Neural network-based prediction of drilling fluid leakage

Abstract

During the drilling process, reservoir fractures may lead to drilling-fluid loss, thereby slowing drilling progress and reducing well productivity. Therefore, it is necessary to choose the appropriate materials and formulations for plugging, and the leakage amount and rate are the most important indicators for selecting plugging agents. In this study, the amount of rigid mineral particles and plant fibers commonly used in drilling, as well as the width of formation fractures, were used as input variables, while leakage volume served as the output variable. By combining the multiple-population genetic algorithms (MPGA) and the backpropagation neural network (BPNN), an MPGA-BPNN prediction model was established to predict the leakage amount under different plugging formulations. The results showed that the correlation coefficient of the established prediction model reached 0.9741, indicating strong predictive accuracy for leakage volume and plugging performance under varying formulation conditions, providing useful reference and guidance for the optimization of plugging agents.

Keywords

Drilling fluid / Lost circulation control and plugging / Leakage amount / Neural networks / Multi-population genetic algorithm

Funding

This research was supported by the Fundamental Research Funds for the Central Universities (grant number: 24CX02013A) and the Science and Technology Project of China National Petroleum Corporation Offshore Engineering Company Limited (grant number: 202303-0101).

Conflict of interest

Wenbing Wu, Tao Liu, Lianlu Huang, and Hua Li were employed by the Drilling Company, China National Petroleum Corporation (CNPC) Offshore Engineering Company Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The Drilling Company, CNPC Offshore Engineering Company Limited, had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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