Managed Pressure Drilling

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.

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, MPD, one of the advanced techniques in the Drilling and Petroleum Industry, currently at the top of the drilling technology evolution pyramid, is a collection of tested drilling techniques and variations. MPD, when properly engineered and executed, in most of the scenarios, can help mitigate several drilling issues, and improve personal and environmental safety. However, not all wells with drilling issues and/or problems need MPD. Proper screening of potential candidates with utmost diligence is required prior to electing to use MPD. Any technology, let alone MPD, new or old, simple or complex, expensive or cheap, should not be applied without thorough understanding and careful evaluation of the entire process. Technology used for wrong reasons, with incomplete comprehension, and half-hearted assessment and analysis, is bound to give wrong, sometimes even catastrophic, results. This work, a research submitted to Texas A&M in 2009, freely downloadable from www.tamu.edu, is an attempt to provide basic steps in the candidate selection and evaluation while screening for MPD applications. Basic understanding of Drilling and Petroleum Engineering will help.

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.