摘要:
随着油气勘探程度的不断深化,非常规油气藏在油气勘探中不断被发现,在实际的勘探过程中低阻油层的储量和产量都在不断增加。低阻油层作为一种非常规储层,其含油性受多个因素影响。常规测井解释方法评价低阻油层有很大的困难。针对闵桥油田断裂构造错综复杂、油层低阻特征典型、测井解释难度大等特点,利用BP人工神经网络算法,对已知样本进行学习获得识别模式,并使用自编软件,成功识别了闵北断块阜宁组三段低阻油层。识别结果显示,该层新增油井8口,含油面积增加0.53 km2,新增石油地质储量23.62万t,经济效益显著;同时深化了对该断块油气分布规律与油藏类型的认识,理论意义重要。
Abstract:
More and more unconventional reservoirs have been discovered recently in oil and gas exploration. The reserves and production of low resistivity reservoirs are also increasing. As an unconventional one, the low resistivity oil-bearing strata are affected by many factors and the common logging interpretation method is not so efficient in evaluation of the oil reservoir. The Minqiao Oilfield is a complex faulting structure and a typical low resistance reservoir. The logging data is difficult for interpretation. The BP artificial neural network algorithm, learnt from known samples, is applied in this case using the self-compiled software. We have recognized the low resistivity reservoir E1f31-1 successfully in the north fault block of the Minqiao oilfield. The results have led to the increase in 8 new oil wells, 0.53 km2 of new oil-bearing area and 23.62 million tons of new geological reserves of oil as the economic benefits in the reservoir of E1f31-1. It is also contributed to the deepening of understanding of the oil and gas distribution pattern and reservoir type in the fault block, with great theoretical significance.
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