Analytical Determination of Interwell Connectivity Based on Interwell Influence  

作　　者：Jiangru Yuan Xingjie Zeng Haiyun Wu Weishan Zhang Jiehan Zhou Bingyang Chen 

机构地区：[1]Research Institute of Petroleum Exploration and Development,Beijing 100083,China [2]College of Computer Science and Technology,China University of Petroleum,Qingdao 266580,China [3]Department of Computer and Electronic Engineering,University of Oulu,Oulu FI-90014,Finland

出　　处：《Tsinghua Science and Technology》2021年第6期813-820,共8页清华大学学报（自然科学版（英文版）

基　　金：supported by the Ministry of Industry and Information Technology’s 2018 Big Data Industry Development Pilot Demonstration Project “Demonstration Project of Oil and Gas Exploration and Development Innovation and Efficiency Enhancement Based on the Application of Big Data” (Letter of the Ministry of Industry and Information Technology [2018] No.339);the Ministry of Industry and Information Technology Demonstration Project Supporting Project “Petroleum Exploration and Development Big Data and Artificial Intelligence Key Technology” (No.2018D-5010-16);the Innovation Project of PetroChina Science and Technology Research Institute Co.,Ltd.“Exploration and Research on Predicting the Remaining Oil Saturation of Each Layer under the Condition of Co-Injection by Applying Big Data Deep Learning Method” (No.2017ycq02);the National Key R&D Program (No.2018YFE0116700);the Shandong Provincial Natural Science Foundation (No.ZR2019MF049,Parallel DataDriven Fault Prediction under Online-Offline Combined Cloud Computing Environment)。

摘　　要：Interwell connectivity, an important element in reservoir characterization, especially for water flooding,is used to make decisions for better oil production. The existing methods in literature directly use related data of wells to infer interwell connectivity, but they ignore the influence between different wells. The connection of one well to more than two wells(as is often true in the oil field well pattern) will impact the accuracy of the connectivity analysis. To address this challenge, this paper proposes the Particle Swarm Optimization-based CatBoost for Interwell Connectivity(PSOC4IC) based on relative features to analyze interwell connectivity with the combination of joint mutual information maximization-based denoising sparse autoencoder for inter-feature construction and extraction and PSO-based CatBoost(PSO-CatBoost) for connectivity prediction with high-dimensional noise data.The experimental results show that the PSOC4IC improves analysis accuracy.

关 键 词：interwel connectivity interwel influence Particle Swarm Optimization(PSO) CatBoost 

分 类 号：TE319[石油与天然气工程—油气田开发工程]