关键词: 沙埝油田, 低电阻率油层, 成因, 识别, BP神经网络, 苏北盆地

Abstract:

It′s very difficult to distinguish hydrocarbon-bearing reservoirs from water bearing reservoirs electrically because there is the similar resistivity between them.The analysis on lithology,physical property,clay minerals,heavy minerals,salinity of formation water,thickness of reservoirs,well logs and well testing for E¹f^1₃ reservoirs in Sha 19 Fault Block of Shanian Oilfield shows that there are low resistivity reservoirs there,and fine grain lithology,high salinity of formation water,thin interbedding sandstone and mudstone are dominant factors resulting in low resistivity reservoirs in Shanian Oilfield.With BP artificial neural network,five logs which can reflect the property of low resistivity reservoirs is taken as input parameters to establish systematical learning samples and forecast unknown reservoirs.This method is successively applied to identify low resistivity reservoirs in Shanian oilfield.

Key words: Shanian Oilfield, low resistivity reservoirs, factors, identification, BP artificial neural network, Subei Basin