The accountability processes and responsibility tracing of problems in the oilfield production are significant for the management of oilfield companies. This study proposes a comprehensive approach leveraging semantic analysis techniques to optimize the accountability processes and responsibility tracing of production problems in oilfields. Semantic analysis is applied on the texts of both the problem description and the job responsibility. A storage method is established for semantic responsibilities to facilitate the key-value matching. Subsequently, a Semantic Accountability Mechanism (SAM) based on the M3E-BASE model is proposed, which screens positions according to the management areas. Both the M3E-BASE model and the semantic similarity matching are employed in the SAM to achieve the precise alignment between the problem descriptions and the job responsibilities. Additionally, a Problem Tracing Mechanism (PTM) based on semantic networks is developed, involving the construction of a semantic network at the job level and an accountability tracing inference machine. The PTM enables automated problem tracing by inferring the hierarchical relationships among the positions in the outcomes of accountability. Experimental evaluations and practical implementations validate the effectiveness of the proposed mechanisms. Specifically, the SAM both reduces the time and improves the accuracy of accountability significantly. The PTM automates the tracing process and attributes the responsibilities of abnormal events in the oilfield production activities effectively. Consequently, the research is benefit for advancing the level of oilfield management, improving the efficiency, accuracy and automation of oilfield operations, and enhancing the overall productivity and safety of oilfield practice.