# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 3 Laboratory Tests and Numerical Approach Method
### Artificial Intelligence Methods
Neural network, short for Artificial Neural Networks (ANNs), is an artificial intelligence method that describes the intelligent behavior of the human brain from a microscopic perspective [143] and has good intelligence in solving fuzzy relations and nonlinear problem methods. In 1995, [PERSON] and [PERSON] [144] were the first to study formation damage identification using the BAM (Bidirectional Associative Memories) neural network approach. The BAM neural network has a clear structure, intuitive operation, and fast learning speed [145]. However, its inference and algorithm are too simple, and only seven damage types were considered, with narrow coverage. Therefore, after the initial attempts at formation damage diagnosis, few people have applied BAM neural networks to diagnose formation damage. In 2004, [PERSON] et al. [146; 147] used a BP (Back Propagation) neural network to predict formation sensitivity damage. The BP neural network was trained according to the error backpropagation algorithm [148], and the model showed good prediction accuracy. However, it was restricted by the hardware conditions at that time, which limited overall efficiency. Later, with the improvement of computer hardware levels, BP neural networks gradually became the mainstream prediction method for formation damage [149]. In 2010, [PERSON] et al. [150] used an RBF (Radial Basis Function) neural network for quantitative diagnosis of formation damage type and extent, which changed the time required for training to one-thousandth of that of BP neural networks, while maintaining a high accuracy rate. In the same year, [PERSON] et al. [151] applied formation rock physics, interface chemistry theory, and artificial neural networks to low-porosity formations. They correlated these with numerous experimental results, predicted five formation sensitivities,and established a water-lock damage model. The most recent research result was in 2016, when [PERSON] et al. [152] used quantum neural networks to predict water-lock damage in low-permeability formations. Quantum neural networks (QNNs) are computational neural network models based on the principles of quantum mechanics [153; 154], which overcome the drawbacks of the gray correlation analysis method that requires matrix analysis and is complicated to operate. The genetic algorithm (GA) is an evolutionary optimization algorithm developed on the basis of [PERSON]'s hypothesis. According to this hypothesis, each generation of viable solutions is generated by a different genetic operator, including mutations and crossovers with the previous set of solutions. Then, it replaces the previous generation with the new population. After each generation, a predefined function, which becomes the fitness function, is evaluated and the viable solutions are ranked according to the evaluation results. The repeating process will stop only when termination condition is met. The advantage of genetic algorithms is the absence of derivatives in the optimization process, which can be applied to a wide range of nonlinear problems without getting into local extremes [155]. In 2001, [PERSON] and [PERSON] [156] applied genetic algorithms and material balance theory to simulate formation damage due to precipitation, adsorption, dissolution, and clay swelling. For the acidizing model, GAs optimized the Damkohler and acid capacity numbers to match permeability predictions with experimental data. In the formation damage model, GAs calibrated rate constants for deposition, dissolution, and entrainment processes, aligning predictions of porosity and permeability changes with observations. By iteratively minimizing discrepancies, GAs improved the accuracy of these  
Figure 8: Results of the fuzzy expert system proposed by [PERSON] et al. [141; 142] to predict the sensitivity of oil formations. models, showcasing their utility in optimizing complex reservoir simulations. The GA method is simple and practical, but its considerations are too simple to evaluate formation damage in oil and gas field development in a systematic way.