Closed-Loop Feedback Control in Intelligent Wells: Application to a Heterogeneous, Thin Oil-Rim Reservoir in the North Sea

2012 ◽  
Author(s):  
Fahad Ahmed Dilib ◽  
Matthew David Jackson ◽  
Ali Mojaddam Zadeh ◽  
Robert Aasheim ◽  
Kristine Årland ◽  
...  
Keyword(s):  
Feedback Control ◽  
North Sea ◽  
Closed Loop ◽  
The North ◽  
The North Sea ◽  
Loop Feedback ◽  
Closed Loop Feedback ◽  
Intelligent Wells ◽  
Oil Rim

Closed-Loop Feedback Control in Intelligent Wells: Application to a Heterogeneous, Thin Oil-Rim Reservoir in the North Sea

2015 ◽  
Vol 18 (01) ◽  
pp. 69-83 ◽  
Author(s):  
F.A.. A. Dilib ◽  
M.D.. D. Jackson ◽  
A. Mojaddam Zadeh ◽  
R.. Aasheim ◽  
K.. Årland ◽  
...  
Keyword(s):  
Feedback Control ◽  
Closed Loop ◽  
Control Strategies ◽  
Open Loop ◽  
Loop Control ◽  
Loop Feedback ◽  
Closed Loop Feedback ◽  
Intelligent Wells ◽  
Oil Rim ◽  
Direct Feedback

Summary Important challenges remain in the development of optimized control strategies for intelligent wells, particularly with respect to incorporating the impact of reservoir uncertainty. Most optimization methods are model-based and are effective only if the model or ensemble of models used in the optimization captures all possible reservoir behaviors at the individual-well and -completion level. This is rarely the case. Moreover, reservoir models are rarely predictive at the spatial and temporal scales required to identify control actions. We evaluate the benefit of the use of closed-loop control strategies, on the basis of direct feedback between reservoir monitoring and inflow-valve settings, within a geologically heterogeneous, thin oil-rim reservoir. This approach does not omit model predictions completely; rather, model predictions are used to optimize a number of adjustable parameters within a general direct feedback relationship between measured data and inflow-control settings. A high-resolution sector model is used to capture reservoir heterogeneity, which incorporates a locally refined horizontal grid in the oil zone, to accurately represent the horizontal-well geometry and fluid contacts, and capture water and gas flow. Two inflow-control strategies are tested. The first is an open-loop approach, using fixed inflow-control devices to balance the pressure drawdown along the well, sized before installation. The second is a closed-loop, feedback-control strategy, using variable inflow-control valves that can be controlled from the surface in response to multiphase-flow data obtained downhole. The closed-loop strategy is optimized with a base-case model, and then tested against unexpected reservoir behavior by adjusting a number of uncertain parameters in the model but not reoptimizing. We find that closed-loop feedback control yields positive gains in net-present value (NPV) for the majority of reservoir behaviors investigated, and higher gains than the open-loop strategy. Closed-loop control also can yield positive gains in NPV even when the reservoir does not behave as expected, and in tested scenarios returned a near optimal NPV. However, inflow control can be risky, because unpredicted reservoir behavior also leads to negative returns. Moreover, assessing the benefits of inflow control over an arbitrarily fixed well life can be misleading, because observed gains depend on when the calculation is made.

Closed-loop feedback control of microfluidic cell manipulation via deep-learning integrated sensor networks

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Ningquan Wang ◽  
Ruxiu Liu ◽  
Norh Asmare ◽  
Chia-Heng Chu ◽  
Ozgun Civelekoglu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sensor Networks ◽  
Feedback Control ◽  
Closed Loop ◽  
Microfluidic System ◽  
Cell Manipulation ◽  
Integrated Sensor ◽  
Loop Feedback ◽  
Closed Loop Feedback ◽  
On Chip

An adaptive microfluidic system changing its operational state in real-time based on cell measurements through an on-chip electrical sensor network.

Stability analysis in machining process by using adaptive closed-loop feedback control system in turning process

2020 ◽  
pp. 107754632095261
Author(s):  
Kashfull Orra ◽  
Sounak K Choudhury
Keyword(s):  
Control System ◽  
Feedback Control ◽  
Closed Loop ◽  
Machining Process ◽  
Feedback Control System ◽  
Turning Process ◽  
Tool Vibration ◽  
Loop Feedback ◽  
Closed Loop Feedback ◽  
The Stability

The study presents model-based mechanism of nonlinear cutting tool vibration in turning process and the strategy of improving cutting process stability by suppressing machine tool vibration. The approach used is based on the closed-loop feedback control system with the help of electro–magneto–rheological damper. A machine tool vibration signal generated by an accelerometer is fed back to the coil of a damper after suitable amplification. The damper, attached under the tool holder, generates counter forces to suppress the vibration after being excited by the signal in terms of current. The study also discusses the use of transfer function approach for the development of a mathematical model and adaptively controlling the process dynamics of the turning process. The purpose of developing such mechanism is to stabilize the machining process with respect to the dynamic uncut chip thickness responsible for the type-II regenerative effect. The state-space model used in this study successfully checked the adequacy of the model through controllability and observability matrices. The eigenvalue and eigenvector have confirmed the stability of the system more accurately. The characteristic of the stability lobe chart is discussed for the present model-based mechanism.

Closed-loop feedback control of product properties in flexible metal forming processes with mobile tools

CIRP Annals ◽  
2009 ◽  
Vol 58 (1) ◽  
pp. 287-290 ◽  
Author(s):  
Julian M. Allwood ◽  
Omer Music ◽  
Ankor Raithathna ◽  
Stephen R. Duncan
Keyword(s):  
Feedback Control ◽  
Metal Forming ◽  
Closed Loop ◽  
Loop Feedback ◽  
Closed Loop Feedback ◽  
Flexible Metal

scholarly journals Closed-loop feedback control of microbubble diameter from a flow-focusing microfluidic device

2020 ◽  
Vol 14 (3) ◽  
pp. 034101
Author(s):  
Yanjun Xie ◽  
Adam J. Dixon ◽  
J. M. Robert Rickel ◽  
Alexander L. Klibanov ◽  
John A. Hossack
Keyword(s):  
Feedback Control ◽  
Microfluidic Device ◽  
Closed Loop ◽  
Flow Focusing ◽  
Loop Feedback ◽  
Closed Loop Feedback
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