Abstract
In this study, a model based on the improved grey wolf optimizer (GWO) for optimizing RVFL is proposed to enable the problem of poor accuracy of Oil layer prediction due to the randomness of the parameters present in the random vector function link (RVFL) model to be addressed. Firstly, GWO is improved based on the advantages of chaos theory and the marine predator algorithm (MPA) to overcome the problem of low convergence accuracy in the optimization process of the GWO optimization algorithm. The improved GWO algorithm was then used to optimize the input weights and implicit layer biases of the RVFL network model so that the problem of inaccurate and unstable classification of RVFL due to the randomness of the parameters was avoided. MPA-GWO was used for comparison with algorithms of the same type under a function of 15 standard tests. From the results, it was concluded that it outperformed the algorithms of its type in terms of search accuracy and search speed. At the same time, the MPA-GWO-RVFL model was applied to the field of Oil layer prediction. From the comparison tests, it is concluded that the prediction accuracy of the MPA-GWO-RVFL model is on average 2.9%, 3.04%, 2.27%, 8.74%, 1.47% and 10.41% better than that of the MPA-RVFL, GWO-RVFL, PSO-RVFL, WOA-RVFL, GWFOA-RVFL and RVFL algorithms, respectively, and its practical applications are significant.
Highlights
Petroleum exploration is a complex system that involves a wide range of activities and high-risk values
Chen et al [36] proposed a support vector machine (MPA-SVM) technique based on the marine predator algorithm (MPA) for rolling bearing fault diagnosis, and the results demonstrated the effectiveness of the fault diagnosis method, which was able to diagnose bearing faults with 100% accuracy
EInxptheirsimsuebnsteacltiCono,mcopmarpiasorantiNveunmuemreicraiclaOl oppttiimmiizzaattiioonn eExxppereirmimenetnstasre performed for MIPnA-tGhWisOs,tWudOyA, fMivPeA,oGthWeOr,oPpSOtimanidzaGtWioFnOaAl.gTohreitchonmvsergaernecesecut ruvpes faorerschoomwnparison mineFnigtusrewAit1h. the proposed MPA-grey wolf optimizer (GWO)-random vector function link (RVFL) algorithm, including MPA-GWO, MPA, GWO, particle swarm algorithms (PSO) and GWFOA [46]
Summary
Petroleum exploration is a complex system that involves a wide range of activities and high-risk values. Reservoir categories are determined based on upper and lower criteria for the electrical properties of these reservoir parameters and other logging indicators. This method has some advantages, an accurate mathematical model needs to be developed. Such a structured approach to semistructured and unstructured problems makes it difficult to achieve satisfactory results. From a mathematical point of view, it follows that the logging interpretation problem refers to the solution of a mapping problem. Roman et al [2] analyzed and assessed the mininggeological and mining-technical conditions of the open pit “Severny” using mathematical models. The technical and economic indexes of open-pit mining were effectively improved
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