Innovating Oil and Gas Field Operations - Harnessing the Power of Generative Ai for Supporting Workforce Towards Achieving Autonomous Operations

Abstract

Abstract In today's dynamic and competitive oil and gas industry, the integration of Artificial Intelligence (AI) has emerged as a game-changer, offering unparalleled opportunities for optimization, cost reduction, and operational excellence. The main objective of autonomous operations is to minimize manual interactions and maximize self-directed plant operations. ADNOC Onshore has implemented generative AI agents in daily maintenance and production operations to boost workforce productivity in the journey of achieving autonomous operations. This paper explains the use cases, challenges, AI architecture & data security in deployment. Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classification. GPT-4 Turbo is a large multimodal model (accepting text or image inputs and generating text) that can solve difficult problems with greater accuracy and advanced reasoning capabilities. The scope includes empowering reliability, maintenance, and operations professionals to draw insights from equipment manuals, asset operating manuals and operating procedures, maintenance records, and safety & integrity manuals. This in-house solution with support across structured and unstructured data, an LLM-agnostic architecture, deterministic responses with source references, and granular access controls. The solution has been integrated ERP SAP system and sensor time series PI system, data historians for integrated context. A unique automated contextualization engine has been used based on oil and gas specific vocabulary to bring context to their operations. A conversational interactive agent has been built for user interactions. The maintenance and operations engineer can receive suggestions on the proper steps to identify the root cause based on OEM product manuals, previous events, and current performance. This Generative AI solution accelerates time to insight for operators by equipping teams to streamline maintenance operations and Investigate maintenance records with generative AI to troubleshoot operations challenges more efficiently. The internal study showed that operational productivity has increased by 20% after this solution's implementation. For the model to understand industrial environments, it would require retraining the model on industrial data. Using existing models on uncontextualized, unstructured industrial data significantly increases the risk of incorrect and untrustworthy answers – referred to as AI hallucinations. Another significant challenge lies in the dependence on the quality and quantity of available data for training. AI models require extensive and representative datasets to produce accurate and reliable predictions. Large language models are a type of artificial intelligence (AI) model designed to understand and generate human language. These models are built upon deep learning architectures, particularly transformer architectures. Generative AI can play a significant role in oil and gas asset operations towards the goal of achieving autonomous operations.