摘要
北美页岩气革命取得的巨大成功对全球能源格局产生了深远的影响,并引起了工业及学术界的广泛关注。甜点预测是提高页岩气钻井成功率、保障压后产能的一项关键技术。目前传统方法预测页岩气甜点区存在误差较大的问题。以加拿大商业开发成功的页岩气产区Fox Creek地区为例,对控制该区页岩气甜点的地质及工程因素进行详细剖析,在此基础上,提出一种改进残差的神经网络方法来分析页岩气甜点主控因素,并建立甜点区预测模型。结果表明,影响研究区页岩气甜点区的主控因素为孔隙度、渗透率、泥质含量、埋深、地层压力、脆性指数和压裂施工参数(水平段长度、压裂段数、支撑剂注入质量和压裂液注入体积)。改进残差神经网络算法在测试及训练的产气量数据预测方面吻合度分别达到0.94和0.85,展现出很好的预测效果。基于改进残差神经网络的预测模型表明,西部和南部Duvernay边界处发育页岩甜点区,向东北部逐渐变差。该页岩气甜点预测模型为该地区页岩气的后续高效开发提供了可靠的基础。
The great success of the North American shale gas revolution has posed a profound impact on the global energy market,attracting great interest from both industry and academia.Accurate prediction of sweet spots is essential to determine the well location and enable a high after-stimulation productivity for the fractured wells.However,there exist in the traditional approach to the shale gas sweet spot prediction.In this study,Fox Creek,a commercially developed shale gas producing area in Canada,is taken as an example to investigate the geological and engineering factors which can be utilized to identify the sweet spot area of shale gas reservoirs.A modified residual neural network approach is proposed to determine the main controlling factors and establish a prediction model for the sweet spot area.Results show that the main controlling factors affecting the sweet spot area of shale gas formations are porosity,permeability,shale content,burial depth,formation pore pressure,shale brittleness index and fracturing parameters(horizontal length,number of fracturing stages,total placed proppant and total fluid injection).The gas production reached 0.94 and 0.85 in the modified residual neural network algorithm in the test and training respectively.The distribution of shale sweet spots has been contoured based on the prediction model of the modified residual neural network.It is shown that the shale sweet spots locate along the Duvernay boundary in the west and south and deteriorated toward the northeast.This prediction model of shale gas sweet spot area provides a reliable foundation for the subsequent efficient development of shale gas.
作者
惠钢
陈胜男
王海
顾斐
HUI Gang;CHEN Shengnan;WANG Hai;GU Fei(University of Calgary,Calgary,Alberta T2N1N4,Canada;PetroChina Research Institute of Petroleum Exploration&Development,Haidian,Beijing 100083,China)
出处
《西南石油大学学报（自然科学版）》
CAS
CSCD
北大核心
2021年第5期19-32,共14页
Journal of Southwest Petroleum University(Science & Technology Edition)
基金
加拿大卓越研究基金(CFREF2020)
联盟基金(ALLRP548576–2019)
加拿大发现基金(RGPIN–2020–05215)。
关键词
页岩气
甜点区
控制因素
改进残差神经网络
加拿大西部盆地
shale gas
sweet spot
controlling factors
improved residual neural networks
Western Canada Basin
