index Prediction Using Neural Networks. Petroleum Geology and Engineering, 12(03): 22-24+60.

神经网络模型预测采油指数

An Approach for Productivity Index Prediction Using Neural Networks

  • 摘要: 从实用的观点来看,采油指数是油层生产能力的直接标志,通常是用试井的方式获得,但在多层开采的条件下,用试井的方法求取和预测采油指数是极为有限的。本文介绍了油藏工程中一种新的实现方法─用人工神经网络模型求取开发初期分采时的采油指数。研究表明该方法是一种实用、有效、准确性较高的采油指数间接求取方法。

     

    Abstract: Productivity index, a direct mark of production potentials of producing formations from the point of view of practicality, usually is obtained through well testing. In multilayered reservoirs, however, productivity indexes derived from well test data are limited.This paper introduces a new method, which extracts the productivity indexes of seperate zone production in the initial period of development with artificial neural networks. It is a practical and effective indirect productivity index extraction method with high accuracy.

     

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