基于RBF神经网络的页岩气丰度预测  

Prediction of shale gas abundance based on RBF artificial neutral network

作　　者：杨宇宁[1,2] 王剑[2,3] 郭秀梅[1,2] 

机构地区：[1]成都理工大学研究生院,四川成都610082 [2]国土资源部沉积盆地与油气资源重点实验室成都地质矿产研究所,四川成都610082 [3]中国地质科学院研究生部,北京100037

出　　处：《西安石油大学学报（自然科学版）》2015年第6期45-49,8,共5页Journal of Xi’an Shiyou University（Natural Science Edition）

基　　金：国家自然科学基金项目"华南新元古代楔状地层沉积充填序列及大地构造研究"(编号:41030315)

摘　　要：对页岩气成藏条件进行分析认为,机质含量、类型、成熟度、储层厚度、埋藏深度和孔隙度等为影响页岩气成藏的主要因素;依据四川盆地和北美页岩气形成的地质条件的相似性和所收集的大量页岩气数据,利用RBF神经网络构建了各主要因素与资源量丰度的网络模型,并用MATLAB对网络模型进行训练。对Ohio和龙马溪组页岩资源丰度进行了预测,预测误差分别为27.5%和3.5%,表明RBF神经网络模型可较好预测页岩气产量,对页岩气开采和勘探具有参考价值。According to the analysis of shale gas accumulation conditions,it is held that the content,type and maturity of organic matter,and the thickness,buried depth and porosity of reservoir are the main factors of influencing shale gas accumulation. The relationship model between shale gas abundance and the factors is established using RBF neural network according to the similarity of the geological conditions of Sichuan Basin to the forming conditions of North American shale gas and a large number of shale gas data,and the model is trained with MATLAB. The error between the prediction results and the actual values of the shale resource abundance of Ohio and Longmaxi formation in Sichuan Basin is 27. 5% and 3. 5% separately,which shows that the prediction of shale gas production based on RBF neural network model can provide practical value and theoretical guidance for the shale gas development and exploration.

关 键 词：页岩气丰度预测 人工神经网络 RBF 四川盆地 

分 类 号：TE121[石油与天然气工程—油气勘探]