A Formation Inversion Algorithm Based on Collaborative Fuzzy Gradient Neural Dynamics for Natural Gamma Logging While Drilling

Abstract

A formation inversion algorithm with real-time performance and accuracy is crucial for natural gamma logging while drilling (LWD). However, traditional inversion algorithms are often limited by high computational resource consumption and insufficient accuracy. To address these issues, an improved forward method for natural gamma LWD is proposed. The inverse problem is subsequently modelled using the proposed forward method through which the search methodology and region of formation information are determined. On this basis, a collaborative fuzzy gradient neural dynamics (CFGND) algorithm is proposed, which combines the advantages of the collaborative mechanism in swarm intelligence algorithms and fuzzy gradient neural dynamics (FGND) to improve its accuracy and real-time performance. Specifically, the collaborative mechanism is applied to conduct a global search using all possible formation information. Concurrently, the FGND algorithm initiates a local search from each particle and dynamically and intelligently adjusts the learning rate of the neural dynamics through a fuzzy logic system during the process to achieve rapid and stable local convergence. The CFGND algorithm subsequently updates its globally optimal solution using the optimal solution obtained from the FGND algorithm. This iterative process continues until the termination condition is met. Theoretical analysis proves the existence of an optimal solution for the inverse problem and the convergence of the CFGND algorithm. The results of simulations and experiments demonstrate that the proposed formation inversion algorithm features high accuracy and sufficient real-time performance.

Keywords

collaborative mechanism / fuzzy logic system / logging while drilling (LWD) / neural dynamics

Acknowledgements

The authors would like to acknowledge the support of the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project (Grant No. 2025ZD1007305); the National Natural Science Foundation of China (62476115); the Fundamental Research Funds for Central Universities at Lanzhou University (lzujbky-2023-ct05, lzujbky-2023-stlt01); the Central Government's Guidance Funds for Local Science and Technology Development (24ZYQA045, YDZX20216200001297); the Ling Chuang Research Project of China National Nuclear Corporation (CNNC-LCKY-2024-080); the Special Funds from Gansu Nuclear Industry Research Institute; the National Key Research and Development Program of China (2023YFF1303501); the Lanzhou University Talent Cooperation Research Funds sponsored by Lanzhou City (561121203); the Supercomputing Center of Lanzhou University.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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