4.2 Difficulty in Understanding Professional Knowledge 

DeepSeek is confronted with the challenge of insufficient understanding of professional knowledge. Petroleum engineering involves a highly specialized multidisciplinary knowledge system, covering fields such as geomechanics, reservoir engineering, and drilling techniques. Its terminology system is complex and highly dependent on the specific field. Although the model can be trained using public data, a large amount of core data, such as oilfield exploration logs and real-time drilling parameters, is not available due to industry confidentiality or commercial sensitivity, resulting in a limited coverage of training data and making it difficult to support high-precision knowledge representation. Additionally, the dynamic evolution characteristics of petroleum engineering technology place higher demands on the model's continuous learning ability. If the model lacks a mechanism for synchronous updates with industry frontiers, it is prone to generating outdated content or distorted technical details. Moreover, the embedding of industry norms and safety standards is also a challenge. Petroleum operations must strictly follow international standards such as API and ISO, as well as regional regulations. However, the design of the compliance review mechanism in general models is insufficient, which may reduce the practicality and reliability of the output results. In such circumstances, a more appropriate approach might be to use professional knowledge for guidance or to use specialized models within specific domains to enhance the model's adaptability to petroleum engineering scenarios.

4.3 Lack of Research Innovation 

In the field of petroleum engineering, engineers often encounter various complex challenges, including geological exploration, reservoir development, well drilling and completion, and production. These areas involve the comprehensive application of knowledge from multiple disciplines such as geology, geophysics, fluid mechanics, rock mechanics, thermodynamics, and chemistry, as well as the accurate interpretation and effective utilization of data. Although LLM can handle large amounts of data and assist in integrating information and generating technical documents to a certain extent, it lacks in-depth understanding of domain-specific expertise and innovative thinking. In the intelligent application of petroleum engineering, DeepSeek excels in handling large-scale construction information and production data. However, its decision-making ability is limited by the algorithms and rules set by petroleum engineers. This limitation makes the decision-making logic of DeepSeek highly dependent on the preset algorithm framework and historical data paradigm, making it difficult for it to break through the existing knowledge boundaries when facing unstructured and complex problems. As a result, it is unable to generate new concepts or directly assist researchers in exploring new research directions in the field of petroleum engineering.

4.4 High Training Costs

The field of petroleum engineering involves a large amount of data, including geological exploration data, reservoir data, and production data. The acquisition, organization, and preparation of these data require a significant amount of time and resources. The performance and effectiveness of DeepSeek are affected by the quality and quantity of training data, therefore requiring a significant investment of resources to obtain high-quality training data. The multi-source heterogeneity of petroleum engineering data poses higher requirements for data cleaning, annotation, and fusion, requiring the participation of domain experts to ensure the effectiveness and applicability of the data. This significantly increases the cost of data preparation in the early stages. Building a specialized model that adapts to complex geological conditions and engineering scenarios requires multidimensional parameter tuning, including geological feature extraction, multimodal data fusion, and real-time optimization. Such processes require a significant amount of computational resources. The shortage of interdisciplinary talents is particularly prominent, requiring both professionals proficient in petroleum engineering and engineers with deep learning model development capabilities. The cost of forming such composite teams is relatively high. It can be seen that although LLM has great potential for application in the oil and gas industry, the high investment in data acquisition, preparation, model training, professional talent, hardware and software infrastructure needs to be carefully considered. Appropriate measures should be taken in the future to reduce costs and effectively utilize LLM in the field of petroleum engineering.

5. Development Suggestions and Prospects of DeepSeek Large Model Combined with Petroleum Engineering 

As an AI general intelligence, LLM is currently in its early stage of development. Although it is good at handling language, it lacks the innovative thinking and precise logic required for professional intelligence, and there are still doubts about whether it can play a positive role in professional fields. However, historical experience shows that with the advancement of technology, existing problems will be continuously solved, and an optimistic attitude should be held towards the emergence and development of new technologies, exploring their potential. This article proposes five suggestions for the future development of LLM, aiming to achieve its efficient and reliable application in the field of petroleum engineering.

5.1 DeepSeek Large Model for Petroleum Engineering

Petroleum engineering is a complex and diverse field that involves multiple aspects such as geological exploration, reservoir development, drilling engineering, and oil recovery engineering, relying on a deep understanding of physical mechanisms and effective utilization of data information. As the most representative LLM in China, DeepSeek has significant research value and development potential in the specialized application of petroleum engineering. Building a specialized LLM for the entire lifecycle of oil and gas exploration, drilling, and development has become an important research direction to address the issues of insufficient understanding of the mechanisms and limited ability to analyze professional terminology of general large models in petroleum engineering. The construction of this model requires breakthroughs in key technologies such as domain knowledge embedding, physical mechanism coupling, and multi-source heterogeneous data fusion. By integrating professional algorithm frameworks such as logging interpretation and reservoir simulation, intelligent decision support for geological modeling, engineering optimization, and other scenarios can be achieved. The research and development of petroleum specific LLM can promote the deep integration of artificial intelligence and petroleum engineering, and is expected to provide innovative solutions for key issues such as complex oil and gas reservoir development and unconventional resource evaluation, helping the industry's digital transformation and intelligent upgrading.

5.2 Database and Information Extraction in the Oil and Gas Field 

Extracting key information from various non-standard formats of documents in the field of petroleum engineering using DeepSeek is a significant and challenging task. In the future, a database containing a large number of articles, reports, and statements in the petroleum engineering field can be established. The text needs to be preprocessed, including cleaning, tokenization, and stemming, and then input into the model. Supervised learning methods can be used to fine-tune it to enable it to better understand and extract information from the articles in the petroleum engineering field. Further, clear task objectives and evaluation metrics need to be defined to utilize DeepSeek to automatically perform various tasks, such as information extraction, feature recognition, summary generation, algorithm programming, etc., providing convenient and high-quality auxiliary functions for professionals in the petroleum engineering field.

5.3 Networked Search and Real-time Update Function 

Given the limitations of DeepSeek in citing papers and providing the latest research progress, especially for papers published after the training time of the model and real-time information processing, it is necessary to consider updating the model data to ensure the accuracy of academic applications. To better meet the requirements of timeliness, DeepSeek can rely on its pre-trained optimization framework for the energy field to efficiently integrate data and materials in the oil field, and achieve dynamic iteration of model parameters through the incremental learning mechanism to adapt to the rapid evolution of oil engineering technology. Additionally, a domain knowledge graph-driven content linkage system can be constructed to automatically map real-time academic achievements and engineering cases to the professional terminology system, thereby enhancing the timeliness of technical analysis and decision-making recommendations. This function can provide dynamic knowledge support for complex scenarios (such as the optimization of unconventional oil and gas development plans), and has a significant promoting effect on improving the efficiency of intelligent research in the industry.

5.4 Image Processing and Video Generation Technology

Static images and dynamic videos play an important role in data acquisition, analysis, and decision-making. Static images are commonly used to capture static scenes in oil exploration, production, and equipment maintenance, such as core samples, geological profiles, and equipment structures. These images provide intuitive visual information, which is helpful for analysis and judgment in geological exploration and modeling, equipment detection, and maintenance. Dynamic videos can capture the dynamic processes and real-time operating status in petroleum engineering, such as drilling operations, oilfield production processes, equipment operation and maintenance, etc. They can not only provide more comprehensive information, but also show the changes and evolution of things, which is conducive to real-time monitoring, anomaly detection, and decision-making. By analyzing dynamic video data, production efficiency, equipment operation status, and safety risks can be more accurately evaluated, providing important references for the optimization and management of petroleum engineering.

DeepSeek can further integrate big data-driven capabilities with the physical principles involved in the field of petroleum engineering to construct a more physically consistent dynamic simulation framework, which can effectively avoid the limitations of generating images or videos that do not conform to reality. The dynamic simulation framework constructed by DeepSeek can generate high fidelity static images and dynamic videos based on text or structured data, especially when simulating complex geological evolution processes, real-time underground operations, and equipment mechanical behavior. It can effectively balance data-driven flexibility and physical law constraints, significantly improving the authenticity and interpretability of generated content.

Under specific conditions, big data-driven models can effectively capture and simulate certain complex dynamics in the real world, such as predicting weather, simulating wind tunnel experiments, etc. However, they are prone to problems in understanding and generalizing to complex environments, such as predicting water breakthrough patterns in low-permeability bottom water reservoirs. In the future, it is necessary to incorporate the basic principles involved in petroleum engineering, such as oil and gas flow mechanisms, solid mechanics constitutive equations, etc., into the model training process, so that it can better understand and simulate the complex dynamic processes in petroleum engineering.

5.5 Confidentiality Requirements and Data Security Issues 

The petroleum industry involves a large amount of sensitive data, such as geological exploration, production, and monitoring data. Data leakage can lead to significant economic losses and security threats. When using DeepSeek, sensitive data of the oilfield cannot be uploaded to the Internet; instead, they need to be trained and deployed locally. DeepSeek, with its independently developed distributed computing framework and lightweight model architecture, provides technical feasibility for the local deployment of oilfield data. By building a private knowledge enhancement system, the model can achieve closed-loop processing of exploration and development data, avoiding the leakage of sensitive information to the public network. Additionally, enterprises can lead the development of large-scale language models with independent intellectual property rights, similar to the intelligent cloud platform of China National Petroleum Corporation's "Exploration and Development Dream Cloud". During data transmission and storage, strict encryption measures and access control strategies must be adopted to ensure data security. During the model deployment and usage stages, system security should also be strengthened, and effective monitoring mechanisms should be established to promptly detect and address potential security vulnerabilities. Only by strengthening data management and protection, abiding by relevant laws and regulations, and establishing a sound security mechanism can the security and confidentiality of petroleum engineering data be effectively protected, ensuring the smooth operation of the industry.

6. Conclusion 

DeepSeek demonstrates great potential in the application of petroleum engineering, but there are still some challenges in the application process. In terms of data scale, the amount of data is increasing, confidentiality is getting higher, and data security is becoming more important. These requirements mean that the model must have stronger privacy protection and efficient data processing capabilities. In terms of data quality, current data sources are diverse, resulting in uneven data quality. For example, some data has severely missing information, is inaccurate, and has chaotic forms. These requirements mean that the model must have the ability to effectively handle multi-source heterogeneous data. In the future, the development of large models in the oil and gas industry must be guided by "technical adaptability" and "collaboration between industry, academia, and research institutions". In terms of technical adaptability, we should abandon the blind pursuit of algorithm complexity and focus on actual production pain points, such as cost control and process optimization. Based on the existing domestic L0 general large model, downstream task adaptation and model fine-tuning should be carried out, and the effectiveness of L2 domain large models and L3 scenario large models should be prioritized for research. Gradually build a lightweight and interpretable dedicated intelligent system. In the "collaboration between industry, academia, and research institutions" innovation aspect, the foundation research can be strengthened through cross-institutional sharing mechanisms of data, algorithms, computing power, and human resources. A teaching platform for cultivating interdisciplinary talents with expertise in oil and gas engineering and artificial intelligence should be constructed. Ultimately, relying on school-enterprise cooperation to promote the deep integration of theoretical innovation and industrial scenarios. This development framework can effectively promote the development of artificial intelligence in China's petroleum industry.