_ Imagine a day where completions can be designed in less than a day with high accuracy for operation and production success, a day where completions are autonomously designed based on minimal data inputs and can self-optimize upon changes in subsurface data. The effectiveness of the design is then autonomously assessed upon completion of the well, and the experience is fed back into a closed-loop knowledge curve. We are not there yet, but we are on the right trajectory as artificial intelligence (AI) is making waves in the completion discipline. Digitalization, data analytics, and automation (DDAA) have swept across the energy industry over the past decade. Clear value creation can be seen in drilling activities through advancements in autonomous drilling, robotic technologies, and physics-based AI. The end-to-end completions cycle is now seeing a similar trend. At the design stage, agentic conversational analytics is an emerging way of working that integrates data-driven insights with domain expertise. In paper SPE 228143, completions data are integrated into a chat application. The framework includes a router, code executor, and auxiliary agents that manage the conversation and computations. It uses large language models wherein domain expertise is encoded in the core agents through prompts. This enhances the completion design process by enabling quick screening of correlations in the data set based on geographical distribution and recommends optimal completion design and prediction of gas production per foot of completed lateral length. In operations, paper OTC 35540 details digital-twin deployment by integrating the contextual perspective that only humans can provide with engineering processes and sensors. It leverages tridirectionally synced data from operational controls, remote platforms, and phone applications. Data sets from all three elements align prejob modeling and actual operation, providing a single source of truth. The system delivers essential insights into surface operations and downhole fracture characteristics to reduce costs and nonproductive time and improve surface efficiencies and safety. Physical evaluation advancements in completion are also significant. Paper SPE 223570 illustrates the benefit of integrating multiphysics downhole imaging with machine-learning techniques to improve perforation-erosion analysis. Integrated array video and phased-array ultrasound sensor systems provide data analyzed by a two-stage AI model to identify perforations within the well and measure their geometries. A third AI model then analyzes the same perforation geometries from the corresponding ultrasound data set, significantly reducing analysis time and improving consistency and reliability. DDAA, when applied responsibly, opens unlimited possibilities. For completion, its influence on the end-to-end process has been encouraging and innovative. At its current rate of advancement, a fully automated completion process as a business-as-usual practice in the next decade is not impossible. Summarized Papers in This April 2026 Issue OTC 35540 - Engineer-Centered Process Creates Digital Twin of Completions Operations by Travis Thomas, SPE, Yunxho Zhou, and Robert Fairley, NOV, et al. SPE 228143 - Well Completions Optimized by Agentic Conversational Analytics by Carla J. S. Santiago, SPE, Niven Shumaker, and Ashley Weir, SLB SPE 223570 - AI-Enhanced Multiphysics Imaging Advances Perforation-Erosion Analysis by Glyn Roberts, SPE, Souvick Saha, SPE, and Shane Moreno, SPE, EV Offshore, et al. Recommended Additional Reading at OnePetro: www. onepetro. org. IPTC 24725 - Optimization of Inflow-Control-Device Completion Design Using Metaheuristic Algorithms and Supervised Machine-Learning Surrogate by C. Tan, SLB, et al. SPE 224026 - Cloud-Based Slickline Reporting Software Improves Operational Efficiency by J. Segura, SLB, et al. SPE 224045 - Advanced Wireline-Conveyance Modeling Powered by Physics-Informed Neural Network by J. Wang, SLB, et al.
Shahril Mokhtar (Wed,) studied this question.