Breaking Barriers and System Construction for 
Innovation-Industrial Chain Integration in Resource-Based SOEs: A Case Study of L Oilfield

Yueyang Wu; Shuangying Li; Han Li; Le Wang

doi:doi:10.11648/j.si.20261403.14

Research Article |

| Peer-Reviewed

Breaking Barriers and System Construction for Innovation-Industrial Chain Integration in Resource-Based SOEs: A Case Study of L Oilfield

Yueyang Wu^*

, Shuangying Li

, Han Li

, Le Wang

Published in Science Innovation (Volume 14, Issue 3)

Received: 18 March 2026 Accepted: 27 May 2026 Published: 29 May 2026

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Abstract

Deepening the integration of the innovation chain and industrial chain ("Dual-Chain Integration") is a core strategic pathway to overcome the sustainable development bottlenecks of mature oilfields, cultivate new quality productivity forces, and ensure national energy security. Taking Oilfield L as a typical case, this paper first constructs an analytical framework encompassing "Factor Aggregation—Platform Empowerment—Mechanism Synergy—Ecosystem Evolution." Through field research and policy text analysis, it systematically diagnoses the prominent challenges faced by Oilfield L in the process of Dual-Chain Integration. A coupled System Dynamics and Agent-Based Modeling (ABM-SD) framework is designed: the upper-level SD module simulates the evolution of macro-level resource constraints and policy environments, while the lower-level ABM module simulates the micro-level game-playing and strategy learning processes of three types of agents—research institutions, production units, and coordination platforms. Multi-agent reinforcement learning is employed to derive flexible organizational models featuring risk-sharing and benefit-sharing mechanisms. Parameter calibration and scenario simulations are conducted based on empirical data from Oilfield L. The findings reveal that: (1) establishing a substantive collaborative innovation platform improves overall system efficiency by approximately 21.6%; (2) technology transfer rates reach an optimal equilibrium when the risk-sharing ratio is optimized to 32% for research institutions and 68% for production units; and (3) building industrial innovation ecosystems around characteristic areas such as heavy oil green development and CCUS can effectively break the structural barrier of a weak pilot-scale testing phase. This study provides a replicable micro-level practical paradigm for resource-based state-owned enterprises to achieve kinetic energy conversion through Dual-Chain Integration.

Published in	Science Innovation (Volume 14, Issue 3)
DOI	10.11648/j.si.20261403.14
Page(s)	84-89
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Dual-Chain Integration (Innovation-Chain and Industrial-Chain), Resource-based SOEs, ABM-SD Coupling Model, Multi-Agent Reinforcement Learning, New Quality Productivity Forces

1．引言

1.1．研究背景与意义

能源作为国民经济的命脉，在国际地缘政治博弈加剧、全球能源供需版图深刻重构的当下，战略属性愈发凸显，已成为大国博弈的关键领域和影响经济社会发展的关键变量。在此背景下，资源型国企的转型发展不仅关乎企业自身的生存与竞争力，更直接关系到国家能源供应的稳定性、能源转型的主导权以及国民经济的整体安全与长远韧性。”近年来，L油田相继发布《关于支持全面创新的相关举措（试行）》等系列文件，明确提出“推动科技创新与产业创新融合，因地制宜培育新质生产力”，并布局“3+12+4”技术体系，旨在通过创新链与产业链深度融合实现转型升级

[1, 2]

。

从理论层面看，创新链与产业链融合涉及宏观政策环境、中观组织架构、微观主体行为的多层次交互，传统单一建模方法难以捕捉系统的复杂反馈与涌现特征

[3]

。本文引入系统动力学与多智能体建模相耦合的混合仿真方法，为“双链融合”研究提供新的方法论视角。

从实践层面看，L油田作为典型资源型国企，其转型经验对我国老工业基地和老油田的动能转换具有重要借鉴意义

[2]

。通过量化模拟不同政策情景下的融合效果，可为决策者提供科学依据。

1.2．研究内容与创新点

本文的核心创新在于：①构建ABM-SD耦合模型，系统刻画“双链融合”的宏观约束与微观博弈

[4, 5]

；②引入多智能体强化学习，内生求解风险共担与利益共享的柔性组织模式；③基于L油田最新政策实践进行参数校准与情景仿真，实现理论模型与实证数据的深度融合

[6]

。

2．理论基础

2.1．ABM-SD耦合建模方法与应用

系统动力学（System Dynamics，SD）擅长处理宏观层面的存量流量与反馈结构，能够刻画政策干预的长期演化效应；多智能体建模（Agent-Based Modeling，ABM）则聚焦微观主体的异质性行为与局部交互，能够揭示个体决策导致的宏观涌现现象

[7, 11]

。

近年来，ABM-SD耦合模型在供应链管理、公共健康、生态系统管理等领域的应用日益广泛。Hosseinnezhad等将其应用于供应链韧性分析，发现耦合模型能够有效捕捉企业间的复杂交互与个体偏好

[8]

。Nguyen等提出了混合仿真概念建模的实用框架

[9]

，为模型开发提供了方法论指导。Pessoa等将其应用于油气行业R&D项目技术成熟度预测，验证了模型在创新管理领域的适用性。

在中文文献中，周愉峰等将ABM+SD耦合建模应用于血液供应链绩效演进研究

[5]

，张家瑞等将其应用于环境政策仿真

[15]

，均取得了良好效果。这为本文在资源型国企场景下的应用提供了方法借鉴。

2.2．理论框架构建

创新链与产业链融合是近年来创新管理学的研究热点。现有研究主要从创新生态系统、技术转移转化、产学研协同等视角展开，但针对资源型国企特殊情境的深入剖析相对有限。基于上述分析，本文构建“要素汇聚—平台赋能—机制协同—生态演进”的四层次分析框架（见图1）。其中，要素汇聚指创新资源与产业资源的双向流动；平台赋能指实体化协同创新联合体的支撑作用；机制协同指风险共担与利益共享的制度设计；生态演进指围绕特色领域形成产业创新生态圈的长远目标。

Download: Download full-size image

Figure 1. 图1 “双链融合”四层次分析框架。

3．L油田“双链融合”的现实诊断

3.1．政策实践梳理

2023年以来，L油田在创新体系建设方面推出多项重要举措：科学立项方面，建立科学技术委员会“自上而下”主导的科技立项模式，突出“业务主导、科技统筹”原则，破解科研与生产“两张皮”问题；简政放权方面，简化科技项目外协程序，扩大科技项目经费自主权，将预算调整权限下放给项目承担单位；精准激励方面，实施“科技项目基础奖”制度，设置“‘揭榜挂帅’项目奖”和“科技成果转化创效奖”，鼓励技术成果产业化，奖励上不封顶；创新联合体方面，扎实推进深层油气、页岩油、非常规油气、稠油绿色低碳开发等创新联合体建设，探索联合攻关、成果共享的有效机制；人才培养方面，打破“论资排辈”桎梏，建立青年人才破格晋升通道；容错机制方面，明确对未达预期的科学探索项目给予充分包容，鼓励探索不怕试错。

3.2．主要壁垒诊断

尽管政策举措密集出台，但L油田在“双链融合”过程中仍面临以下突出挑战：

第一，基础研究与应用开发脱节。科研机构追求技术前沿与学术突破，生产单元关注短期稳产与成本控制，二者目标函数不一致导致创新方向与实际需求错位。

第二，成果转化中试环节薄弱。从实验室成果到工业化应用之间缺乏有效的中试平台和风险分担机制，大量技术成果“沉睡”在实验室。

第三，跨组织协同成本高昂。研究院、采油厂、管理部门之间信息壁垒较高，协同决策流程繁琐。

第四，创新收益分配机制不健全。成果转化收益在科研团队、研究院、生产单元之间的分配比例缺乏科学依据，激励效果有限。

4．ABM-SD耦合模型构建

4.1．模型总体架构

本文采用AnyLogic仿真平台构建ABM-SD耦合模型，模型架构由上层SD模块和下层ABM模块构成，二者通过双向接口实现动态交互：

SD模块模拟宏观资源环境，包括总资产存量、总人才存量、碳排放配额、累计原油产量等存量变量，以及油价波动、政策约束等外生变量。SD模块每季度向ABM模块传递“总预算上限”“年度减排目标”等约束参数。

ABM模块模拟微观主体行为，包含科研机构智能体、生产单元智能体、协调平台智能体三类。智能体在SD模块设定的宏观约束下进行策略博弈，其博弈结果（如实际技术转化率、实际协同成本）反馈至SD模块，调整相关速率变量。这一耦合架构借鉴了Nguyen等提出的混合仿真概念框架和Pessoa等在R&D项目中的应用。

4.2．SD模块设计

SD模块的存量流量主要方程包括：

创新链存量

K_{I}

(t)：

\frac{d K_{I}}{dt} = R_{D} (t) - O_{TR} (t) - D_{I} \cdot K_{I}

(t)(1)

其中，

R_{D} (t)

为研发投入速率，

O_{TR} (t)

为成果转化流出速率，

D_{I}

为知识折旧率。

产业链存量

K_{P} (t

\frac{d K_{p}}{dt} = P_{OUT} (t) - O_{TR} (t) \cdot η - D_{P} \cdot K_{P}

(t)(2)

其中，

P_{OUT} (t)

为常规产出速率，

η

为成果转化对产出的贡献系数。

总资产存量

A (t) : \frac{d A_{}}{dt} =

R(t)

- C_{RD} (t) - C_{OP} (t)

(3)

其中，R(t)为营业收入，

C_{RD} (t)

为研发投入，

C_{OP} (t)

为运营成本。

4.3．ABM模块设计

4.3.1．智能体设定

参照Hosseinnezhad等对供应链协同的建模思路，本文设置三类智能体：

科研机构智能体（Agent_R）：代表研究院、实验室等创新主体。目标函数为最大化科研成果转化率与学术影响力，策略空间包括研发方向选择（基础研究/应用研究）、中试参与意愿、成果定价等。

生产单元智能体（Agent_P）：代表采油厂、炼化企业等产业链主体。目标函数为最大化采收率与成本控制，策略空间包括新技术采纳意愿、中试风险承担比例、内部技术采购决策等。

协调平台智能体（Agent_C）：代表实体化运营的协同创新联合体。目标函数为最大化“双链融合”整体效能，策略空间包括风险补偿比例设定、信息透明度调节、联合基金分配等。

4.3.2．智能体学习机制

引入多智能体强化学习，采用多智能体深度确定性策略梯度（MADDPG）算法进行训练

[10]

。每个智能体i 的观测空观测空间 oi 、动作空间ai 和奖励函数ri定义如下：

观测空间：包括SD模块传递的宏观约束（预算上限、碳配额）、其他智能体的历史策略、技术成熟度水平、市场环境参数等。

动作空间：

Agent_R：基础研究投入比例aR，1

¡Ê [0 ， 1

]，成果定价系数aR，2

¡Ê [0.5 ， 2

]

Agent_P：新技术采纳比例 aP，1

\in [0 ， 1

]，风险承担比例，aP，2

\in [0 ， 1

]，

Agent_C：风险补偿比例aC，1

\in

[0，0.5]，信息透明度aC，2

\in

[0，1]

奖励函数：设计包含多目标的奖励函数：

ri=w1·

{Profit}_{i}

−w2·

{Risk}_{i}

+w3·

{Coop}_{i}

(4)

其中，

{Profit}_{i}

为个体收益，

{Risk}_{i}

为承担风险，

{Coop}_{i}

为

协同贡献，权重系数通过层次分析法确定。

4.3.3．耦合机制设计

ABM与SD的耦合采用“弱耦合”方式：以季度为时间步长，SD模块更新宏观状态变量并传递至ABM模块；ABM模块进行多轮微观博弈，将聚合后的行为结果（如平均转化率、平均协同成本）反馈至SD模块，调整下一期的速率参数。

4.4．参数校准与数据来源

模型参数校准基于以下数据来源：

① L油田2022-2025年科技统计年报、财务报表；②《L油田公司关于支持全面创新的相关举措（试行）》等政策文件；③“3+12+4”技术体系相关数据；④对研究院、采油厂管理人员的深度访谈（N=15）；⑤对于无法直接获取的参数，采用遗传算法进行反向校准，使模型输出与历史数据拟合度达到85%以上。

5．仿真实验与结果分析

5.1．实验设计

设置三组对比情景进行仿真实验，仿真周期为10年（120个月），蒙特卡洛运行次数为50次：

情景S0（基准情景）：维持现有体制机制，无实体化联合体，智能体基于本地信息独立决策。

情景S1（政策干预情景）：引入实体化协同创新联合体（Agent_C），联合体负责风险补偿和信息协调，但收益分配沿用现行规则。

情景S2（优化情景）：在S1基础上，采用MADDPG算法优化收益分配比例，实现激励相容。

5.2．结果分析

5.2.1．整体效能对比

仿真结果显示，S2情景下的系统整体效能较S0提升21.6%，较S1提升9.3%。具体指标方面：

技术转化率从S0的34.2%提升至S2的67.8%；

中试周期从平均18个月缩短至9.5个月；

跨组织协同成本下降28.3%；

这一结果与Hosseinnezhad等关于供应链协同的研究发现一致，即协调机制的引入能够显著提升系统韧性。

5.2.2．风险分担优化

通过MADDPG算法训练，模型收敛到最优风险分担比例：科研机构承担32%，生产单元承担68%。这一比例下，科研机构有足够动力推进技术研发，生产单元也因承担可控风险而愿意参与中试，实现了风险与收益的平衡

[12]

。

敏感性分析表明，该最优比例对技术成熟度敏感：对于成熟度较高的技术（TRL≥7），生产单元愿意承担更高比例（可达75%）；对于早期技术（TRL≤4），需要协调平台提供更多风险补偿。

5.2.3．特色领域生态圈仿真

针对L油田“3+12+4”技术体系，分别对稠油绿色开采、CCUS、储气库建设三个特色领域进行专项仿真。结果表明：

稠油绿色开采领域：SAGD技术产业化进程最快，第5年可实现规模化应用；

CCUS领域：初期投入大、回报周期长，需要持续的政策补贴支持；

储气库建设领域：涉及多主体协同，对信息透明度要求最高。

5.3．鲁棒性检验

通过改变油价波动幅度（±20%）、碳配额收紧速度、技术外生突破概率等外生参数，对模型进行敏感性分析。结果显示，S2情景在各组参数下均保持优于S0和S1的表现，表明优化方案具有较好的鲁棒性。

6．体系构建与政策建议

6.1．实体化协同创新联合体建设

基于仿真结论，建议L油田进一步做实“创新联合体”机制：一是赋予联合体实质性资源配置权，设立联合创新基金；二是建立联合体层面的技术成熟度评估体系，分类施策推进成果转化；三是将联合体作为“风险共担池”，为早期技术提供风险补偿。

6.2．柔性组织模式与激励相容设计

采用MADDPG算法优化出的收益分配比例（科研32%、生产68%）作为基准

[13]

，结合技术成熟度动态调整。同时，借鉴L油田“科技成果转化创效奖”经验，将奖励与转化实效挂钩，上不封顶。

6.3．数智驱动的产业创新生态圈

将仿真模型产品转化为“双链融合智能决策支持系统”，实现融合壁垒的实时监控与组织模式的动态调整。围绕“3+12+4”技术体系，构建稠油绿色开采、CCUS、储气库建设等特色领域的创新生态圈，形成可复制的“L模式”

[14]

。

7．结论与展望

本文以L油田为实证对象，构建ABM-SD耦合模型系统研究了资源型国企“双链融合”的壁垒突破与体系构建问题。研究揭示了宏观政策环境与微观主体行为的互动机制，量化评估了实体化协同创新联合体的政策效果，求解了激励相容的风险分担与收益分配比例，为L油田战略转型提供了科学依据。

研究存在以下局限：一是模型参数校准受数据可得性限制，部分参数依赖专家判断；二是智能体行为规则进行了必要简化，未能完全反映现实复杂性。未来研究可引入图神经网络挖掘智能体交互网络的动态演化特征

[16]

，进一步深化对“双链融合”微观机制的理解。

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Wu, Y., Li, S., Li, H., Wang, L. (2026). Breaking Barriers and System Construction for Innovation-Industrial Chain Integration in Resource-Based SOEs: A Case Study of L Oilfield. Science Innovation, 14(3), 84-89. https://doi.org/10.11648/j.si.20261403.14

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ACS Style

Wu, Y.; Li, S.; Li, H.; Wang, L. Breaking Barriers and System Construction for Innovation-Industrial Chain Integration in Resource-Based SOEs: A Case Study of L Oilfield. Sci. Innov. 2026, 14(3), 84-89. doi: 10.11648/j.si.20261403.14

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AMA Style

Wu Y, Li S, Li H, Wang L. Breaking Barriers and System Construction for Innovation-Industrial Chain Integration in Resource-Based SOEs: A Case Study of L Oilfield. Sci Innov. 2026;14(3):84-89. doi: 10.11648/j.si.20261403.14

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@article{10.11648/j.si.20261403.14,
  author = {Yueyang Wu and Shuangying Li and Han Li and Le Wang},
  title = {Breaking Barriers and System Construction for 
Innovation-Industrial Chain Integration in Resource-Based SOEs: A Case Study of L Oilfield},
  journal = {Science Innovation},
  volume = {14},
  number = {3},
  pages = {84-89},
  doi = {10.11648/j.si.20261403.14},
  url = {https://doi.org/10.11648/j.si.20261403.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20261403.14},
  abstract = {Deepening the integration of the innovation chain and industrial chain ("Dual-Chain Integration") is a core strategic pathway to overcome the sustainable development bottlenecks of mature oilfields, cultivate new quality productivity forces, and ensure national energy security. Taking Oilfield L as a typical case, this paper first constructs an analytical framework encompassing "Factor Aggregation—Platform Empowerment—Mechanism Synergy—Ecosystem Evolution." Through field research and policy text analysis, it systematically diagnoses the prominent challenges faced by Oilfield L in the process of Dual-Chain Integration. A coupled System Dynamics and Agent-Based Modeling (ABM-SD) framework is designed: the upper-level SD module simulates the evolution of macro-level resource constraints and policy environments, while the lower-level ABM module simulates the micro-level game-playing and strategy learning processes of three types of agents—research institutions, production units, and coordination platforms. Multi-agent reinforcement learning is employed to derive flexible organizational models featuring risk-sharing and benefit-sharing mechanisms. Parameter calibration and scenario simulations are conducted based on empirical data from Oilfield L. The findings reveal that: (1) establishing a substantive collaborative innovation platform improves overall system efficiency by approximately 21.6%; (2) technology transfer rates reach an optimal equilibrium when the risk-sharing ratio is optimized to 32% for research institutions and 68% for production units; and (3) building industrial innovation ecosystems around characteristic areas such as heavy oil green development and CCUS can effectively break the structural barrier of a weak pilot-scale testing phase. This study provides a replicable micro-level practical paradigm for resource-based state-owned enterprises to achieve kinetic energy conversion through Dual-Chain Integration.},
 year = {2026}
}

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TY  - JOUR
T1  - Breaking Barriers and System Construction for 
Innovation-Industrial Chain Integration in Resource-Based SOEs: A Case Study of L Oilfield
AU  - Yueyang Wu
AU  - Shuangying Li
AU  - Han Li
AU  - Le Wang
Y1  - 2026/05/29
PY  - 2026
N1  - https://doi.org/10.11648/j.si.20261403.14
DO  - 10.11648/j.si.20261403.14
T2  - Science Innovation
JF  - Science Innovation
JO  - Science Innovation
SP  - 84
EP  - 89
PB  - Science Publishing Group
SN  - 2328-787X
UR  - https://doi.org/10.11648/j.si.20261403.14
AB  - Deepening the integration of the innovation chain and industrial chain ("Dual-Chain Integration") is a core strategic pathway to overcome the sustainable development bottlenecks of mature oilfields, cultivate new quality productivity forces, and ensure national energy security. Taking Oilfield L as a typical case, this paper first constructs an analytical framework encompassing "Factor Aggregation—Platform Empowerment—Mechanism Synergy—Ecosystem Evolution." Through field research and policy text analysis, it systematically diagnoses the prominent challenges faced by Oilfield L in the process of Dual-Chain Integration. A coupled System Dynamics and Agent-Based Modeling (ABM-SD) framework is designed: the upper-level SD module simulates the evolution of macro-level resource constraints and policy environments, while the lower-level ABM module simulates the micro-level game-playing and strategy learning processes of three types of agents—research institutions, production units, and coordination platforms. Multi-agent reinforcement learning is employed to derive flexible organizational models featuring risk-sharing and benefit-sharing mechanisms. Parameter calibration and scenario simulations are conducted based on empirical data from Oilfield L. The findings reveal that: (1) establishing a substantive collaborative innovation platform improves overall system efficiency by approximately 21.6%; (2) technology transfer rates reach an optimal equilibrium when the risk-sharing ratio is optimized to 32% for research institutions and 68% for production units; and (3) building industrial innovation ecosystems around characteristic areas such as heavy oil green development and CCUS can effectively break the structural barrier of a weak pilot-scale testing phase. This study provides a replicable micro-level practical paradigm for resource-based state-owned enterprises to achieve kinetic energy conversion through Dual-Chain Integration.
VL  - 14
IS  - 3
ER  -

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Author Information

Yueyang Wu

Economics and Technology Research Institute, Petrochina Liaohe Oilfield Company, Panjin, China

Contact Email

http://orcid.org/0009-0004-1489-1796
Shuangying Li

Economics and Technology Research Institute, Petrochina Liaohe Oilfield Company, Panjin, China

Contact Email

http://orcid.org/0009-0007-2692-3523
Han Li

Economics and Technology Research Institute, Petrochina Liaohe Oilfield Company, Panjin, China

Contact Email

http://orcid.org/0009-0003-9605-5953
Le Wang

Economics and Technology Research Institute, Petrochina Liaohe Oilfield Company, Panjin, China

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Figure 1

Figure 1. 图 1 “双链融合”四层次分析框架。

Plain Text BibTeX RIS

APA Style

Wu, Y., Li, S., Li, H., Wang, L. (2026). Breaking Barriers and System Construction for Innovation-Industrial Chain Integration in Resource-Based SOEs: A Case Study of L Oilfield. Science Innovation, 14(3), 84-89. https://doi.org/10.11648/j.si.20261403.14

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ACS Style

Wu, Y.; Li, S.; Li, H.; Wang, L. Breaking Barriers and System Construction for Innovation-Industrial Chain Integration in Resource-Based SOEs: A Case Study of L Oilfield. Sci. Innov. 2026, 14(3), 84-89. doi: 10.11648/j.si.20261403.14

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AMA Style

Wu Y, Li S, Li H, Wang L. Breaking Barriers and System Construction for Innovation-Industrial Chain Integration in Resource-Based SOEs: A Case Study of L Oilfield. Sci Innov. 2026;14(3):84-89. doi: 10.11648/j.si.20261403.14

Copy | Download

@article{10.11648/j.si.20261403.14,
  author = {Yueyang Wu and Shuangying Li and Han Li and Le Wang},
  title = {Breaking Barriers and System Construction for 
Innovation-Industrial Chain Integration in Resource-Based SOEs: A Case Study of L Oilfield},
  journal = {Science Innovation},
  volume = {14},
  number = {3},
  pages = {84-89},
  doi = {10.11648/j.si.20261403.14},
  url = {https://doi.org/10.11648/j.si.20261403.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20261403.14},
  abstract = {Deepening the integration of the innovation chain and industrial chain ("Dual-Chain Integration") is a core strategic pathway to overcome the sustainable development bottlenecks of mature oilfields, cultivate new quality productivity forces, and ensure national energy security. Taking Oilfield L as a typical case, this paper first constructs an analytical framework encompassing "Factor Aggregation—Platform Empowerment—Mechanism Synergy—Ecosystem Evolution." Through field research and policy text analysis, it systematically diagnoses the prominent challenges faced by Oilfield L in the process of Dual-Chain Integration. A coupled System Dynamics and Agent-Based Modeling (ABM-SD) framework is designed: the upper-level SD module simulates the evolution of macro-level resource constraints and policy environments, while the lower-level ABM module simulates the micro-level game-playing and strategy learning processes of three types of agents—research institutions, production units, and coordination platforms. Multi-agent reinforcement learning is employed to derive flexible organizational models featuring risk-sharing and benefit-sharing mechanisms. Parameter calibration and scenario simulations are conducted based on empirical data from Oilfield L. The findings reveal that: (1) establishing a substantive collaborative innovation platform improves overall system efficiency by approximately 21.6%; (2) technology transfer rates reach an optimal equilibrium when the risk-sharing ratio is optimized to 32% for research institutions and 68% for production units; and (3) building industrial innovation ecosystems around characteristic areas such as heavy oil green development and CCUS can effectively break the structural barrier of a weak pilot-scale testing phase. This study provides a replicable micro-level practical paradigm for resource-based state-owned enterprises to achieve kinetic energy conversion through Dual-Chain Integration.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Breaking Barriers and System Construction for 
Innovation-Industrial Chain Integration in Resource-Based SOEs: A Case Study of L Oilfield
AU  - Yueyang Wu
AU  - Shuangying Li
AU  - Han Li
AU  - Le Wang
Y1  - 2026/05/29
PY  - 2026
N1  - https://doi.org/10.11648/j.si.20261403.14
DO  - 10.11648/j.si.20261403.14
T2  - Science Innovation
JF  - Science Innovation
JO  - Science Innovation
SP  - 84
EP  - 89
PB  - Science Publishing Group
SN  - 2328-787X
UR  - https://doi.org/10.11648/j.si.20261403.14
AB  - Deepening the integration of the innovation chain and industrial chain ("Dual-Chain Integration") is a core strategic pathway to overcome the sustainable development bottlenecks of mature oilfields, cultivate new quality productivity forces, and ensure national energy security. Taking Oilfield L as a typical case, this paper first constructs an analytical framework encompassing "Factor Aggregation—Platform Empowerment—Mechanism Synergy—Ecosystem Evolution." Through field research and policy text analysis, it systematically diagnoses the prominent challenges faced by Oilfield L in the process of Dual-Chain Integration. A coupled System Dynamics and Agent-Based Modeling (ABM-SD) framework is designed: the upper-level SD module simulates the evolution of macro-level resource constraints and policy environments, while the lower-level ABM module simulates the micro-level game-playing and strategy learning processes of three types of agents—research institutions, production units, and coordination platforms. Multi-agent reinforcement learning is employed to derive flexible organizational models featuring risk-sharing and benefit-sharing mechanisms. Parameter calibration and scenario simulations are conducted based on empirical data from Oilfield L. The findings reveal that: (1) establishing a substantive collaborative innovation platform improves overall system efficiency by approximately 21.6%; (2) technology transfer rates reach an optimal equilibrium when the risk-sharing ratio is optimized to 32% for research institutions and 68% for production units; and (3) building industrial innovation ecosystems around characteristic areas such as heavy oil green development and CCUS can effectively break the structural barrier of a weak pilot-scale testing phase. This study provides a replicable micro-level practical paradigm for resource-based state-owned enterprises to achieve kinetic energy conversion through Dual-Chain Integration.
VL  - 14
IS  - 3
ER  -

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