Tight carbonate reservoirs appear to be heterogeneous due to the patchy production of various digenetic properties. Consequently, the permeability calculation of tight rocks is costly, and only a finite number of core plugs in any single reservoir can be estimated. Hence, in the present study, a novel hybrid model constructed by combination of the improved version of the Harris Hawks optimisation (HHO), i.e., IHHO, and extreme learning machine (ELM) is proposed to predict the permeability of tight carbonates using limited number of input variables. The proposed IHHO employs a mutation mechanism to avoid trapping in local optima by increasing the search capabilities. Subsequently, ELM-IHHO, a novel metaheuristic ELM-based algorithm, was developed to predict the permeability of tight carbonates. Experimental results show that the proposed ELM-IHHO attained the most accurate prediction with R² = 0.9254 and RMSE = 0.0619 in the testing phase. The result of the proposed model is significantly better than those obtained from other ELM-based hybrid models developed with particle swarm optimisation, genetic algorithm, and slime mould algorithm. The results also illustrate that the proposed ELM-IHHO model outperforms the other benchmark model, such as back-propagation neural nets, support vector regression, random forest, and group method of data handling in predicting the permeability of tight carbonates.

致密碳酸盐岩储层似乎是非均质的，因为各种成岩特性的生产不一。因此，致密岩石的渗透率计算成本高昂，并且在任何单个储层中只能估计有限数量的岩心塞。因此，在本研究中，提出了一种由改进版本的 Harris Hawks 优化（HHO），即 IHHO 和极限学习机（ELM）相结合构建的新型混合模型，以使用有限数量来预测致密碳酸盐岩的渗透率输入变量。提议的 IHHO 采用了一种变异机制，通过增加搜索能力来避免陷入局部最优。随后，开发了一种新的基于 ELM 的元启发式算法 ELM-IHHO 来预测致密碳酸盐岩的渗透率。 在测试阶段，R ² = 0.9254 和 RMSE = 0.0619。所提出模型的结果明显优于从其他基于 ELM 的混合模型中获得的结果，这些模型采用粒子群优化、遗传算法和粘液模型算法开发。结果还表明，所提出的 ELM-IHHO 模型在预测致密碳酸盐岩渗透率方面优于其他基准模型，例如反向传播神经网络、支持向量回归、随机森林和数据处理分组方法。