Abstract
Well construction is a complex multi-step process that requires decision-making at every step. These decisions, currently made by humans, are inadvertently influenced by past experiences and human factor issues, such as the situational awareness of the decision-maker. This human bias often results in operational inefficiencies or safety and environmental issues. While there are approaches and tools to monitor well construction operations, there are none that evaluate potential action sequences and scenarios and select the best possible sequence of actions. This paper defines a generalized iterative methodology for setting up a digital twin to address this shortcoming. Depending on its application, the objectives and constraints around the twin are formulated. The digital twin is then built using a cyclical process of defining the required outputs, identifying and integrating the necessary process models, and aggregating the required data streams. The twin is set up such that it is predictive in nature, thus enabling scenario analysis. The method is demonstrated here by setting up twinning systems for two different categories of problems. First, an integrated multi-model twin to replicate borehole cleaning operations for stuck-pipe prevention is developed and tested. Second, the creation, implementation, and testing of a twinning system for assisting with operational planning and logistics is demonstrated by considering the time it takes to drill a well to total depth (TD). These twins are also used to simulate multiple future scenarios to quantify the effects of different actions on eventual outcomes. Such systems can help improve operational performance by allowing more informed human, as well as automated, decision-making. Development of a system for well construction operations that integrates multiple sources of information with process and equipment models to quantify the system state and analyzes different scenarios by evaluating action sequences is a novel contribution of this paper. The approach presented here can be applied to the construction of digital twins for any well construction operation.