Generative AI Enabled Conversational Chatbot for Drilling and Production Analytics
Authors: Singh Ajay , Jia Tianxia , Nalagatla Varun 
Published: October 2023

Getting intelligent insight from large amount of dataset is critical for Energy companies to optimize their operations across various business segments such as drilling, production and completion etc. The paper proposes end-to-end workflow to 1) extract data form rig and production reports and store dataset into databases 2) build a conversational generative AI enabled chatbot which is trained to answer questions related to drilling and production monitoring, queries dataset, frequently performed diagnostic analysis and can generate recommendations to improve operations. The chatbot is integrated with large language models (LLM) and machine learning models (ML) on the cloud and based on questions asked by user it provides answers in conversational settings.

Chatbot is hosted in cloud and is integrated with various databases, document repositories and several machine learning model. The machine learning models are built to enable chatbot's capability to answer questions related to drilling and production analytics. Chatbot is integrated with user interface where user can type or ask questions. Using natural language process (NLP) and artificial intelligence (Al), chatbot understands intent of question and if needed asks relevant follow-up questions to provide the answer. Chatbot can also perform statistical analysis, generate SQL queries on datasets and can use those statistics to answer questions. Further if enabled, chatbot can also search information from drilling and production reports and scientific articles. Three case studies are presented. In case study#1, chatbot was integrated with operator's historical PDF drilling reports (Volve dataset), which traditionally are not easy to extract and analyze at scale. Several thousand drilling reports were extracted and stored in database. Various capabilities were added to chatbot such has Cross-documents insights and trend, for example, well progression, operation history, can be generated and displayed on user interface and further analysis can be performed in conversational manner. The dataset created was used to perform comparative analysis identifying wells having significant higher non production time (NPT) due to repair or fishing events. In this manner, chatbot can compare one well's operational statistics with other well and generate various visuals which helps identifying possible ways to improve drilling operations. Similarly, chatbot was also trained to provide answers for production diagnostics such as comparing well's relative performances and root cause identification for poor performing wells. When analyzed on test dataset chatbot was able to identify 20% uplift in production for wells supported on plunger lift. Finally, chatbot was enabled to support NLP based searches. Engineers can ask specific questions such as "provide operational log for well F4 when fishing happened and sort the result by reporting date in ascending order. Show me both SQL query and the resulted table" and chatbot will generate SQL query and resulted table. The work demonstrates that generative AI has great potential to transform the Energy industry.