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Improper hole cleaning is a major cause of non-productive time (NPT) in drilling. Current hole cleaning practices are mostly based on experience, rules of thumb and simplistic calculations. Hence, they are not reliable and do not work as expected in all scenarios. There is the need for a robust, fast, and accurate approach to simulate cuttings transport, and provide reliable and useful estimations of the hole conditions in real-time. In this paper, a transient cuttings transport model is presented for real-time hole cleaning simulations. The model solves the transient conservation equations using a drift-flux modeling approach, which is applied to describe the multiphase flow of cuttings and fluid in the wellbore. The model uses experimentally derived equations that account for the effects of pump rate, pipe rotation, eccentricity, fluid rheology, inclination, etc. on cuttings transport. A fast numerical solver is used to enable real-time simulations, while providing numerical stability that is crucial to maintain the modeling convergence under area discontinuities. Using small time-steps, the model captures pressure wave behavior, which is necessary for simulating managed pressure drilling (MPD) operations. Pressure-dependent mud density, non-Newtonian viscosity, and cuttings slip velocity models are used to estimate downhole parameters such as pressure, cuttings concentration, bed height, drilling fluid and cuttings velocities, etc. The model can provide a drilling crew with accessible real-time simulation results on the rig for monitoring the hole cleaning operation and preventing problems from happening. Case studies are performed based on field experiments to analyze the effectiveness of the developed model on avoiding operational problems such as pack-off and stuck pipe. Results show that by monitoring the real-time cuttings concentration and bed height along the wellbore, the developed model can detect improper hole cleaning conditions and provide optimum drilling parameters to resolve problems, thereby minimizing NPT. The robust numerical scheme allows for simulations that are several times faster than the real-time operation on a standard desktop PC, providing the crew with enough time to take preventive actions. Clean-up cycles can also be simulated by the model to calculate and optimize the required parameters for optimum hole cleaning results. Required clean-up times are calculated for the field cases to ensure that cuttings are effectively removed from the wellbore before pulling out of hole and running casing. Moreover, MPD operations can be simulated using the model that consider the effects of cuttings concentration and bed blockage on the pressure profile. The developed model can provide valuable real-time information on downhole conditions to the drilling crew during the drilling process and give adequate time to the crew to take timely corrective action if necessary. Moreover, the model can simulate the planned drilling process and calculate optimum drilling parameters to avoid hole cleaning problems.

Deep closed-loop geothermal systems (DCLGS) are introduced as an alternative to traditional enhanced geothermal systems (EGS) for green energy production that is globally scalable and dispatchable. Recent modeling work shows that DCLGS can generate an amount of power that is similar to EGS, while overcoming many of the downsides of EGS (such as induced seismicity, emissions to air, mineral scaling etc.). DCLGS wells can be constructed by leveraging and extending oil and gas extended reach drilling (ERD) and high-pressure high-temperature (HPHT) drilling expertise in particular. The objectives of this paper are two-fold. First, we demonstrate that DCLGS wells can generate power / electricity on a scale that is comparable to EGS, i.e. on the order of 40-55 MW per well. To this extent, we have developed a coupled hydraulic-thermal model, validated using oil and gas well cases, that can simulate various DCLGS well configurations. Secondly, we highlight the technology gaps and needs that still exist for economically drilling DCLGS wells, showing that it is possible to extend oil and gas technology, expertise and experience in ERD and HPHT drilling to construct complex DCLGS wells. Our coupled hydraulic-thermal sensitivity analyses show that there are key well drilling and design parameters that will ultimately affect DCLGS operating efficiency, including strategic deployment of managed pressure drilling / operation (MPD/MPO) technology, the use of vacuum-insulated tubing (VIT), and the selection of the completion in the high-temperature rock zones. Results show that optimum design and execution can boost initial geothermal power generation to 50 MW and beyond. In addition, historical ERD and HPHT well experience is reviewed to establish the current state-of-the-art in complex well construction and highlight what specific technology developments require attention and investment to make DCLGS a reality in the near-future (with a time horizon of ∼10 years). A main conclusion is that DCLGS is a realistic and viable alternative to EGS, with effective mitigation of many of the (potentially show-stopping) downsides of EGS. Oil and gas companies are currently highly interested in green, sustainable energy to meet their environmental goals. DCLGS well construction allows them to actively develop a sustainable energy field in which they already have extensive domain expertise. DCLGS offers oil and gas companies a new direction for profitable business development while meeting environmental goals, and at the same time enables workforce retention, retraining and re-deployment using the highly transferable skills of oil and gas workers.

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.

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