What question did this study set out to answer?

This research aims to identify strategies for integrating innovation and industrial chains to enhance efficiency and overcome development challenges in resource-based enterprises.

June 11, 2026Open Access

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

Key Points

This research aims to identify strategies for integrating innovation and industrial chains to enhance efficiency and overcome development challenges in resource-based enterprises.
Constructed an analytical framework for dual-chain integration focusing on factor aggregation and ecosystem evolution.
Developed a coupled System Dynamics and Agent-Based Modeling (ABM-SD) framework for macro and micro-level simulations.
Conducted parameter calibration and scenario simulations using empirical data from Oilfield L.
Overall system efficiency improved by approximately 21.6% through a collaborative innovation platform.
Optimal technology transfer rates achieved with a risk-sharing ratio of 32% for research institutions and 68% for production units.
Industrial innovation ecosystems can effectively address barriers in the pilot-scale testing phase.

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.

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