Optimization method of pso drilling parameters based on neural network

Abstract

The invention belongs to the technical field of drilling engineering, and in particular relates to a neural network-based PSO drilling parameter optimization method, which is characterized in that it comprises the following steps: (1) inputting the current design parameters into the drilling parameter optimization system database; (2) inquiring whether there is The historical data of adjacent wells of the current well, the data is transmitted to the system data analysis and processing module, the preprocessed data is input into the neural network for model training, and the pre-drilling parameter prediction model is output; (3) after drilling, the current data parameters while drilling are obtained synchronously , calculate the current ROP, update the model, and compare the ROP under the same formation lithology and hole size in the model with the current ROP. The present invention collects original parameter signals and processes them through software to analyze the information data of the entire drilling process, and then optimizes the key data, and realizes real-time and dynamic intelligent optimization and system synthesis of parameters through the integration of multiple technologies in the whole process of drilling parameter optimization. analyze.

Description

technical field [0001] The invention belongs to the technical field of drilling engineering, in particular to a neural network-based PSO drilling parameter optimization method. Background technique [0002] Drilling engineering is a key link in the field of oil and gas development, and one of the links with the largest economic investment and the highest risk in the exploration and development process. The efficiency and cost of the drilling process directly reflect the drilling level, which is of great significance to subsequent oil and gas production and operations. Due to the comprehensive influence of geological conditions, mechanical properties of drilling rigs, drilling fluid properties, bit types and other factors, the drilling process presents great uncertainty. For a long time, how to improve drilling efficiency, shorten drilling cycle and save development cost under the influence of complex factors has become an engineering difficulty and hot spot in drilling work...

AI Technical Summary

Problems solved by technology

However, due to the complexity of the drilling process, the drilling parameters are not only affected by multiple factors, but also there is correlation coupling between each parameter, which presents great uncertainty, randomness and nonlinearity. Optimizing the calculation by establishing a conventional mathematical model not only The calculation process is extremely complicated, and the model often ignores some actual conditions, resulting in the model being inaccurate, practicable and limited

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