With the accelerated development of large language models and intelligent agent technologies, digital transformation in the oil and gas industry is evolving from traditional information retrieval and auxiliary analysis toward intelligent collaboration across job roles, scenarios, and entire business chains. For oil companies, truly valuable intelligent agents should not remain at the level of natural language-based Q&A tools. Instead, they should function as “digital job roles” that are capable of understanding professional business semantics, integrating with real operational data and enterprise systems, executing workflow-based tasks, and delivering stable, controllable outputs within defined boundaries.
Exploration and production (E&P) operations are characterized by long professional chains, high knowledge density, complex data sources, and strong requirements for cross-functional collaboration. These characteristics determine that oil companies require a portfolio of specialized intelligent agents designed for high-frequency, repetitive, and decision-intensive scenarios, rather than a single “universal assistant.” Based on this understanding, oil company intelligent agents can be classified into five maturity levels: L1 question-answering agents, L2 assistant agents, L3 scenario-execution agents, L4 role-based agents, and L5 operational collaboration agents.
Each level corresponds to different types of problems and business value. L1 focuses on knowledge and data retrieval; L2 supports content generation and preliminary deliverables; L3 enables closed-loop execution within individual scenarios; L4 achieves continuous role-level task delegation; and L5 supports cross-role and cross-disciplinary operational collaboration. This five-level maturity model provides a clear framework for defining goals and development stages in the construction of E&P intelligent agents for oil companies.
Keywords: oil companies; exploration and production; intelligent agents; digital job roles; maturity model; operational collaboration
In recent years, artificial intelligence—particularly large language model technologies—has evolved rapidly, and “intelligent agents” have gradually become a key lever for enterprise digital transformation and intelligent upgrading. However, in the oil and gas industry, especially within exploration and production (E&P), there remains significant divergence in the understanding of intelligent agents. Some interpret them as intelligent Q&A systems, others regard them as office assistants, while some even expect them to enable job replacement and operational collaboration. This conceptual ambiguity can easily lead to misaligned development objectives, blurred capability boundaries, and distorted expectations of practical applications. For oil companies, the primary question is not “which model to use,” but rather “what kind of intelligent agents are truly needed.”
E&P operations are characterized by a high degree of complexity. First, the business chain is extensive, involving multiple disciplines such as geology, reservoir engineering, drilling and completion, production operations, equipment integrity, health, safety and environment (HSE), and supply chain management. Second, information sources are highly fragmented, with documents, knowledge bases, daily reports, work orders, business systems, monitoring systems, ledgers, and meeting minutes distributed across different platforms and formats. Third, a large portion of the work is not simple information retrieval, but rather involves analysis, judgment, workflow execution, and output generation under rule-based constraints. Therefore, the intelligent agents required by oil companies should not merely be “conversation tools,” but systems capable of understanding business context, automatically completing missing information, producing conclusions with supporting evidence, and initiating subsequent actions or escalating to human intervention when necessary.
Based on this, this paper focuses on two key questions: first, what kind of intelligent agents oil companies require; and second, how to construct a five-level maturity model of intelligent agents from the perspective of business application stages. The discussion concentrates on target forms and hierarchical classification, without involving specific technical architectures, implementation conditions, or deployment pathways.
For oil companies, truly mature professional intelligent agents are not Q&A chatbots that remain at the level of natural language interaction. Rather, they should function as “digital roles” capable of consistently producing deliverables in specific business scenarios. This means that the value of an intelligent agent should not be judged by whether it can “speak like a person,” but by whether it can “work like a role.”
Such agents should possess at least five fundamental characteristics.
First, they should truly understand the business. They should not only possess general knowledge, but also understand the professional language, workflows, job roles, and constraints of oil companies. They should be able to distinguish the different meanings of concepts such as “abnormality,” “risk,” “priority,” and “closed-loop management” across different disciplines.
Second, they should be able to access real enterprise systems and data, including enterprise databases, knowledge bases, daily reports, work order systems, ERP, MES, LIMS, SCADA, equipment ledgers, procurement systems, contract systems, and meeting minutes.
Third, they should not merely “provide suggestions,” but should be able to drive work forward. For example, they should be capable of automatically retrieving data, generating conclusions, drafting reports, initiating approvals, creating work orders, tracking status, and issuing overdue reminders.
Fourth, they should have controllability and boundary awareness. They should operate under the constraints of safety, compliance, and confidentiality, with controllable permissions, traceable processes, and auditable results.
Fifth, they should have the ability to continuously improve. They should learn in an orderly manner under the framework of institutional updates, project reviews, case accumulation, and enterprise governance.
More specifically, mature professional intelligent agents should be able to understand task contexts, automatically complete the information required for execution, output “conclusions + evidence + next actions,” and determine when human intervention is needed. In this way, they can shorten the entire process from problem identification to problem resolution. Such agents are closer to “digital colleagues” within an enterprise than to passive response tools.
The focus of intelligent agent development for oil companies is not to enable every employee to engage in generalized conversations with AI, but to embed agents into business scenarios that are high-frequency, repetitive, and judgment-intensive. In other words, the first question an intelligent agent should address is not whether it can provide an answer, but whether it can create business value in the scenarios most worthy of transformation.
From the perspective of application roles, intelligent agents in oil companies should first undertake at least four types of tasks.
First, they should serve as “business data query specialists.” For example, production, research, equipment, sales, and supply chain personnel often need to quickly answer questions such as: Which wells show abnormal production? Which equipment failure rates are increasing? Which batches of materials face the highest delivery risk? Which customers are slow in payment collection? Why is energy consumption high in a certain area? Traditionally, answering these questions requires searching for data, locating reports, asking colleagues, and piecing together information. Intelligent agents, by contrast, should be able to automatically retrieve data, perform comparisons, explain abnormalities, propose possible causes, and provide supporting evidence, thereby transforming the work process from “finding data” to “obtaining conclusions.”
Second, they should serve as “analysis assistants.” Many business problems do not lack data; what is truly lacking is the ability to quickly organize fragmented data and conduct structured analysis. Questions such as why drilling progress is behind schedule, what the main cause of an unplanned shutdown is, whether safety incidents in the past three months share common characteristics, and why the procurement cycle for certain spare parts is becoming longer all require the integration of daily reports, logs, work orders, meeting minutes, and historical cases. The output should include key findings, cause classification, impact assessment, and recommended actions. At this level, the value of intelligent agents lies in compressing fragmented information into decision-ready content.
Third, they should serve as “workflow facilitators.” In oil companies, the real difficulty of many tasks does not lie in professional judgment itself, but in lengthy processes, slow collaboration, and complex follow-up. Examples include equipment abnormality closed-loop management, tracking of hazard rectification, procurement requests and price comparison, contract review, preparation of technical scheme review materials, weekly and monthly report compilation, and execution of research evaluation tasks. Intelligent agents can break these workflows down into clear steps and perform actions such as draft generation, material verification, missing-item reminders, responsibility assignment, status recording, and automatic follow-up, thereby reducing the cost of manual tracking and cross-departmental coordination.
Fourth, they should act as “amplifiers of expert experience.” In the oil and gas industry, many critical judgments rely heavily on expert experience. For example, engineers can quickly identify unstable gas lift, wax deposition, or equipment abnormalities based on parameter changes. If intelligent agents can integrate expert rules, historical cases, handling manuals, and real-time data, they can provide preliminary judgments similar to those of experts. This helps transform individual experience into organizational capability, enabling ordinary employees to approach high-level operational performance under standardized support.
In the practice of exploration and production, oil companies do not need an all-encompassing “universal agent.” Instead, they need a group of “small but powerful” professional agents. Specifically, professional agent groups with clear divisions of responsibility can be developed around typical scenarios such as production abnormality analysis, equipment fault diagnosis and maintenance recommendations, safety hazard identification and closed-loop tracking, procurement demand review and sourcing suggestions, contract review, technical scheme evaluation, automatic preparation of weekly and monthly reports, and training and knowledge Q&A. These agents can then be collaboratively invoked through a unified entry point or scheduling mechanism.
This model is more consistent with how enterprises actually operate. An enterprise does not rely on one person to complete all tasks; instead, business objectives are achieved through collaboration among different roles, such as dispatching, engineering, procurement, legal affairs, finance, and operations. Therefore, what oil companies truly need is essentially a group of professional intelligent agents oriented toward specific business scenarios and role divisions. The ultimate goal is to build a digital work system that understands the business, executes tasks, and collaborates effectively.
For oil companies, the evaluation of whether an intelligent agent has truly been implemented should not focus solely on the fluency of its language interaction. More importantly, it should focus on whether the agent has generated measurable value in actual business operations. This can be assessed from five perspectives: whether it reduces the number of person-to-person communications, whether it shortens the time required for data preparation and organization, whether it reduces omissions and misjudgments, whether it shortens the business closed-loop cycle, and whether it improves the working capability of ordinary employees.
If an intelligent agent fails to deliver substantive improvements in these dimensions, it remains at the stage of a demonstrative product and has not truly entered business application. In summary, a mature intelligent agent should be an organic integration of professional knowledge, enterprise data, workflow execution, risk controllability, and continuous optimization.
The core of the intelligent agent maturity model does not lie in how large the model parameters are or how advanced the underlying algorithms are, but in the actual business impact it delivers. Therefore, from the perspective of user-level application, professional intelligent agents in oil companies can be classified into five maturity levels: L1 question-answering agents, L2 assistant agents, L3 scenario-execution agents, L4 role-based agents, and L5 operational collaboration agents. This classification does not represent a purely technical hierarchy, but rather reflects the depth of business impact and the degree of responsibility assumed by the agent.
L1 represents the most basic form of intelligent agents. Its core capability is to answer general questions, data-related queries, and certain domain-specific knowledge questions. It mainly relies on enterprise data, regulations, operational manuals, knowledge bases, or knowledge graphs for retrieval-based question answering. At this stage, users still need to independently judge whether the output is usable, and the agent cannot truly drive business processes.
In oil companies, L1 is mainly used to answer regulatory interpretations, production data queries, terminology explanations, process and standard descriptions, and simple document summarization, as well as to support natural language queries over enterprise data. Its primary value lies in reducing the cost of knowledge retrieval and repetitive consultation, enabling users to access data and basic knowledge through natural language. However, its limitations are clear: it only provides answers without taking action; it does not understand specific business contexts; and it cannot handle complex tasks or cross-system processes. Essentially, L1 addresses the need to “find knowledge, regulations, explanations, and data.”
L2 builds on L1 by adding content generation and contextual understanding capabilities. It not only answers questions but can also summarize content, draft documents, provide suggestions, and generate diagrams. It can produce reports, meeting minutes, analysis documents, and professional visualizations based on templates, while still operating in a human-led, AI-assisted mode.
In oil companies, L2 agents are mainly used for generating draft weekly and monthly reports, summarizing equipment failure records, assisting procurement, contract, and production personnel in drafting analytical materials, and producing reports and visualizations. Their primary value lies in significantly reducing the time required for document preparation and reporting, thereby improving daily operational efficiency. However, L2 also has clear limitations: it mainly provides suggestions and drafts, while actual business execution still depends on human intervention; output quality is highly dependent on context quality and usage patterns. Therefore, L2 primarily addresses the task of “writing materials, producing summaries, and generating drafts.”
L3 represents a key turning point where intelligent agents move from “being able to talk” to “being able to act.” Its defining characteristic is deep integration into specific business scenarios, with the ability to connect to enterprise data and certain business systems, completing a closed loop of data retrieval, analysis, and output, and triggering predefined actions under rule constraints.
In oil company scenarios, L3 agents can automatically analyze production anomalies and generate lists of potential causes, identify key equipment risks based on alarms, work orders, and maintenance records, check procurement request completeness, or extract action items from meeting minutes and track responsible parties. Compared with L1 and L2, L3 no longer simply provides answers or drafts, but instead completes relatively end-to-end business workflows within a defined scenario, significantly shortening processing cycles and improving role-level efficiency. Its limitation is that its scope is still largely confined to well-defined scenarios, and its ability to handle ambiguous, cross-domain, or highly complex tasks remains limited. Therefore, L3 primarily addresses “closed-loop execution of single scenarios.”
L4 is one of the most strategically important levels for oil companies today. Its defining feature is that intelligent agents begin to approximate “digital roles.” They are capable of understanding role objectives, business constraints, and process rules, coordinating across multiple systems, autonomously executing routine tasks, and requesting human approval at key decision points.
In oil companies, typical L4 applications include equipment integrity agents that continuously monitor anomalies, generate work order recommendations, prioritize interventions, and track closure; production operation agents that produce daily anomaly lists, impact assessments, and remediation suggestions while driving execution at the team level; procurement agents that support end-to-end workflows from demand validation, quotation preparation, and risk alerts to process follow-ups; and research-oriented agents that can organize parts of research workflows, retrieve data, generate maps, documents, and reports.
The value of L4 lies in systematically taking over a large portion of repetitive, rule-based, and judgment-intensive tasks within a role, thereby amplifying employee capabilities and improving cross-functional collaboration efficiency. However, it also imposes higher requirements on data governance, permission control, responsibility boundaries, and standardized process design. Therefore, L4 addresses the challenge of “continuously executing a full category of role-based work.”
L5 represents the highest level of maturity for professional intelligent agents. At this stage, agents are no longer isolated tools but an enterprise-level intelligent system capable of orchestrating multiple specialized agents to collaboratively achieve complex business objectives, supported by unified monitoring, accountability, auditing, knowledge accumulation, and continuous evolution mechanisms.
In oil company scenarios, L5 manifests as coordinated operation among production, research, equipment, safety, supply chain, finance, and sales agents. These agents work together to form a complete closed loop from anomaly detection and resource coordination to operational impact assessment. For management, the output is no longer fragmented data, but integrated insights combining risks, root causes, actions, and predictive outcomes. Meanwhile, expert knowledge, institutional rules, and historical cases are continuously accumulated into organizational capabilities within the system.
The core value of L5 is to establish an enterprise-grade intelligent operational foundation, enabling a shift from “humans searching for information” to “systems proactively identifying issues and driving resolution,” thereby supporting cost reduction, efficiency improvement, risk control, and quality enhancement. However, L5 is also the most challenging level to implement, requiring systemic transformation in organizational structure, business processes, data foundations, and IT architecture. Therefore, L5 addresses the problem of “cross-role, cross-discipline, and cross-organizational operational collaboration.”
From the perspective of capability evolution, the five maturity levels can be summarized as five continuous stages of upgrading: L1 is the ability to answer; L2 is the ability to write; L3 is the ability to execute a single task; L4 is the ability to handle an entire category of work; and L5 is the ability to orchestrate end-to-end collaboration.
More specifically, L1 represents the transition from scattered information and human consultation to accessible knowledge and data retrieval; L2 represents the transition from knowing answers to generating deliverables; L3 represents the transition from providing suggestions to completing closed-loop scenarios; L4 represents the transition from completing a scenario to assuming role-level responsibilities; and L5 represents the transition from role-based intelligence to enterprise-wide operational intelligence.
This model illustrates that the essence of intelligent agent maturity is not merely an improvement in language generation capability, but a continuous enhancement of business responsibility, execution capability, and organizational collaboration capacity. The progression across levels is not a simple parallel classification, but a continuous evolution from localized tools to systemic capabilities, and from individual assistance to organizational empowerment.
The construction of exploration and production intelligent agents for oil companies should first clarify not the level of technological sophistication, but the positioning of business objectives. Truly valuable intelligent agents are not general-purpose assistants limited to question-answering functions. Instead, they should function as “digital role-based agents” capable of understanding professional business contexts, accessing real operational data, executing workflow tasks, and delivering stable outputs within controllable boundaries.
Such agents should be prioritized in high-frequency, repetitive, and judgment-intensive business scenarios, where they take on key roles such as data querying, analysis, workflow execution, and experience amplification. Ultimately, this will lead to the formation of a domain-oriented ecosystem of specialized intelligent agents tailored to different scenarios and job functions.
On this basis, the five-level maturity model provides a clear stage-based framework for intelligent agent development in oil companies: L1 addresses knowledge and data retrieval; L2 focuses on content and deliverable generation; L3 enables closed-loop execution within single scenarios; L4 supports continuous role-level task delegation; and L5 achieves enterprise-level operational collaboration intelligence. Each level corresponds to different problem types, capability boundaries, and business values.
Only by accurately identifying the problems addressed by intelligent agents at each maturity level can enterprises define development objectives scientifically and avoid misrepresenting lower-level capabilities as higher-level ones. In this way, a clear implementation pathway can be established for the construction of intelligent agent systems in exploration and production within oil companies.
About Us
