The prospects of applying natural language processing technology in oil and gas exploration and development: A case study of ChatGPT
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CNOOC EnerTech-Drilling & Production Co., Ltd., Tianjing 300450, P. R. China

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Supported by Major Special Program of Cenertech(HFKJ-ZX-GJ-2023-02).

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    Abstract:

    With the rapid progress of artificial intelligence, the application of natural language processing(NLP) has expanded across various fields, such as finance, medical care, education, and e-commerce, offering efficient and intelligent solutions for diverse business arenas. This study primarily discusses the specific application scenarios, challenges, and potential future developments of ChatGPT in oil and gas exploration and development. Using Python to access the ChatGPT API, illustrative examples demonstrate its strengths in information retrieval, decision support, and customer service within this industry. These advantages translate into improved operational efficiency, optimized decision-making, enhanced customer service and communication, and innovative problem-solving methods. Additionally, ChatGPT’s strong programming capabilities further improve the efficiency of AI applications in this field. Fine-tuning ChatGPT with domain-specific knowledge and data enables the development of dedicated intelligent systems for oil and gas operations, where the quantity and quality of data determine the model's accuracy and expertise. Nevertheless, challenges remain, such as response reliability, data quality, model accuracy, and data security. Future trends are expected to include enhanced comprehension, improved creativity, and greater interactivity. Furthermore, ChatGPT can be integrated with data lakes to support data querying, fault prediction, automated report generation, training, and workflow automation. With continuous upgrades and user-driving optimization, NLP technologies such as ChatGPT are anticipated to play an increasingly critical role in the oil and gas sector.

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李桂仔,魏莉,尹彦君,冯高城,王凤刚,张震.自然语言处理技术在油气勘探开发的应用前景——以ChatGPT为例[J].重庆大学学报,2025,48(10):95~109

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  • Received:August 12,2023
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  • Online: October 20,2025
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