碳酸盐岩储层自适应模型常规测井渗透率预测

doi:10.13810/j.cnki.issn.1000-7210.2022.05.021

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摘要碳酸盐岩储层常规测井渗透率预测主要依靠物理模型和拟合模型，然而物理模型通常难以获取精准的物理参数，拟合模型难以直接预测非均质性较强的储层。为此，从优化物理模型、拟合模型参数出发，利用改进的粒子群优化算法(IPSO)同时优化物理模型参数及机器学习模型参数，进而提出渗透率预测的自适应模型。以川中北部高石梯—磨溪区块灯影组灯二段储层为研究对象，通过两个实验验证该自适应模型的渗透率预测能力。结果表明：①耦合物理模型和机器学习模型能提高模型的预测能力，有效地解决了应用过程中物理模型基础资料不足问题；②IPSO算法能更有效地优化物理模型参数和机器学习模型参数，IPSO-XGBoost预测性能更优，具有很强的推广性；③通过IPSO算法迭代确定离散岩石类型(DRT)聚类算法中的经验系数更具推广性。该自适应模型可为碳酸盐岩储层渗透率的准确预测提供新思路。

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关键词 ：碳酸盐岩IPSO-XGBoost, 自适应模型, DRT, 渗透率预测

Abstract：Permeability prediction of carbonate reservoirs by conventional logging data is mainly conducted through physical and fitting models. However, accurate physical parameters are difficult to obtain via traditional physical models, and it is also difficult to directly predict the permeability of carbona-te reservoirs with strong heterogeneity by fitting models. With optimized physical model and fitting model parameters, this paper employs the improved particle swarm optimization (IPSO) algorithm and optimizes the parameters of the physical model and machine learning model. Finally, an adaptive model for permeability prediction is proposed. Taking the second member reservoirs of Dengying Formation of Gaoshiti-Moxi block in north central Sichuan Basin as the research object, the paper validates the permeability prediction capability of the adaptive model by two experiments. The results are as follows. ① The prediction capability of the model can be improved by coupling physical model and machine learning model, which addresses the insufficient basic data of physical model during applications. ② The parameters of physical model and machine learning model can be more effectively optimized by the IPSO algorithm, and IPSO-XGBoost features better prediction performance with strong applicability. ③ IPSO is proved to be more suitable for determining the empirical coefficient in the discrete rock type (DRT) clustering algorithm. The adaptive model provides a new idea for the accurate prediction of carbonate reservoir permeability.

Key words： carbonate IPSO-XGBoost adaptive mo-del DRT permeability prediction

收稿日期: 2021-10-22

PACS:

P631

基金资助:本项研究受国家自然科学基金项目“致密储层裂缝系统诱发地震异常的机理及其与储层产能的关系”(41874143)资助

通讯作者: 徐鹏宇,四川省成都市成华区二仙桥东三路1号成都理工大学“油气藏地质及开发工程”国家重点实验室，610059。Email：317067647@qq.com E-mail: 317067647@qq.com

作者简介: 徐鹏宇,博士研究生，1993年生；2015年获兰州交通大学地质工程专业学士学位，2021年获成都理工大学能源学院地质工程专业硕士学位；现在成都理工大学地球物理学院攻读地球探测与信息技术工程专业博士学位，主要研究方向为地震与测井综合解释及储层预测

引用本文:

徐鹏宇, 周怀来, 官俊洁, 陈莉, 周捷, 宋雨莎. 碳酸盐岩储层自适应模型常规测井渗透率预测[J]. 石油地球物理勘探, 2022, 57(5): 1192-1203. XU Pengyu, ZHOU Huailai, GUAN Junjie, CHEN Li, ZHOU Jie, SONG Yusha. Permeability prediction of carbonate reservoirs by conventional logging with adaptive model. Oil Geophysical Prospecting, 2022, 57(5): 1192-1203.

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