(544h) Addressing Control Challenges of Discontinuous Processes with Multi-Fidelity Model Predictive Control

Beal, L., Brigham Young University
Hedengren, J. D., Brigham Young University
Petroleum currently
fulfills 32% of total national energy demand, more than any other source of
energy, and is likely to continue to do so for at least the next 30 years[1]. While the need for alternative sources of energy is
clear, the growth and maturity of renewable energies has been slow and unable
to meet energy demands, especially in transportation. Therefore, to meet the
growing demand for energy, more efficient, robust, and reliable technological
advances are needed in the petroleum industry. The petroleum industry is
historically divided into upstream and downstream divisions. The upstream
division finds and extracts oil and gas from geologic formations, while the downstream
division refines the crude oil and gas into usable products. The downstream
sector has seen many technological advances in process control and
optimization, but many processes in the upstream sector, such as oil well
drilling, still lack any significant automation[2]. When automation is optimized, it
improves safety and efficiency over manual control by responding faster to
process disturbances and by operating closer to process constraints. However, a
unique challenge of automating Managed Pressure Drilling (MPD) for oil and gas is
the discontinuous nature of the process[2]. An oil or gas well is created by
drilling into the earth for several hundred to several thousand feet, stopping
to insert and cement in place a segment of casing pipe to the well bore, then
repeating the process until the target depth is reached. Before the casing pipe
is inserted into the well bore, the string of drill pipe must be removed from
the well, set aside, and then placed back in the well to resume operations. As
the well deepens, more drill pipe is connected to the drillstring. At the
bottom of the drillstring, a Bottom Hole Assembly (BHA), consisting of
measurement and steering equipment, is attached to the drill bit. The drill bit
is cooled by the drilling fluid, or mud, which also moves the rock cuttings to
the surface and maintains pressure in the well annulus (see Figure 1). The well
annulus pressure consistently needs to be greater than the geologic reservoir
pressure to prevent hydrocarbons from entering the well during the drilling
process. If the mud pressure in the well is too high, it can damage the rock
formation; if it is too low, hydrocarbons from the subsurface reservoir can
come to the surface in an uncontrolled and dangerous manner. When this
catastrophe happens it is known as a blowout. The well bore pressure must be
maintained within a small range of pressures that balances the reservoir fluid
pressure to prevent damaged formations and blowouts. Maintaining the pressure
balance in the well is the goal of MPD[3].


1 A simplified diagram of the MPD process


Currently, industrial MPD
is completely manual. However, it has been demonstrated that an automated
controller can maintain borehole pressure and reject disturbances faster and
more accurately than manual control by using Nonlinear Model Predictive Control
(NMPC)[4]. A controller
can coordinate the mud pump flow rate and the annulus choke valve opening to
reach the desired bit pressure set point[5]. One of the challenges with automated
control of MPD is the necessary stopping and starting of operations. This
discontinuous process can cause pressure sensors in the drillstring and BHA to
lose calibration and controller models to lose tuning in the intervals.

To address these
challenges, this work simulates multiple control models arranged in an ensemble
control structure to maintain control over various process stages: normal
drilling operations, pipe reconnection procedures, and the disturbance of
unwanted gas influx from the reservoir to the well bore. The novel control
structure allows switching among control models of varying accuracy. A high
fidelity model of the process is used when available, but the long computation
time makes it unavailable for every control instance. The faster and less
accurate low-order first principles model is the primary controller, yet it may
not converge to an optimal solution at every time step due to the use of dynamic
parameter estimation using Moving Horizon Estimation (MHE). An empirical First
Order Plus Dead Time (FOPDT) model generally converges to an optimal solution
at each time step, yet it is the least accurate model available. The controller
makes use of these multi-fidelity models based on the availability and accuracy
of the past model predictions compared to the current process state. The model
solution with the least error is used to control the process at the present
control instance. A limitation on the rate of change of process inputs is set
to avoid sudden jumps when switching between models. An additional benefit of
this control structure is the ability to tune one model while another is being
used to control the process. Thus, model tuning can be accomplished without
interrupting the drilling process[6]. Hence, the redundant model control
structure offers benefits typically associated with redundant process
measurements. This novel ensemble controller is capable of maintaining control
over the discontinuous MPD process.         



1.            Annual Energy Outlook 2014, U. S. Energy
Information Administration. p. A-1.

2.            Godhavn,
J.M., et al., Drilling Seeking Automation Control Solutions, in 18th
IFAC World Congress
. 2011: Milano, Italy.

3.            Breyholtz,
O., G. Nygaard, and M. Nikolaou. Automatic control of managed pressure
. in American Control Conference (ACC), 2010. 2010.

4.            Asgharzadeh
Shishavan, R., et al., Combined Rate of Penetration and Pressure Regulation
for Drilling Optimization by Use of High-Speed Telemetry
SPE Drilling and
Completion Journal, 2015. 30(1).

5.            Breyholtz,
O., et al. Evaluating control designs for co-ordinating pump rates and choke
valve during managed pressure drilling operations
. in Control
Applications, (CCA) & Intelligent Control, (ISIC), 2009 IEEE
. 2009.

6.            Sui, D., et al., Ensemble Methods for Process
Monitoring in Oil and Gas Industry Operations.
Journal Of Natural Gas
Science and Engineering, 2011. 3: p. 748-753.