Innovation and Application of Cooperative Management Mechanism of Progress and Cost of Oil and Gas Exploration and Development Projects

Abstract

Aiming at the inefficiency caused by the separation of schedule and cost control in oil and gas exploration and development projects, a dynamic collaborative management mechanism based on digital twinning and deep reinforcement learning (DRL) is constructed in this study. Through system dynamics modeling, it reveals the dynamic influence of key coupling variables such as geological risk index and engineering complexity on schedule and cost, and puts forward a management framework of "three-layer linkage and dynamic closed loop": using the digital twin platform integrated with Internet of Things and BIM to realize real-time correction of geological risks; The deep Q network (DQN) is used to solve the multi-objective optimal scheduling strategy. Establish a closed-loop process of "early warning-negotiation-adjustment-learning" to improve the management synergy coefficient. Taking Block Y of X Oilfield as an example, after implementation, the project duration was shortened by 2.4 months, the cost overrun rate was reduced by 20.9%, and the inter-departmental decision-making period was shortened to 1.5 days, which verified the remarkable effect of this mechanism in unconventional oil and gas field projects and provided a reusable technical path for project collaborative management under complex geological conditions.