Advanced fluid dynamics in well construction with application of hybrid physics-based and data-driven modeling

Modeling of the physical systems is essential to capture action and response relationships during well construction processes, such as while drilling for geothermal, oil and gas wells. Accurate fluid dynamics models are required especially to fully automate the well construction process, which will significantly improve the safety, security and consistency of the operations. However, it is difficult to achieve robust and accurate modeling of any inherently complex drilling process. Recent publications across various domains showed that data-driven models have the potential to capture complex relationships successfully. However, purely data-driven approaches come with characteristic disadvantages. For instance, they lack interpretability and struggle to properly capture causal relationships. This dissertation focuses on three major fluid dynamics research topics in well construction: cuttings transport, non-Newtonian fluid flow in drilling circulation systems and thixotropic fluid behavior. Specifically, physics-based modeling of each topic is investigated and then several hybrid modeling applications are attempted through combining physics-based and data-driven models via rule-based stochastic decision-making with the goal of mitigating the disadvantages of using the two types of approaches standalone. Furthermore, the last chapter demonstrates how better fluid dynamics modeling will lead to more robust control of rig actuators. This dissertation aims to advance fluid dynamics research and open the way for further hybrid modeling applications in well construction.Mechanical Engineerin

Well Construction Action Planning and Automation through Finite-Horizon Sequential Decision-Making

Well construction operations require continuous complex decision-making and multi-step action planning. Action selection at every step demands a careful evaluation of the vast action space, while guided by long-term objectives and desired outcomes. Current human-centric decision-making introduces a degree of bias, which can result in reactive rather than proactive decisions. This can lead from minor operational inefficiencies all the way to catastrophic health and safety issues. This paper details the steps in structuring unbiased purpose-built sequential decision-making systems. Setting up such systems entails representing the operation as a Markov decision process (MDP). This requires explicitly defining states and action values, defining goal states, building a digital twin to model the process, and appropriately shaping reward functions to measure feedback. The digital twin, in conjunction with the reward function, is utilized for simulating and quantifying the different action sequences. A finite-horizon sequential decision-making system, with discrete state and action space, was set up to advise on hole cleaning during well construction. The state was quantified by the cuttings bed height and the equivalent circulation density values, and the action set was defined using a combination of controllable drilling parameters (including mud density and rheology, drillstring rotation speed, etc.). A non-sparse normalized reward structure was formulated as a function of the state and action values. Hydraulics, cuttings transport, and rig state detection models were integrated to build the hole cleaning digital twin. This system was then used for performance tracking and scenario simulations (with each scenario defined as a finite-horizon action sequence) on real-world oil wells. The different scenarios were compared by monitoring state–action transitions and the evolution of the reward with actions. This paper presents a novel method for setting up well construction operations as long-term finite-horizon sequential decision-making systems, and defines a way to quantify and compare different scenarios. The proper construction of such systems is a crucial step towards automating intelligent decision-making

Predicting the pressure losses while the drillstring is buckled and rotating using artificial intelligence methods

The prediction of equivalent circulating density in realistic conditions is complex due to many parameters in effect. Drillstring configuration and motion can play a significant role on the pressure profile in the annulus. Eccentricity, rotation and axial position of the drillstring can cause distinct pressure losses. If an accurate prediction is desired, these effects need to be accounted for. In this study, the pressure losses of Yield Power Law fluids with various drillstring rotation speeds and configurations are analyzed. These configurations include eccentricity and various buckling configurations and rotation speeds of the drillstring. Neural networks are used to predict the pressure losses and the results are compared with the experimental results and existing models from the literature. The input to the neural networks is optimized by comparing using direct measurements and using dimensionless parameters derived from the measurements. The comparison shows that using direct measurements as input yield better results instead of using dimensionless parameters, considering the experimental data used in this study. The results of this study showed that using neural networks to predict the pressure losses in complex geometries and motion showed a better precision compared to the existing models from the literature. The results analysis show that predicting with neural networks can yield as low as 5% absolute average percent error while predicting using existing models can yield as high as 115% absolute average percent error. Using neural networks shows a strong potential to accurately predict the pressure losses especially considering complex fluids and geometries

Well Construction Action Planning and Automation through Finite-Horizon Sequential Decision-Making

Well construction operations require continuous complex decision-making and multi-step action planning. Action selection at every step demands a careful evaluation of the vast action space, while guided by long-term objectives and desired outcomes. Current human-centric decision-making introduces a degree of bias, which can result in reactive rather than proactive decisions. This can lead from minor operational inefficiencies all the way to catastrophic health and safety issues. This paper details the steps in structuring unbiased purpose-built sequential decision-making systems. Setting up such systems entails representing the operation as a Markov decision process (MDP). This requires explicitly defining states and action values, defining goal states, building a digital twin to model the process, and appropriately shaping reward functions to measure feedback. The digital twin, in conjunction with the reward function, is utilized for simulating and quantifying the different action sequences. A finite-horizon sequential decision-making system, with discrete state and action space, was set up to advise on hole cleaning during well construction. The state was quantified by the cuttings bed height and the equivalent circulation density values, and the action set was defined using a combination of controllable drilling parameters (including mud density and rheology, drillstring rotation speed, etc.). A non-sparse normalized reward structure was formulated as a function of the state and action values. Hydraulics, cuttings transport, and rig state detection models were integrated to build the hole cleaning digital twin. This system was then used for performance tracking and scenario simulations (with each scenario defined as a finite-horizon action sequence) on real-world oil wells. The different scenarios were compared by monitoring state–action transitions and the evolution of the reward with actions. This paper presents a novel method for setting up well construction operations as long-term finite-horizon sequential decision-making systems, and defines a way to quantify and compare different scenarios. The proper construction of such systems is a crucial step towards automating intelligent decision-making