Digital Transformation of Well Completion Selection and Design Through Data Insights

Abstract

Well completion selection and design involve the selection of optimum well completion architecture and associated downhole equipment to deliver hydrocarbon to surface in a safe and efficient manner. A number of well architectures can be conceived for a given application, and a plethora of equipment is available across the industry, with hardware to meet a wide range of operating conditions including hole size, pressure, temperature, flow rate and fluid type. This wealth of choice results in a highly complex and challenging selection process that today is done manually, relying on subject matter experts and local best practices through trial and error approach. As a result, the process can be quite inefficient, designs can be suboptimal and fail to consider unique reservoir and well conditions leading to premature equipment failure causing loss of production and well integrity. These failures can have impact ranging from unplanned well intervention, equipment pull outs, fishing operations, extended rig time, workovers, or even complete well loss—costing the oil and gas industry billions of dollars. The shortcomings in design are therefore ripe for innovative digital solutions. This paper describes how manual completion selection process can be seamlessly transformed into an intuitive digital solution providing insights for the well completion selection process. The proposed digital solution describes software tools and architecture used to consolidate thousands of historic, unstructured, completion schematic data into a structured database. It automatically maps the completion architecture and equipment details to relevant operating environments, captures nonproductive time and highlights installation challenges. The solution also identifies correlations and data trends across various types of well designs and equipment categories, using advanced artificial intelligence and machine learning algorithms to provide insights into equipment reliability, operational efficiency, total cost of ownership and production performance. A minimum viable product consisting of 24,000 wells from across the world has been successfully developed to demonstrate key value propositions. New data coming in from recently completed wells can be seamlessly integrated with the existing data bases and the algorithms constantly improvise its learning process to provide better accuracy. The digital solution proposed for well completion selection and design process ultimately enables oil and gas companies to optimize well completion configurations and equipment that can deliver maximum value. It allows them to identify offset well issues, derisk operational concerns, check compatibility of equipment with respect to dimensional constraints, pressure and load rating requirements, thread configurations, metallurgy constraints, seal elements, complete well on digital file with tracing and accountability.