This paper presents a convex optimization approach to control the density distribution of autonomous mobile agents with two control modes: ON and OFF. The main new characteristic distinguishing this model from standard Markov decision models is the existence of the ON control mode and its observed actions. When an agent is in the ON mode, it can measure the instantaneous outcome of one of the actions corresponding to the ON mode and decides whether it should take this action or not based on this new observation. If it does not take this action, it switches to the OFF mode where it transitions to the next state based on a predetermined set of transitional probabilities, without making any additional observations. In this decisionmaking model, each agent acts autonomously according to an ON/OFF decision policy, and the discrete probability distribution for the agent's state evolves according to a discrete-time Markov chain that is a linear function of the stochastic environment (i.e., transition probabilities) and the ON/OFF decision policy. The relevant policy synthesis is formulated as a convex optimization problem where hard safety and convergence constraints are imposed on the resulting Markov matrix. We first consider the case where the ON mode has a single action and the OFF mode has deterministic transitions (rather than stochastic) to demonstrate the model and the ideas behind our approach, which is then generalized to the case where ON mode has multiple actions and OFF mode has stochastic transitions.
The challenging offshore and shale production environments have increased the need for cost-effective, standardized drilling operations while providing accurate well placement and high borehole quality. Automation of directional drilling processes bears the promise of delivering these consistent and reliable performances while maximizing production potential. This paper introduces a steering advisory system for rotary steerable systems (RSS), which provides steering decisions automatically given the BHA configuration, bit selection, well plan and/or target(s), and real-time sensory information received from the RSS. These decisions can be either displayed to directional drillers or down-linked directly to the tool for autonomous directional drilling. The system has proven itself on multiple commercial jobs across North America with a new generation RSS. By exactly following the advisory system-generated steering decisions, multiple curve sections were smoothly drilled and accurately landed within tight tolerances.
Accessing difficult to reach hydrocarbon reservoirs while simultaneously reducing risk and increasing efficiency demonstrates a need for improved autonomous directional control of rotary steerable systems (RSS). The inherently uncertain drilling environment presents a challenge for control algorithms and human operators alike, where model mismatch can be significant and the parameters are time varying. Parameter estimation can improve the performance of steering controllers through model adaptation as well as provide valuable information to human operators. This paper proposes the use of a Markov Chain Monte Carlo (MCMC) based method to estimate time-varying model parameters in real-time using only measurements commonly obtained while drilling. The proposed method is evaluated on historical field data and the estimator's accuracy is quantified by prediction accuracy to achieve a mean absolute error of 0.68 degrees over 30 m. Next, the proposed method is used to adapt the model of a model predictive controller (MPC) and its performance is compared with a static MPC in a closed-loop simulation of a realistic drilling scenario. The results show the estimator improves tracking performance by 93.36%. Lastly, the utility of estimation for human-in-the-loop operation is explored through the design of an early warning system (EWS). The posterior distribution produced by MCMC is utilized in the EWS to predict the probability of undesirable future trajectories. By providing automatic alerts, the EWS serves as a safety mechanism and improves the operator's proficiency when monitoring several autonomously drilled wells.
Reliable toolface calculation is essential for achieving robust automatic steering control with rotary steerable systems (RSS). For RSS with fully rotating sensor packages, this task becomes particularly challenging under extreme conditions, where signal-to-noise ratio (SNR) of measurements from one or more sensors reduce significantly (e.g., while drilling near-vertical wells, along dip, towards magnetic north, in the vicinity of casing and/or under severe vibration and stick-slip). To ensure robust toolface control for fully rotating RSS under these conditions, this paper proposes a novel dynamic toolface calculation method.
The proposed dynamic toolface calculation method of the new-generation fully rotating RSS overcomes the challenge of achieving robust toolface control despite extreme drilling conditions, by bringing together real-time health monitoring, online sensor calibration and novel sensor fusion techniques. Considering that robust toolface control is the heart of any drilling automation architecture with RSS, this technology is key to enable advanced drilling control strategies in the future.
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