# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
[PERSON]  
3 Department of Earth Science and Engineering, Imperial College London, London SW7 2 AZ, UK;  
[EMAIL_ADDRESS]  
[PERSON]  
1 School of Energy Resources, China University of Geosciences, Beijing 10083, China;  
[EMAIL_ADDRESS] (L.G. ); [EMAIL_ADDRESS] (Z.K. ); [EMAIL_ADDRESS] (H.Y.) [PERSON]  
2 State Key Laboratory for Continental Dynamics, Department of Geology, Northwest University,  
Xi'an 710069, China; [EMAIL_ADDRESS] (S.D. ); [EMAIL_ADDRESS] (M.Z.) [PERSON]  
3 Department of Earth Science and Engineering, Imperial College London, London SW7 2 AZ, UK;  
[EMAIL_ADDRESS]  
[PERSON]  
1 School of Energy Resources, China University of Geosciences, Beijing 10083, China;  
[EMAIL_ADDRESS] (L.G. ); [EMAIL_ADDRESS] (Z.K. ); [EMAIL_ADDRESS] (H.Y.) [PERSON]  
2 State Key Laboratory for Continental Dynamics, Department of Geology, Northwest University,  
Xi'an 710069, China; [EMAIL_ADDRESS] (S.D. ); [EMAIL_ADDRESS] (M.Z.) [PERSON]  
3 Department of Earth Science and Engineering, Imperial College London, London SW7 2 AZ, UK;  
[EMAIL_ADDRESS]  
[PERSON]  
1 School of Energy Resources, China University of Geosciences, Beijing 10083, China;  
[EMAIL_ADDRESS] (L.G. ); [EMAIL_ADDRESS] (Z.K. ); [EMAIL_ADDRESS] (H.Y.) [PERSON]  
2 State Key Laboratory for Continental Dynamics, Department of Geology, Northwest University,  
Xi'an 710069, China; [EMAIL_ADDRESS] (S.D. ); [EMAIL_ADDRESS] (M.Z.) [PERSON]  
3 Department of Earth Science and Engineering, Imperial College London, London SW7 2 AZ, UK;  
[EMAIL_ADDRESS]  
[PERSON]  
1 School of Energy Resources, China University of Geosciences, Beijing 10083, China;  
[EMAIL_ADDRESS] (L.G. ); [EMAIL_ADDRESS] (Z.K. ); [EMAIL_ADDRESS] (H.Y.) [PERSON]  
2 State Key Laboratory for Continental Dynamics, Department of Geology, Northwest University,  
Xi'an 710069, China; [EMAIL_ADDRESS] (S.D. ); [EMAIL_ADDRESS] (M.Z.) [PERSON]  
3 Department of Earth Science and Engineering, Imperial College London, London SW7 2 AZ, UK;  
[EMAIL_ADDRESS]  
[PERSON]  
1 School of Energy Resources, China University of Geosciences, Beijing 10083, China;  
[EMAIL_ADDRESS] (L.G. ); [EMAIL_ADDRESS] (Z.K. ); [EMAIL_ADDRESS] (H.Y.) [PERSON]  
2 State Key Laboratory for Continental Dynamics, Department of Geology, Northwest University,  
Xi'an 710069, China; [EMAIL_ADDRESS] (S.D. ); [EMAIL_ADDRESS] (M.Z.) [PERSON]  
3 Department of Earth Science and Engineering, Imperial College London, London SW7 2 AZ, UK;  
[EMAIL_ADDRESS]  
[PERSON]  
1 School of Energy Resources, China University of Geosciences, Beijing 10083, China;  
[EMAIL_ADDRESS] (L.G. ); [EMAIL_ADDRESS] (Z.K. ); [EMAIL_ADDRESS] (H.Y.) [PERSON]  
2 State Key Laboratory for Continental Dynamics, Department of Geology, Northwest University,  
Xi'an 710069, China; shwding@126.  
is sometimes considered beneficial during certain stages, such as the early phases of water flooding, as they can accelerate fluid flow. On the other hand, during the water flooding process, if these high-permeability channels do not effectively displace oil, they can form ineffective water circulation channels. These ineffective water circulation channels significantly reduce oil displacement efficiency, inhibit oil and gas recovery, and lead to resource wastage [3]. In recent years, with most reservoirs entering the high water cut stage, developing ineffective circulation channels between wells has become one of the main factors restricting the efficient development of oil fields [4]. By identifying and analyzing the ineffective circulation channels, the problems of low water injection efficiency, high water cuts in producers, and low recovery could be potentially addressed [5; 6]. Currently, the methods used to identify ineffective circulation channels include well logging curve analysis, well testing analysis, geochemical methods, tracer testing, fuzzy comprehensive evaluation, machine learning algorithms, and capacitance-resistance model (CRM) analysis [1; 7; 8].