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

Effect of Pipe Rotation on Casing Pressure Within MPD Applications

Well control is one of the most crucial sectors in drilling engineering. Human lives and safety depend on the correct execution of the engineering design. Managed Pressure Drilling (MPD) is a new technology that has recently emerged in the oil and gas industry. It has special well control abilities supported by the RCD to continue drilling or carry operations that involve pipe rotation, while circulating out a gas kick. This thesis examines the effect of pipe rotation on casing pressure profiles within MPD kick circulation application. The analysis was carried on real scale kick experiments. These experiments were carried in a controlled environment that mimicked downhole conditions with a gas influx entering the wellbore. Both water based mud and oil based mud were evaluated. Then, the real scale tests analysis was coupled with the effect of pipe rotation through the application of correlations. The correlations estimate the change in frictional pressure loss in the annuls for non-Newtonian fluids with pipe rotation. A study of the effect of a larger size gas bubble breakage into smaller size bubbles on the maximum anticipated casing pressure is also included in this research. The thesis was divided into three models: (1) dissolved gas model in OBM. (2) single bubble model in WBM. (3) dispersed bubble model in WBM. The first two models studied the effect of frictional pressure changes on the anticipated casing pressure. The dispersed bubble model studies the effect of breaking the gas bubble into many very small bubbles. The practical outcome is to further the precision of the estimation of downhole pressure limits since MPD address narrow fracture-pore pressure window and to find if casing pressure changes would have any effect on the RCD rating selection and if the rotation can be safely conducted

Modeling and control of managed pressure drilling operations

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.Mechanical Engineerin

A Simulation Study of Factors that Affect Pressure Control During Kick Circulation in Managed Pressure Drilling Operations

An university-industry consortium has been studying alternative well control procedures to be used for kicks taken during managed pressure drilling (MPD) operations using the constant bottom hole pressure (CBHP) method. The CBHP method of MPD allows more precise control of wellbore pressure than conventional drilling. MPD surface equipment allows more alternatives for controlling a kick and may support faster detection of kicks and losses which can reduce the severity of a well control event. Nevertheless, the elimination of well control incidents cannot be guaranteed, and the uncertainty in downhole drilling margins are not reduced by adopting MPD methods. The primary objective of this research was to evaluate pressure variation and maximum pressure during kick circulation to properly design and conduct a MPD operation. Three specific objectives were addressed in this project. First, a pump start up method to keep bottomhole pressure approximately constant when beginning kick circulation after shut in is presented. Second, since formation pressure cannot be calculated by using shut in drillpipe pressure during typical MPD operations, a procedure to estimate kick zone formation pressure based on circulating pressure was documented. And third, a simple and practical method to estimate maximum expected casing pressure during well control operations was developed. This method was also used as part of a method for selecting kick circulating rate. Methods for making calculations to achieve each of these objectives were developed. Computer simulations were used for comparison to a range of realistic well conditions. Full-scale gas kicks experiments were done to confirm applicability to a limited range of real situations. The applicability and accuracy of the method developed in this research were tested based on actual drilling practices reproduced in computer simulations and LSU well facility experiments

Continuous learning of analytical and machine learning rate of penetration (ROP) models for real-time drilling optimization

Oil and gas operators strive to reach hydrocarbon reserves by drilling wells in the safest and fastest possible manner, providing indispensable energy to society at reduced costs while maintaining environmental sustainability. Real-time drilling optimization consists of selecting operational drilling parameters that maximize a desirable measure of drilling performance. Drilling optimization efforts often aspire to improve drilling speed, commonly referred to as rate of penetration (ROP). ROP is a function of the forces and moments applied to the bit, in addition to mud, formation, bit and hydraulic properties. Three operational drilling parameters may be constantly adjusted at surface to influence ROP towards a drilling objective: weight on bit (WOB), drillstring rotational speed (RPM), and drilling fluid (mud) flow rate. In the traditional, analytical approach to ROP modeling, inflexible equations relate WOB, RPM, flow rate and/or other measurable drilling parameters to ROP and empirical model coefficients are computed for each rock formation to best fit field data. Over the last decade, enhanced data acquisition technology and widespread cheap computational power have driven a surge in applications of machine learning (ML) techniques to ROP prediction. Machine learning algorithms leverage statistics to uncover relations between any prescribed inputs (features/predictors) and the quantity of interest (response). The biggest advantage of ML algorithms over analytical models is their flexibility in model form. With no set equation, ML models permit segmentation of the drilling operational parameter space. However, increased model complexity diminishes interpretability of how an adjustment to the inputs will affect the output. There is no single ROP model applicable in every situation. This study investigates all stages of the drilling optimization workflow, with emphasis on real-time continuous model learning. Sensors constantly record data as wells are drilled, and it is postulated that ROP models can be retrained in real-time to adapt to changing drilling conditions. Cross-validation is assessed as a methodology to select the best performing ROP model for each drilling optimization interval in real-time. Constrained to rig equipment and operational limitations, drilling parameters are optimized in intervals with the most accurate ROP model determined by cross-validation. Dynamic range and full range training data segmentation techniques contest the classical lithology-dependent approach to ROP modeling. Spatial proximity and parameter similarity sample weighting expand data partitioning capabilities during model training. The prescribed ROP modeling and drilling parameter optimization scenarios are evaluated according to model performance, ROP improvements and computational expensePetroleum and Geosystems Engineerin

Modeling of Swab and Surge Pressures: A Survey

Swab and surge pressure fluctuations are decisive during drilling for oil. The axial movement of the pipe in the wellbore causes pressure fluctuations in wellbore fluid; these pressure fluctuations can be either positive or negative, corresponding to the direction of the movement of the pipe. For example, if the drill string is lowering down in the borehole, the drop is positive (surge pressure), and if the drill string is pulling out of the hole, the drop is negative (swab pressure). The intensity of these pressure fluctuations depends on the speed of the lowering down (tripping in) or withdrawing the pipe out (tripping out). High tripping speed corresponds to higher pressure fluctuations and can lead to fracturing the well formation. Low tripping speed leads to a slow operation, causing non-productive time, thus increasing the overall well budget. Researchers used mathematical equations and physics to understand the phenomena and have provided many empirical, mathematical, and physics-based models. This paper starts with a literature study on the swab and surge pressures. After that, this paper concludes with a proposal for a new approach. The new approach proposes developing new models that are more robust, using field data, as we have access to field data from drilling operations. Research using field data would provide data-driven methodologies as new solutions for the rate of penetration, reservoir management, and drilling optimization. The expected outcome will improve the performance of the tripping in and tripping out process within drilling and well construction, and will further reduce the risk related to swab and surge pressures.publishedVersio