# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 5 Conclusions  
Formation damage is one of the most challenging problems in the process of oil and gas exploration and development. Moreover, formation damage prediction and diagnosis is a prerequisite for formation damage prevention and treatment and mitigation. This paper systematically reviews and summarizes the principles, advantages, and disadvantages of current formation damage prediction and diagnosis methods from two aspects: wellsite diagnosis and laboratory-based diagnosis and prediction.  
While substantial progress has been made in imaging techniques, numerical simulations, and artificial intelligence applications, significant gaps persist in understanding and predicting multifactorial damage mechanisms and their spatiotemporal evolution. High-resolution imaging methods, like NMR and X-ray scanning, provide detailed insightsinto pore-scale damage mechanisms, yet their integration into field-scale diagnostics and real-time applications remains underexplored. Advanced numerical models offer critical insights into the dynamics of formation damage, though they often face challenges related to computational complexity and the availability of comprehensive field data for validation. Emerging AI techniques, such as expert systems and neural networks, hold significant promise for real-time diagnostics and predictive modeling, but their success depends on robust datasets and algorithms capable of handling the complexities of multifactorial formation damage. Moving forward, it is imperative to develop multi-damage factor coupling models that integrate chemical, mechanical, and biological damage mechanisms, as well as predictive systems that dynamically model the progression of formation damage over time and space. These systems should leverage real-time field data and incorporate probabilistic approaches to address uncertainties. Furthermore, integrating laboratory experiments, imaging techniques, numerical simulations, and AI into unified diagnostic platforms can significantly enhance diagnostics and mitigation strategies. For example, combining CT imaging data with machine learning models could automate damage classification and guide operational decisions. Field trials in diverse geological settings are essential to validate and refine these innovations, necessitating collaboration between academic researchers and industry stakeholders to address practical challenges, including cost constraints and the difficulty of obtaining representative samples. By addressing these future directions, the oil and gas industry can advance its capability to effectively predict, diagnose, and mitigate formation damage, optimizing hydrocarbon recovery, reducing operational costs, and enhancing the sustainability of energy production.  
Idea, Z.S.; Data Collection, Z.S.; Writing--Original Draft, Z.S.; Financial Support, Z.C.; Reviewing, Z.C.; Editing, Z.C.; Supervisor, Z.C.; Idea Conceiving, Z.C.; Discussion, Z.S. and Z.C.; Data Research, Z.S. and Z.C.; Finalizing, Z.S. and Z.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.