scholarly journals A Computationally Fast Iterative Dynamic Programming Method for Optimal Control of Loosely Coupled Dynamical Systems with Different Time Scales * *This work has been performed within the Combustion Engine Research Center at Chalmers (CERC) with financial support from the Swedish Energy Agency.

2017 ◽  
Vol 50 (1) ◽  
pp. 5953-5960 ◽  
Author(s):  
Jonathan Lock ◽  
Tomas McKelvey
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Dynamical Systems ◽  
Financial Support ◽  
Combustion Engine ◽  
Dynamic Programming Method ◽  
Energy Agency ◽  
Loosely Coupled ◽  
Iterative Dynamic Programming ◽  
Different Time Scales

Control of Down-Hole Drilling Process Using a Computationally Efficient Dynamic Programming Method

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Chong Ke ◽  
Xingyong Song
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Oil And Gas ◽  
Dynamic Programming Method ◽  
Hole Drilling ◽  
Drilling Process ◽  
Computationally Efficient ◽  
Dynamics Model ◽  
Control Optimization ◽  
Drilling System

The unconventional down-hole resources such as shale oil and gas have gradually become a critical form of energy supply thanks to the recent petroleum technology advancement. Its economically viable and reliable production highly depends on the proper operation and control of the down-hole drilling system. The trend of deeper drilling in a complex environment requires a more effective and reliable control optimization scheme, either for predrilling planning or for online optimal control. Given the nonlinear nature of the drilling system, such an optimal control is not trivial. In this paper, we present a method based on dynamic programming (DP) that can lead to a computationally efficient drilling control optimization. A drilling dynamics model that can enable this method is first constructed, and the DP algorithm is customized so that much improved computational efficiency can be achieved compared with using standard DP. A higher-order dynamics model is then used to validate the effectiveness of the optimized control, and the control robustness is also evaluated by adding perturbations to the model. The results verify that the proposed approach is effective and efficient to solve the down-hole drilling control optimization problem.

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