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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.
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.
The influx of reservoir fluid (kick) has a significant impact on drilling operations. Unmitigated kick can lead to a blowout causing financial losses and impacting human lives on the rig. Kick is an unmeasured disturbance in the system, and so detection, estimation, and mitigation are essential for the safety and efficiency of the drilling operation. Our main objective is to develop a real time warning system for a managed pressure drilling (MPD) system. In the first part of the research, an unscented Kalman filter (UKF) based estimator was implemented to simultaneously estimate the bit flow-rate, and kick. The estimated kick is further used to predict the impact of the kick. Optimal control theory is used to calculate the time to mitigate the kick in the best case scenario. An alarm system is developed based on total predicted influx and pressure rise in the system and compared with actual well operation control matrix. Thus, the proposed method can estimate, monitor, and manage kick in real time, enhancing the safety and efficiency of the MPD operation. So, a robust warning framework for the operators based on real life operational conditions is created in the second part of the research. Proposed frameworks are successfully validated by applying to several case studies.
Managed Pressure Drilling (MPD) is a modified drilling process designed to accurately manage the annular pressures of the wellbore with a combination of surface back pressure (SBP), flow rate and drilling mud density. MPD is designed to identify and circulate out kicks (uncontrolled flow of formation fluids into the wellbore) that possess blowout risk. Since MPD allows fast changes in the bottom hole pressure, the reaction to an undesirable inflow is not automatically to shut in the well and increase the density of the mud which is the well control procedure in conventional drilling. This feature of MPD can aid in lowering the non-productive time (NPT) as well as drilling related problems such as stuck pipe that results from well control operations. The objective of this research is to compare and evaluate the current conventional and MPD well control methods for gas kicks in a deepwater well. A total of seven well control methods are chosen from literature and subjected to an effectiveness criterion such as total time to control and circulate the kick, maximum pressures imposed on surface equipment and whether each well control method resulted in fracturing the formation at the casing shoe. Two different kick volumes (2 bbl and 10 bbl) with two different values of formation permeability (10 mD and 500 mD) and three different kick intensities (0.1 ppge, 0.5 ppge, 1.0 ppge) are chosen for kick scenarios in this study. The kick scenarios are selected to resemble realistic events that occur during drilling operations. In this study, only kicks resulting from drilling into a higher-pressure zone than expected are subjected to analysis. DrillbenchTM Dynamic Well Control simulator, is used in this research. A model validation, in terms of drilling hydraulics and rising gas velocities in the annuli, is included in this study to verify the reliability of the software's output. The validation is carried out using analytical relations and experimental correlations obtained from literature. The Bingham Plastic fluid model is used in the drilling hydraulics validation and the correlations suggested by Caetano (1985) and Hassan and Kabir (1992) are used for the verification of rising gas velocity in the annular space. In general, validation results show a reasonable agreement with the software's output. The results of 84 simulations showed that in the scenarios with low permeability, with both kick volumes and with the three different kick intensities, MPD well control methods were able to reduce the total well control time by more than 50% compared to the driller's method. ii Furthermore, in the same scenarios with high permeability, the MPD methods outperformed the driller's method in terms of time to stop the kick as well as maximum pressures imposed on surface equipment. The MPD well control method that combines Qin-SBP increments shows to have better results in terms of the total time to control and circulate the kick as well as lower imposed pressures in the casing shoe and surface equipment. This study can be used as a basis for further research in well control with MPD, with more attention to the method of combining Qin-SBP to stop the kick. Finally, this research can be used as a guide to determine the best combination of well control practices for wells with similar characteristics of the well studied in this thesis.
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.
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.
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.
Worldwide oil and gas development has shifted from conventional reservoirs to unconventional and deepwater reservoirs, characterized by high pressure, high temperature, ultra-low permeability, and extensively developed natural fractures. This transition to increasingly hostile environments introduces new challenges in well drilling and completion, such as downhole drilling issues, formation damage, and reduced productivity. Aiming to solve the challenges, drilling and completion technologies have excelled greatly in the past two decades. This book covers managed pressure drilling (MPD), the role of artificial intelligence (AI) in refining drilling processes, and the transformative effects of digitalization and automation. Emphasizing efficiency, safety, and environmental responsibility, the book also integrates methods like casing while drilling for improved efficiency, advanced diagnostics for rig safety, stabilization techniques for wellbores in fractured reservoirs, cement sheath integrity maintenance, and the optimization of continuous gas lift. Bridging theoretical concepts with practical applications, the narrative offers insights into both operational techniques and safety strategies, drawing from past experiences. The current state-of-the-art theories, technologies, and practices are covered, bridging the gaps between fundamental theories and engineering applications.
Subsea development and production of hydrocarbons is challenging due to remote and harsh conditions. Recent technology development with high speed communication to subsea and downhole equipment has created a new opportunity to both monitor and control abnormal or undesirable events with a proactive and preventative approach rather than a reactive approach. Two specific technology developments are high speed, long-distance fiber optic sensing for production and completion systems and wired pipe for drilling communications. Both of these communication systems offer unprecedented high speed and accurate sensing of equipment and processes that are susceptible to uncontrolled well situations, leaks, issues with flow assurance, structural integrity, and platform stability, as well as other critical monitoring and control issues. The scope of this dissertation is to design monitoring and control systems with new theoretical developments and practical applications. For estimators, a novel `1-norm method is proposed that is less sensitive to data with outliers, noise, and drift in recovering the true value of unmeasured parameters. For controllers, a similar `1-norm strategy is used to design optimal control strategies that utilize a comprehensive design with multivariate control and nonlinear dynamic optimization. A framework for solving large scale dynamic optimization problems with differential and algebraic equations is detailed for estimation and control. A first area of application is in fiber optic sensing and automation for subsea equipment. A post-installable fiber optic clamp is used to transmit structural information for a tension leg platform. A proposed controller automatically performs ballast operations that both stabilize the floating structure and minimize fatigue damage to the tendons that hold the structure in place. A second area of application is with managed pressure drilling with moving horizon estimation and nonlinear model predictive control. The purpose of this application is to maximize rate of drilling penetration, maintain pressure in the borehole, respond to unexpected gas influx, detect cuttings loading and pack-off, and better manage abnormal events with the drilling process through automation. The benefit of high speed data accessibility is quantified as well as the potential benefit from a combined control strategy versus separate controllers.