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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0.53km2,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 E_1f31-1 successfully in the north fault block of the Minqiao oilfield.The results have led to the increase in 8 new oil wells,0.53km2 of new oil-bearing area and 23.62 million tons of new geological reserves of oil as the economic benefits in the reservoir of E_1f31-1.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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基本信息:
DOI：10.16028/j.1009-2722.2015.11008
中图分类号:P618.13
引用信息:
[1]陈清华,程祥,王晶,等.人工神经网络在闵北断块低阻油层识别上的应用[J].海洋地质前沿,2015,31(11):52-57.DOI:10.16028/j.1009-2722.2015.11008.
基金信息:
“陈堡油田周边地区滚动勘探评价”(31450001-13-FW2099-0004)
2015-11-28
2015-11-28