Progress on artificial intelligence methods in oil and gas drilling and production

Sun Baojiang; Zhou Ziqiang; Sun Qian

doi:10.7623/syxb202511011

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Progress on artificial intelligence methods in oil and gas drilling and production

更新时间：2025-12-10

- Progress on artificial intelligence methods in oil and gas drilling and production
- ACTA PETROLEI SINICA Vol. 46, Issue 11, Pages: 2141-2173(2025)
- 作者机构：
  
  1. 中国石油大学(华东)石油工程学院,山东,青岛,266580
  2. 中国地质大学(北京)能源学院,北京,100083
- 作者简介：
- 基金信息：
- DOI：10.7623/syxb202511011
  CLC： E319
- Published Online：04 December 2025，
  
  Published：04 December 2025
- 稿件说明：
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孙宝江, 周梓强, 孙骞. 油气钻采工程中的人工智能方法研究进展[J]. 石油学报, 2025, 46(11): 2141-2173.

Sun Baojiang, Zhou Ziqiang, Sun Qian. Progress on artificial intelligence methods in oil and gas drilling and production[J]. ACTA PETROLEI SINICA, 2025, 46(11): 2141-2173.
孙宝江, 周梓强, 孙骞. 油气钻采工程中的人工智能方法研究进展[J]. 石油学报, 2025, 46(11): 2141-2173. DOI： 10.7623/syxb202511011.

Sun Baojiang, Zhou Ziqiang, Sun Qian. Progress on artificial intelligence methods in oil and gas drilling and production[J]. ACTA PETROLEI SINICA, 2025, 46(11): 2141-2173. DOI： 10.7623/syxb202511011.

摘要

引入智能方法解决钻采过程中参数优化、状态识别等技术中的效率和可靠性等问题是目前的研究热点

并展现了广阔的发展前景。但在油气钻采智能化工程技术的研发过程中

智能优化算法和预测模型尚存在时效性和鲁棒性差、可靠性不足等问题

导致人工智能方法在油气钻采工程中的应用尚不理想。对国内外油气钻采领域内钻完井工程设计与施工参数优化、水力压裂改造效果评价与工艺参数优化、人工举升井故障诊断、油气藏物性和产能预测等方面的人工智能方法及智能化技术的发展现状进行了概述

并对人工智能模型的训练过程对标签数据依赖严重、可解释性不足以及小样本学习能力欠佳、工程适用性与可靠性验证不足

以及智能方法解决优化问题时效性差、多目标优化决策方法灵活性不足等主要问题进行了归纳分析。结合中国石油工业钻采技术研究现状及上述问题提出了油气钻采工程中人工智能方法发展建议:①建立标准化行业共享数据库体系

解决智能模型对比与验证难题;②加强学习范式研究

缓解标签数据依赖问题;③加强智能优化方法研究

提高决策精准性与时效性;④强化物理约束的数据驱动模型研究

增强物理—数据双驱动模型的可靠性;⑤推进样本均衡与增强技术研究

提升少数类识别准确性与模型稳定性;⑥加强多模态数据融合与处理方法研究

提升智能模型预测精度与工程鲁棒性;⑦发挥通用及行业大模型优势

提高油气钻采多场景智能优化决策可解释性与准确性。

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.

关键词

Keywords

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