# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 3 Laboratory Tests and Numerical Approach Method
### Artificial Intelligence Methods  
The term \"intelligence\" in artificial intelligence was first used in the field of psychology [120]. In the whole process of exploration and development in the field of petroleum engineering, there are many complex and uncertain factors, and much of the information that can be collected and obtained is non-quantitatively expressed, imprecise, imperfect, ambiguous [121], or even polysemous. Artificial intelligence methods are mostly data-driven, and they are not directly used in the field of petroleum engineering. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Parameters** & **Number of Samples** & \
\begin{tabular}{c} **Mean Relative** \\ **Error/\%** \\ \end{tabular} &
\begin{tabular}{c} **Correlation** \\ **Coefficient R** \\ \end{tabular} & **Accuracy Rate/\%** \\ \hline Microparticle migration [115] & 96 & 9.29 & 0.992 & 80.6 \\ Solid phase intrusion [115; 116] & 98 & 12.01 & 0.995 & 92.1 \\ Hydration swelling [115; 117] & 72 & 7.84 & 0.907 & 78.9 \\ Inorganic scale [115; 118] & 96 & 9.17 & 0.907 & 85.7 \\ Organic scale [115] & 96 & 9.56 & 0.999 & 89.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Diagnostic results for each type of damage level. Figure 6: Numerical simulation method to simulate the variation of peri-well causeless permeability with time [115]. Figure 7: Numerical simulation method to simulate the percentage of each damage factor with time [115]. driven [122], without a clear form of mathematical equation, and are very suitable for solving such high-dimensional nonlinear problems. There are numerous successful examples of artificial intelligence methods in the petroleum industry for formation modeling and characterization, drilling and production optimization [123; 124; 125], and production enhancement alternative optimization [126; 127; 128; 129], including smart wells [130; 131; 132], smart formation characterization [133], and online analysis and visualization of logging data [134; 135]. Neural networks, genetic algorithms, and expert systems are currently used in the field of formation damage diagnosis and prediction. Expert systems are artificial intelligence programs that achieve expert-level capabilities in problem solving by representing and processing the knowledge used by human experts. As early as 1988, [PERSON] and [PERSON] [136] conducted an analytical study on the feasibility of intelligent expert systems for formation protection, providing a basis for subsequent developments. Many applications of expert systems have been reported in the literature in various petroleum engineering disciplines, including enhanced oil recovery (EOR) process selection and design, exploration, and drilling. [PERSON] et al. [137] developed the \"Smart-Drill\", a fuzzy expert system for diagnosing and solving leakage problems. [PERSON] et al. [138] and [PERSON] et al. [139] combined fuzzy rules with mathematical models to develop a structured expert system for damage diagnosis and protection countermeasure selection. [PERSON] and [PERSON] [140] proposed an integrated fuzzy expert system to diagnose formation damage and assign appropriate treatment measures. In a recent study, [PERSON] et al. [141; 142] established an oil formation sensitivity prediction method based on a fuzzy expert system. This solved the blindness of pure empirical judgment and greatly improved the prediction accuracy by establishing a fuzzy expert knowledge base and fuzzy rule matching. Figure 5 shows the comparison between the measured and predicted data, which shows that the data points representing the measured and predicted data are very close to the line y = x, which indicates that the error between the measured and predicted data is only marginal. The average prediction accuracy of the model achieves over 90%. Figure 8 shows the results of the fuzzy expert system predicting the sensitivity of oil formations.