Pragmatic AI Approach for Enhanced Well Planning and Execution: Leveraging AI Agents to Automate Workflows, Reduce Human Errors, and Improve Decision-Making Quality

Abstract As Artificial Intelligence increasingly integrates into the Oil and Gas industry, significant value-creation opportunities still remain untapped. Drilling engineers spend approximately 60% of their time on low value adding activities such as processing diverse data sets, analyzing historical well data, verifying design choices against industry standards, and generating routine reports. These inefficiencies are further exacerbated by resource conflicts, poorly maintained archives, and human errors. While Industry 3.0 focused on automating processes, Industry 4.0, driven by AI, aims to automate decision-making. Modern AI systems can substantially optimize drilling operations across the entire well lifecycle - from planning to post-analysis - by adhering to three critical success factors: Pragmatic Approach: Deploy AI selectively for tasks where it can deliver sustainable, scalable, and consistent performance. Custom-made LLM Frameworks: Develop language models tailored to specific engineering use cases rather than adapting generic models to specialized tasks. Hybrid Methodology: Integrate fit-for-purpose technologies, such as physics-based machine learning, alongside large language models (LLMs) or other AI algorithms. The solution to free up engineering time for genuine engineering and high value-adding activities is the deployment of data driven and task specific AI Agents to execute a wide range of tasks. These tasks can range from information mining out of large volumes of various data sources (historical well data, real-time feeds, IOT, corporate documents, etc.) to automating workflows or routine tasks. In the presented study an agentic AI approach based on custom-built LLM frameworks has been applied to various drilling datasets with a purpose to automate tasks, reduce time, eliminate operational risks associated with human errors, reduce oversights caused by processing large volumes of data, and dramatically improve real-time decision-making. The following specific use cases were defined (but not limited to) by drilling engineers: primary QA/QC of data archives, building best composite models, Non-Productive Time (NPT) analysis, road mapping of optimized drilling parameters, generating well status diagrams and end-of-well reports based on output templates. These tasks can be executed in mere hours compared to days and weeks, if done conventionally. With these capabilities, deploying AI can significantly reduce non-productive time, allowing engineers to focus on value-adding activities such as enhancing safety, streamlining processes, and implementing cost-effective solutions. While AI cannot (and should not) replace a drilling engineer or automate the entire drilling cycle, it can dramatically augment each engineer's capabilities individually and thus elevate the company excellency as a whole.