APPLICATION OF NEURAL NETWORK EXPERT SYSTEM TO PREDICTING THE PRODUCIBLE OIL RESERVES—An example of Fanjia Oil district in Shengli Oilfields

Peng Dunlu; Xu Shijin; Wang Rucheng; Guo Yanjun

doi:10.11743/ogg19990207

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APPLICATION OF NEURAL NETWORK EXPERT SYSTEM TO PREDICTING THE PRODUCIBLE OIL RESERVES—An example of Fanjia Oil district in Shengli Oilfields
Vol. 20, Issue 2, Pages: 129-132(1999)
Online First：25 June 1999，

Published：1999
DOI： 10.11743/ogg19990207
稿件说明：

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DOI：

Peng Dunlu, Xu Shijin, Wang Rucheng, et al. APPLICATION OF NEURAL NETWORK EXPERT SYSTEM TO PREDICTING THE PRODUCIBLE OIL RESERVES—An example of Fanjia Oil district in Shengli Oilfields[J]. 1999, 20(2): 129-132. DOI： 10.11743/ogg19990207.

摘要

影响石油可采储量的因素是多种多样的

很难用一简单的表达式来描述两者之间的关系。神经网络专家系统为解决这一问题提供了新途径。具体过程为(1)选取储层参数(累积厚度、温度、有效孔隙度、有效渗透率、压力)和原油参数(含油饱和度、地下原油粘度和密度)等8个参数作为特征参数;(2)将参数进行标准化和归一化处理;(3)以樊家油田8个已知采油区作为学习样本对网络进行训练;(4)运用已训练好的神经网络专家系统对未知油区进行预测。预测结果与实际情况吻合很好

误差都在允许范围之内。

Abstract

There are various kinds of factors that effect producible oil reserves.It is difficult to describe the relationship between the producible oil reserves and the effecting factors by an expression simply.Neural network expert system provides a new approach to solve the problem.Particular process is like that:1. select five reservoir parameters (cumulative thickness

temperature

effective porosity and permeability

pressure) and three crude oil parameters (saturation

viscosity and density)as characteristic parameters;2.the parameters should be standardized and normalized;3.take 8 known oil recovery areas in Fanjia sub oil field as learning samples to train the network system;4.use the trained system to predict the producible oil reserves of unkown oil areas.The prediction results fitted well with actural circumstances and the errors are exceptable.