# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
In recent years, in-depth research on CRM includes one study that explored the advantages and disadvantages of applying data-driven models of CRM in different simulation scenarios [29]; one research proposed a new method combining CRM with reservoir simulation to improve reservoir characterization and identify new development opportunities [30]; another study improved CRM model accuracy by considering variable time constant parameters during production [31]. Previous research highlighted the potential of CRM in quickly evaluating waterflood performance and discussed its application in single-well and multi-well unsaturated reservoirs [32]; [PERSON] et al. also proposed the capacitance resistance aquifer-fractional flow model, a new approach for conventional unsaturated reservoirs in primary production [33]. Despite the progress made in the above studies, some challenges and shortcomings still remain, as summarized below:  
(1) Constrained by static parameters: Although capacitance models use historical data to assess the dynamic relationships between wells, the inter-well connectivity coefficient (\(\lambda\)) and time-lag coefficient (\(\tau\)) in the models are usually treated as fixed values. This static approach limits the models' ability to accurately capture the complex and dynamic nature of oilfield evolution, particularly when faced with continuous changes driven by field operations. Consequently, these models lack the capability to leverage advanced algorithms, such as artificial intelligence, to dynamically monitor and identify the development and extent of ineffective circulation channels, which is crucial for optimizing reservoir management. (2) Lack of comprehensive sensitivity analysis: Different types of reservoirs have diverse geological characteristics, rock properties, and fluid properties, which introduce significant complexity in evaluating inter-well connectivity. This inherent diversity poses challenges for accurate assessments. A critical limitation of almost all existing methods is the lack of a comprehensive and unified sensitivity analysis. Specifically, there is insufficient consideration of various factors that influence inter-well connectivity. This gap in the literature necessitates a more thorough analysis. (3) Limitations in analyzing multi-well group performance: Current studies often do not fully address the production performance of multi-well groups, specifically the formation and development of ineffective circulation channels. This oversight leads to an incomplete understanding of how these channels affect overall reservoir performance and limits the ability to optimize water injection strategies. Furthermore, the lack of in-depth analysis of the dynamics of these ineffective channels hampers efforts to enhance oil recovery effectively. (4) Limitations of evaluation standards and field application for ineffective circulation channels: Previous research on evaluating the development of ineffective circulation channels lacks systematic and clear standards, which limits the reliability and consistency of the evaluation results. Additionally, many studies primarily rely on theoretical analysis and simulation results, lacking validation and support from actual field data, which may affect the effectiveness of the evaluation indicators in practical applications. To address the knowledge gaps, this study proposes a state variable-based dynamic capacitance (SV-DC) model that integrates dynamic reservoir data with geological characteristics for more accurate identification of inter-well connectivity and its evolution. The model leverages injection-production data and utilizes an intelligent algorithm to solve the model efficiently, making the process simple and time-saving. A comprehensive sensitivity analysis was performed on single-well pairs and multi-well groups regarding the permeability amplitude, the width of the high permeable channel, change, and lasting period of injection pressure.