Managed Pressure Drilling Candidate Selection

Managed Pressure Drilling now at the pinnacle of the 'Oil Well Drilling' evolution tree, has itself been coined in 2003. It is an umbrella term for a few new drilling techniques and some preexisting drilling techniques, all of them aiming to solve several drilling problems, including non-productive time and/or drilling flat time issues. These techniques, now sub-classifications of Managed Pressure Drilling, are referred to as 'Variations' and 'Methods' of Managed Pressure Drilling. Although using Managed Pressure Drilling for drilling wells has several benefits, not all wells that seem a potential candidate for Managed Pressure Drilling, need Managed Pressure Drilling. The drilling industry has numerous simulators and software models to perform drilling hydraulics calculations and simulations. Most of them are designed for conventional well hydraulics, while some can perform Underbalanced Drilling calculations, and a select few can perform Managed Pressure Drilling calculations. Most of the few available Managed Pressure Drilling models are modified Underbalanced Drilling versions that fit Managed Pressure Drilling needs. However, none of them focus on Managed Pressure Drilling and its candidate selection alone. An 'Managed Pressure Drilling Candidate Selection Model and software' that can act as a preliminary screen to determine the utility of Managed Pressure Drilling for potential candidate wells are developed as a part of this research dissertation. The model and a flow diagram identify the key steps in candidate selection. The software performs the basic hydraulic calculations and provides useful results in the form of tables, plots and graphs that would help in making better engineering decisions. An additional Managed Pressure Drilling worldwide wells database with basic information on a few Managed Pressure Drilling projects has also been compiled that can act as a basic guide on the Managed Pressure Drilling variation and project frequencies and aid in Managed Pressure Drilling candidate selection

APPLICATION OF REINFORCEMENT LEARNING IN MANAGED PRESSURE DRILLING

Automation in any industry has a control system as its base, and control systems are composed of a controller. In recent years an area of machine learning known as reinforcement learning (RL) has been focused on solving control problems for engineers and scientists. RL methods are actively applied to design control mechanisms for various industrial applications and in this study, the focus will be on designing such algorithms and modeling the given control problem into a structure where these RL algorithms can be applied. In the oil and gas industry, there has been a push to expand operations into areas where usual drilling methods are not successful mainly because of narrow operational windows, and technologies such as Managed Pressure Drilling (MPD) are found to be very successful in solving this issue. MPD is a control technique that is aimed at controlling the bottom hole pressure between narrow operational windows. The standard technique used for automating MPD is a proportional-integral-derivative (PID) controller, but many other non-linear control systems have also been employed to do the same task. This study seeks to add value to the drilling process by developing an Reinforcement Learning (RL) based agent to tune the PID controller. After tuning the PID controller, the system dynamics will be optimized and kept under boundary conditions of the drilling environment. The goal is to provide a reference bottom hole pressure set point and tuning parameters to the PID controller so that the optimum pressure can be reached safely at a certain depth. During the study, the most important features were the depth of the drilling bit, the fracture pressure, and pore pressure at that depth. The RL agent first proposes a suitable reference bottom hole pressure based on the fracture and pore pressure and then tunes the PID controller to achieve the desired pressure set point. The task of training this RL agent is handled in a specialized simulator environment which can calculate the bottom hole pressure at every simulation step and give feedback to the agent about the status. The agent uses a policy gradient method called Proximal Policy Optimization (PPO) and then later on Multi-armed bandit algorithms. PPO is implemented using Mathwork’s Reinforcement Learning Toolbox, and after some tuning of hyperparameters, the agent is able to narrow down to an optimal policy for various depth scenarios, whereas the latter is developed in python. At the end of this study, the agent is able to replace the decision maker and automatically suggest reference bottom hole pressure and tune the PID accordingly

Managed pressure drilling techniques and tools

The economics of drilling offshore wells is important as we drill more wells in deeper water. Drilling-related problems, including stuck pipe, lost circulation, and excessive mud cost, show the need for better drilling technology. If we can solve these problems, the economics of drilling the wells will improve, thus enabling the industry to drill wells that were previously uneconomical. Managed pressure drilling (MPD) is a new technology that enables a driller to more precisely control annular pressures in the wellbore to prevent these drillingrelated problems. This paper traces the history of MPD, showing how different techniques can reduce drilling problems. MPD improves the economics of drilling wells by reducing drilling problems. Further economic studies are necessary to determine exactly how much cost savings MPD can provide in certain situation. Furter research is also necessary on the various MPD techniques to increase their effectiveness

Advanced control of managed pressure drilling

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

The Diagnosis of Well Control Complications during Managed Pressure Drilling

The constant bottom-hole pressure method of managed pressure drilling is generally expected to reduce well control risks and apply well understood concepts when a kick is taken. Nevertheless, complications, such as operator error, leaks, plugging, equipment failures, and exceeding kick tolerance, can occur during kick circulation. By not properly interpreting the symptoms of a complication, a driller risks the consequences of additional influx, lost circulation or the simultaneous occurrence of both. To address the challenge of diagnosing complications, the implied pit gain (IPG) method is being evaluated as an enhancement to established industry practices. Traditional diagnostic methods attempt to match qualitative assessments of changes in the behavior of surface pressures, e.g. pump pressure and choke pressure, to particular complications. Under these circumstances, the interpretation of the onset of a complication may be subjective in nature and vary between individuals. By only evaluating changes in surface pressure, rig personnel may not be informed of the consequences of a given complication. Finally, previously published diagnostic strategies do not incorporate a structured approach for determining when kick tolerance has been exceeded. IPG is based on the concept that changes in surface pressures can be quantitatively linked to changes in pit gain with reasonable accuracy throughout the duration of a complication-free kick circulation. As a result, when these surface indicators deviate from a range of predicted behavior, one can objectively conclude that a complication is occurring. Research has been performed to demonstrate that the profile of the surface indicators, when deviating from predicted trends, contain unique attributes that can facilitate the diagnosis of a complication. Furthermore, quantifying the relationship between changes in surface pressure and pit gain over time provides data that can be used to assess the consequence of a given complication. Such knowledge may be used to facilitate effective field-based decisions or programming for intelligent systems to provide a correct response

Control-Oriented Modeling for Managed Pressure Drilling Automation Using Model Order Reduction

Automation of Managed Pressure Drilling (MPD) enables fast and accurate pressure control in drilling operations. The performance that can be achieved by automated MPD is determined by, firstly, the controller design and, secondly, the hydraulics model that is used as a basis for controller design. On the one hand, such hydraulics model should be able to accurately capture essential flow dynamics, e.g., wave propagation effects, for which typically complex models are needed. On the other hand, a suitable model should be simple enough to allow for extensive simulation studies supporting well scenario analysis and high-performance controller design. In this paper, we develop a model order reduction approach for the derivation of such a control-oriented model for {single-phase flow} MPD {operations}. In particular, a nonlinear model order reduction procedure is presented that preserves key system properties such as stability and provides guaranteed (accuracy) bounds on the reduction error. To demonstrate the quality of the derived control-oriented model, {comparisons with field data and} both open-loop and closed-loop simulation-based case studies are presented