唐军,彭超
TANG Jun,PENG Chao

• 1:长江大学地球物理与石油资源学院
• 2:中国石化江汉石油工程有限公司测录井公司

摘要(Abstract)：

涪陵页岩气储层受地层各向异性影响,水平井初始产能预测困难。根据测井响应特征及岩心观察,将龙马溪组—五峰组气层段细分为9个岩性、电性小层,其中下部的5小层为优质气层段;依据总有机碳含量等储层评价参数,将该地区储层划分为4个等级;依次分析了页岩气厚度、水平段试气长度、穿行轨迹以及水平段构造位置等对水平井产能的影响;随后建立了基于水平井穿行轨迹和BP神经网络预测的初始产能预测方法。实际应用结果表明:虽然2种预测方法均可实现涪陵地区页岩气单井的初始产能预测,但神经网络预测方法比基于水平井穿行轨迹的预测方法精度更高;对于构造复杂地层,深化研究储层压裂改造工艺可提高产能预测精度。
Due to the influence of formation anisotropy on Fuling shale gas reservoirs, it is difficult to predict the initial productivity of the horizontal well. According to the logging response characteristics and coring observation, Longmaxi Formation-Wufeng Formation gas reservoirs were subdivided into 9 lithologic and electrical sublayers,and the in lower part 5 sublayers are high-quality gas intervals.According to the reservoir evaluating parameters such as total organic carbon content, the reservoirs in this block were divided into four grades,the influences of the shale gas thickness, horizontal-interval tested gas length, well trajectory and structural location of the horizontal section on the horizontal well productivity were analyzed successively,and then the initial productivity predicting methods were established based on the horizontal well trajectory and BP neural network prediction. The actual practice results show that the above two predicting methods can both achieve the initial productivity prediction of the individual shale gas well in Fuling area,but the predicted precision of the neural network method is higher than that of the other,and the prediction accuracy of productivity in complex structure formation can be improved by deep study of resrvior fracturing technogy.

关键词(KeyWords)： 页岩气;水平井;水平段长度;产能预测;穿行轨迹;神经网络
shale gas;horizontal well;horizontal section length;productivity prediction;well trajectory;neural network

Abstract:

Keywords:

基金项目(Foundation): 湖北省教育厅科学技术研究项目“含气裂缝性岩石声波传播实验研究”(D20181301)

作者(Author): 唐军,彭超
TANG Jun,PENG Chao

DOI: 10.19597/j.issn.1000-3754.201907059

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