关键词: 人工智能, 智能钻采, 机器学习, 智能优化算法, 应用场景, 发展建议

Abstract: The current study mainly focus on the application of intelligent methods to improve the efficiency and reliability of technologies for optimizing parameters and identifying operational states in drilling and production processes, which exhibit great potential for future development. However, in the development of intelligent engineering technologies for oil and gas drilling and production, the smart optimization algorithms and predictive models still face the challenges including poor timeliness, weak robustness, and limited reliability. This hinder the practical application of artificial intelligence (AI)methods in oil and gas engineering. The paper provides an overview of the development status of AI methods and intelligent technologies for oil and gas drilling and production in China and abroad, involving engineering design and parameter optimization for well drilling and completion, evaluation of hydraulic fracturing performance and optimization of process parameters, diagnosis of artificial lift system failures, and prediction of reservoir properties and productivity. It further summarizes and analyzes the major challenges including a heavy reliance on labeled data for model training, poor model interpretability and weak performance in small-sample learning, inadquate validation of engineering applicability and reliability, poor timeliness of AI methods in performing optimization tasks, and limited flexibility in multi-objective optimization decision-making methods. Based on aforementioned challenges and the current research state of drilling and production technologies in China’s petroleum industry, this paper proposes several suggestions for the development of AI methods in oil and gas drilling and production as below:(1)establishing standardized, shared industry databases to support intelligent model comparison and validation; (2)enhancing research on learning paradigms to reduce the dependency on labelled data; (3)strengthening research on intelligent optimization methods to improve decision-making efficiency and timeliness; (4)focusing on studying physics-contrained data-driven models to improve the reliability of hybrid physics-data driven models; (5)advancing research on sample balancing and augmentation techniques to improve minority class recognition and model stability; (6)making efferts to develop multimodal data fusion and processing methods to boost the prediction accuracy and engineering robustness of intelligent models; (7)leveraging the advantages of general and industry-specific large models to enhance interpretability and accuracy of intelligent optimization decision-making for drill and production operations under multiple scenarios.

Key words: artificial intelligence, intelligent drilling and production, machine learning, intelligent optimization algorithms, application scenarios, development suggestions