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The world’s energy demand has been rapidly increasing and is projected to continue growing for at least the next two decades. With increasing global energy demand and competition from renewable energy, the oil and gas industry is striving for more efficient petroleum production. Many technical breakthroughs have enabled the drilling industry to expand the exploration to more difficult drilling such as deepwater drilling and multilateral directional drilling. For example, managed pressure drilling (MPD) offers ceaseless operation with multiple manipulated variables (MV) and wired drill pipe (WDP) provides two-way, high-speed measurements from bottom hole and along-string sensors. These technologies have maximum benefit when applied in an automation system or as a real-time advisory tool. The objective of this study is to investigate the benefit of nonlinear model-based control and estimation algorithms with various types of models. This work presents a new simplified flow model (SFM) for bottomhole pressure (BHP) regulation in MPD operations. The SFM is embedded into model-based control and estimation algorithms that use model predictive control (MPC) and moving horizon estimation (MHE), respectively. This work also presents a new Hammerstein-Wiener nonlinear model predictive controller for BHP regulation. Hammerstein-Wiener models employ input and output static nonlinear blocks before and after linear dynamics blocks to simplify the controller design. The control performance of the new Hammerstein-Wiener nonlinear controller is superior to conventional PID controllers in a variety of drilling scenarios. Conventional controllers show severe limitations in MPD because of the interconnected multivariable and nonlinear nature of drilling operations. BHP control performance is evaluated in scenarios such as drilling, pipe connection, kick attenuation, and mud density displacement and the efficacy of the SFM and Hammerstein-Wiener models is tested in various control schemes applicable to both WDP and mud pulse systems. Trusted high-fidelity drilling simulators are used to simulate well conditions and are used to evaluate the performance of the controllers using the SFM and Hammerstein-Wiener models. The comparison between non- WDP (semi-closed loop) and WDP (full-closed loop) applications validates the accuracy of the SFM under the set of conditions tested and confirms comparability with model-based control and estimation algorithms. The SFM MPC maintains the BHP within 1 bar of the setpoint for each investigated scenario, including for pipe connection and mud density displacement procedures that experience a wider operation range than normal drilling.
Increasing worldwide demand for petroleum motivates greater efficiency, safety, and environmental responsibility in upstream oil and gas processes. The objective of this research is to improve these areas with advanced control methods. This work develops the integration of optimal control methods including model predictive control, moving horizon estimation, high fidelity simulators, and switched control techniques applied to subsea riser slugging and managed pressure drilling. A subsea riser slugging model predictive controller eliminates persistent offset and decreases settling time by 5% compared to a traditional PID controller. A sensitivity analysis shows the effect of riser base pressure sensor location on controller response. A review of current crude oil pipeline wax deposition prevention, monitoring, and remediation techniques is given. Also, industrially relevant control model parameter estimation techniques are reviewed and heuristics are developed for gain and time constant estimates for single input/single output systems. The analysis indicates that overestimated controller gain and underestimated controller time constant leads to better controller performance under model parameter uncertainty. An online method for giving statistical significance to control model parameter estimates is presented. Additionally, basic and advanced switched model predictive control schemes are presented. Both algorithms use control models of varying fidelity: a high fidelity process model, a reduced order nonlinear model, and a linear empirical model. The basic switched structure introduces a method for bumpless switching between control models in a predetermined switching order. The advanced switched controller builds on the basic controller; however, instead of a predetermined switching sequence, the advanced algorithm uses the linear empirical controller when possible. When controller performance becomes unacceptable, the algorithm implements the low order model to control the process while the high fidelity model generates simulated data which is used to estimate the empirical model parameters. Once this online model identification process is complete, the controller reinstates the empirical model to control the process. This control framework allows the more accurate, yet computationally expensive, predictive capabilities of the high fidelity simulator to be incorporated into the locally accurate linear empirical model while still maintaining convergence guarantees.
If done properly, MPD can improve economics for any well being drilled by reducing a rig's nonproductive time. Written for engineers, drilling managers, design departments, and operations personnel, Managed Pressure Drilling Modeling is based on the author's on experience and offers instruction on planning, designing and executing MPD projects. Compact and readable, the book provides a step by step methods for understanding and solve problems involving variables such as backpressure, variable fluid density, fluid rheology, circulating friction, hole geometry and drillstring diameter. All MPD variations are covered, including Constant Bottomhole Pressure, Pressurized MudCap Drilling and Dual Gradient Drilling. Case histories from actual projects are designed and analyzed using proprietary simulation software online.
Automation of managed pressure drilling (MPD) enhances the safety and increases efficiency of drilling and that drives the development of controllers and observers for MPD. The objective is to maintain the bottom hole pressure (BHP) within the pressure window formed by the reservoir pressure and fracture pressure and also to reject kicks. Practical MPD automation solutions must address the nonlinearities and uncertainties caused by the variations in mud flow rate, choke opening, friction factor, mud density, etc. It is also desired that if pressure constraints are violated the controller must take appropriate actions to reject the ensuing kick. The objectives are addressed by developing two controllers: a gain switching robust controller and a nonlinear model predictive controller (NMPC). The robust gain switching controller is designed using H1 loop shaping technique, which was implemented using high gain bumpless transfer and 2D look up table. Six candidate controllers were designed in such a way they preserve robustness and performance for different choke openings and flow rates. It is demonstrated that uniform performance is maintained under different operating conditions and the controllers are able to reject kicks using pressure control and maintain BHP during drill pipe extension. The NMPC was designed to regulate the BHP and contain the outlet flow rate within certain tunable threshold. The important feature of that controller is that it can reject kicks without requiring any switching and thus there is no scope for shattering due to switching between pressure and flow control. That is achieved by exploiting the constraint handling capability of NMPC. Active set method was used for computing control inputs. It is demonstrated that NMPC is able to contain kicks and maintain BHP during drill pipe extension.
Drilling in challenging conditions require precise control over hydrodynamic parameters for safer and efficient operation in oil and gas industries. Automated managed pressure drilling (MPD) is one of such drilling solution which helps to maintain operational parameters effectively over conventional drilling technique. The main goal is to maintain bottomhole pressure between reservoir formation pressure and fracture pressure with kick mitigation ability. Real life MPD system has to confront nonlinearity induced by drilling fluid rheology and flow parameters. To obtain a better understanding of this operation, a lab scale experimental setup has been developed. Reynolds number and pressure drop per unit length were considered to obtain hydrodynamic similarity. A vertical concentric pipe arrangement has been used to represent the drill string and annular casing region. A linearized gain switching proportional integral (PI) controller and a nonlinear model predictive controller (NMPC) have been developed to automate the control operation in the experimental setup. A linearizer has been designed to address the choke nonlinearity. Based on the flow and pressure criteria, a gain switching PI controller has been developed which is able to control pressure and flow conditions during pipe extension, pump failure and influx attenuation cases. On the other hand, a nonlinear Hammerstein-Weiner model has been developed which assists in bottomhole pressure estimation using pump flow rate and choke opening. The identified model has been integrated with a NMPC algorithm to achieve effective control within predefined pressure and flow constraints. Lastly, a performance comparison has been provided between the linearized gain switching PI controller and NMPC controller.
The upstream oil and gas industry has witnessed a marked increase in the number of wells drilled in areas with elevated subsurface formation pressures and narrow drilling margins. Managed Pressure Drilling (MPD) techniques have been developed to deal with the challenge of narrow margin wells, offering great promise for improved rig safety and reduced non-productive time. Automation of MPD operations can ensure improved control over wellbore pressure profiles, and there are several commercial solutions currently available. However, these automation efforts seldom take into account the uncertainty and complex dynamics inherent in subsurface environments, and usually assume ideally functioning sensors and actuators, which is rarely the case in real-world drilling operations. This dissertation describes a set of tools and methods that can form the basis for an automation framework for MPD systems, with specific focus on the surface back-pressure technique of MPD. Model-based control algorithms with robust reference tracking, as well as methods for detecting system faults and handling modeling uncertainty, are integrated with a novel multi-phase hydraulics model. The control system and event detection modules are designed using physics-based representations of the drilling processes, as well as models relating uncertain variables in a probabilistic fashion. Validation on high-fidelity simulation models is conducted in order to ascertain the effectiveness of the developed methods.
Managed pressure drilling (MPD) is a technique utilized in drilling to manage annular pressure, hold reservoir influx, and divert mud returns away safely from the rig floor through a closed loop system. Thus, MPD plays key roles in well control operations and in drilling deepwater wells. However, despite the operational, safety, and economic benefits, limited information is available on understanding the complexity of MPD system. Furthermore, the oil and gas industry currently relies on a flow monitoring system for earlier kick detection but faces severe flaws and limited progress has been made on approach that monitors kick from downhole due to the complexity of offshore drilling operations. Thus, the main objective of this research is to assess the safety and reliability of MPD. In this research, following novel contributions have been made: several dynamic downhole drilling parameters have been identified to enhance earlier kick detection technique during drilling, including about 33 - 89% damping of bit-rock vibrations due to gas kick; a reliability assessment model has been developed to estimate the failure probability of an MPD system as 5.74%, the assess the increase in reliability of kick control operation increases from 94% to 97% due to structural modification of the MPD components, identify that MPD operational failure modes are non-sequential, and identify that an MPD control system is the most safety-critical components in an MPD system; an automated MPD control model, which implements a nonlinear model predictive controller (NMPC) and a two-phase hydraulic flow model, has been developed to perform numerical simulations of an MPD operation; and lastly, an integrated dynamic blowout risk model (DBRM) to assess the safety during an MPD operation has been developed and its operation involves three key steps: a dynamic Bayesian network (DBN) model, a numerical simulation of an MPD control operation, and dynamic risk analysis to assess the safety of the well control operation as drilling conditions change over time. The DBRM also implemented novel kick control variables to assess the success / failure of an MPD operation, i.e. its safety, and are instrumental in providing useful information to predict the performance of / diagnose the failure of an MPD operation and has been successfully applied to replicate the dynamic risk of blowout risk scenarios presented in an MPD operation at the Amberjack field case study from the Gulf of Mexico.
With extraction out of depleted wells more important than ever, this new and developing technology is literally changing drilling engineering for future generations. Never before published in book form, these cutting-edge technologies and the processes that surround them are explained in easy-tounderstand language, complete with worked examples, problems and solutions. This volume is invaluable as a textbook for both the engineering student and the veteran engineer who needs to keep up with changing technology.
Managed Pressure Drilling Operations is a significant technology worldwide and beginning to make an impact all over the world. Often reservoir and drilling engineers are faced with the decision on how best to construct a well to exploit zones of interest while seeking to avoid drilling problems that contribute to reservoir damage or cause loss of hole. The decision to pursue a MPD operation is based on the intent of applying the most appropriate technology for the candidate and entails either an acceptance of influx to the surface or avoidance of influx into the wellbore. In today's exploration and production environment, drillers must now drill deeper, faster and into increasingly harsher environments where using conventional methods could be counter-productive at best and impossible at worst. Managed Pressure Drilling (MPD) is rapidly gaining popularity as a way to mitigate risks and costs associated with drilling in harsh environments. With this book in hand drilling professionals gain knowledge of the various variations involved in managed pressure drilling operations; understand the safety and operational aspects of a managed pressure drilling project; and be able to make an informed selection of all equipment required to carry out a managed pressure drilling operation.