Proposition of Operational Maps As Tools for Evaluating Safety During the Emergency Disconnection of Risers

The construction of subsea wells under deep water depths brought the necessity to understand the behavior of columns on such conditions. These columns can be risers, drill strings or casing strings, which are either being installed by lowering them until they reach the sea bottom and/or inside the well, or they are already connected and fully operational. Since these columns are exposed to the open sea, environmental loads such as waves and currents will affect them. Depending on how harsh these environmental conditions are, drilling operations may be suspended. Therefore, understanding how such loads interact with such columns are of the utmost importance if one wants to ensure operational safety. In this paper, we discuss about the problem of emergency disconnections of risers. The concern of doing an emergency disconnection is fundamental for ensuring operational safety because the well will lose a safety barrier, as the level of the drilling fluid inside the well can no longer be controlled after the riser is disconnected, and thus the fluid cannot maintain its downhole pressure anymore. This work focuses on a finite elements modeling of riser dynamics, with the appropriate applied loads, to verify under which sea conditions the riser must be disconnected. The result of such analysis is called an “operational map”, which displays the maximum values of stress along the riser as a function of different sea conditions. Using the riser material properties, this map can then be divided in two regions — failure and admissible — and thus one can see for which sea conditions the riser must be disconnected to avoid its failure. The contribution of the present study is proposing a methodology to elaborate an operational map for a given riser scenario, from which both failure and admissible regions can be seen for emergency disconnection operations.

Scale Effect in Freshwater Ice Flexural Strength

Ice flexural strength is an important parameter in the assessment of ice loads on the hulls of ice-class ships, sloped offshore structures or sloped bridge piers. While scale effects are well known for compressive ice strength, there has been debate as to whether or not scale effects in ice flexural strength exist. To investigate scale effects during flexural failure of freshwater ice, a comprehensive up-to-date database of beam flexural strength measurements has been compiled. The data show a considerable decrease in flexural strength as the specimen size increases, when examined over a large range of scales. An empirical model of freshwater ice flexural strength as a function of beam volume has been developed using regression analysis.

Numerical Integration of the AUSMV Scheme With a Dynamic Wellbore Temperature Model

The AUSMV scheme is a hybrid transient model derived from Advection-Upwind-Splitting Method (AUSM) and Flux Vector Splitting (FVS) method. The two-phase flow model was formulated under isothermal condition. This neglected the behavior of temperature during transient scenarios, for instance, unloading and drilling fluid circulation. In contrast to this assumption, wellbore temperature changes locally with time under such dynamic conditions. The numerical accuracy of the AUSMV scheme can be improved in two ways. The scheme can be reformulated by including energy equation in the system of governing conservation laws. This option, however, is computationally rigorous and expensive. A better alternative is to develop a separate dynamic temperature model that will calculate wellbore and formation temperatures. Then the two dynamic models — the AUSMV scheme and temperature model — are numerically coupled into a thermohydraulic simulator. The present work will include a brief introduction to the AUSMV scheme, followed by the description of the temperature model. In addition, how the two transient models are integrated will be presented. Simulation cases, demonstrating the improved modeling capability of the scheme for a drilling situation, will be shown.

Drilling Data Quality Management: Case Study With a Laboratory Scale Drilling Rig

Drilling industry operations heavily depend on digital information. Data analysis is a process of acquiring, transforming, interpreting, modelling, displaying and storing data with an aim of extracting useful information, so that the decision-making, actions executing, events detecting and incident managing of a system can be handled in an efficient and certain manner. This paper aims to provide an approach to understand, cleanse, improve and interpret the post-well or realtime data to preserve or enhance data features, like accuracy, consistency, reliability and validity. Data quality management is a process with three major phases. Phase I is an evaluation of pre-data quality to identify data issues such as missing or incomplete data, non-standard or invalid data and redundant data etc. Phase II is an implementation of different data quality managing practices such as filtering, data assimilation, and data reconciliation to improve data accuracy and discover useful information. The third and final phase is a post-data quality evaluation, which is conducted to assure data quality and enhance the system performance. In this study, a laboratory-scale drilling rig with a control system capable of drilling is utilized for data acquisition and quality improvement. Safe and efficient performance of such control system heavily relies on quality of the data obtained while drilling and its sufficient availability. Pump pressure, top-drive rotational speed, weight on bit, drill string torque and bit depth are available measurements. The data analysis is challenged by issues such as corruption of data due to noises, time delays, missing or incomplete data and external disturbances. In order to solve such issues, different data quality improvement practices are applied for the testing. These techniques help the intelligent system to achieve better decision-making and quicker fault detection. The study from the laboratory-scale drilling rig clearly demonstrates the need for a proper data quality management process and clear understanding of signal processing methods to carry out an intelligent digitalization in oil and gas industry.

Mathematical Modeling and Analysis of Riser Gas Unloading Problem

Pushing the boundaries of oil and gas exploration and development to new frontiers have led to exposure and more significant uncertainties, which necessitates robust strategies and techniques. With the increasing water depth, longer risers, and harsh pressure and temperature conditions; the risk of riser gas getting undetected get bigger. The lack of an integrated system to anticipate the controlling parameters at the choke below the BOP constrains the tackling operations and exacerbate the side effects of oil and gas well blowouts. This leads to an urgent need for an extensive study to address the riser gas unloading (RGU) events. This study encompasses the development of a robust model that can characterize the effect of different parameters such as temperature, mud types, back pressure, and solubility in RGU events. It also presents comparative results of oil-based and water-based mud systems, using a novel tool based on analytical and numerical models. The analytical model is constructed using combined gas law, heat transfer mechanism, and gas solubility and bubble point pressure concepts. Results suggest that the oil-based mud (OBM) takes more time for gas unloading in comparison to the water-based mud. Also, a significant deviation was observed in unloading patterns while considering temperature effect. For the drilling fluid without temperature consideration, the gas unloading occur in a smaller span of time and at a higher depth. Overall, this paper will demonstrate the effect of different parameters affecting the gas unloading in the riser, and present a comparative study of different parameters using an analytical which can be used in the field to get an idea of gas prior to any response for abnormality.

Combining Physics-Based and Data-Driven Models for Estimation of WOB During Ultra-Deep Ocean Drilling

Offshore drilling with drill string over 10,000m long has many technical challenges. Among them, the challenge to control the weight on bit (WOB) between a certain range is inevitable for the integrity of drill pipes and the efficiency of the drilling operation. Since WOB cannot be monitored directly during drilling, the tension at the top of the drill string is used as an indicator of the WOB. However, WOB and the surface measured tension are known to show different features. The deviation among the two is due to the dynamic longitudinal behavior of the drill string, which becomes stronger as the drill string gets longer and more elastic. One feature of the difference is related to the occurrence of high-frequency oscillation. We have analyzed the longitudinal behavior of drill string with lumped-mass model and captured the descriptive behavior of such phenomena. However, such physics-based models are not sufficient for real-time operation. There are many unknown parameters that need to be tuned to fit the actual operating conditions. In addition, the huge and complex drilling system will have non-linear behavior, especially near the drilling annulus. These features will only be captured in the data obtained during operation. The proposed hybrid model is a combination of physics-based models and data-driven models. The basic idea is to utilize data-driven techniques to integrate the obtained data during operation into the physics-based model. There are many options on how far we integrate the data-driven techniques to the physics-based model. For example, we have been successful in estimating the WOB from the surface measured tension and the displacement of the drill string top with only recurrent neural networks (RNNs), provided we have enough data of WOB. Lack of WOB measurement cannot be avoided, so the amount of data needs to be increased by utilizing results from physics-based numerical models. The aim of the research is to find a good combination of the two models. In this paper, we will discuss several hybrid model configurations and its performance.

Time-Dependent Computational Fluid Dynamics (CFD) Simulations of a Closing Blowout Preventer

Reliability of blowout preventers (BOP) is central for the safety of both rig workers and the surrounding environment. Analysis of dynamic fluid conditions within the wellbore and BOP can provide quantitative data related to this reliability. In cases of a hard shut in, it is suspected that the sudden closure of rams can cause a water hammer effect, creating pressure vibrations within the wellbore. Additionally, as the blowout preventer reaches a fully closed state, fluid velocity can drastically increase. This results in increased erosion rates within the blowout preventer. To investigate fluid movement and pressure vibrations during a well shut-in, CFD simulations will be conducted. Dynamic meshing techniques within ANSYS® FLUENT can be utilized to simulate closing blowout preventer configurations for both 2-D and 3-D geometries. These simulations would deliver information that could lead to a better understanding of certain performance issues during well shut-ins. Such information includes flow velocity magnitude within the BOP and maximum pressure pulse values within the wellbore.

Station Keeping Trials in Ice: Ice Load Monitoring System

In connection with the Statoil SKT project, DNV GL have developed a method for estimating ice loads on the ship hull structure and mooring tension of the anchor handling tug supply (AHTS) vessel Magne Viking by full scale measurements. In March 2017, the vessel was equipped with an extensive measurement system as a preparation for the dedicated station-keeping trial in drifting ice in the Bay of Bothnia. Data of the ice impacts acting on the hull were collected over the days of testing together with several other parameters from the ship propulsion system. Whilst moored, the tension in the mooring chain was monitored via a load cell and logged simultaneously to the other parameters. This paper presents the processes involved in developing the measurement concept, including the actual installation and execution phases. The basic philosophy behind the system is described, including the methods used to design an effective measurement arrangement, and develop procedures for estimation of ice loads based on strain measurements. The actual installation and the process of obtaining the recorded data sets are also discussed.

Experimental Study and Modeling of Heating Effect in Electrical Submersible Pump Operating With Ultra-Heavy Oil

A notable trend in the realm of oil production in harsh environments is the increasing use of Electrical Submersible Pump (ESP) systems. ESPs have even been used as an artificial-lift method for extracting high-viscosity oils in deep offshore fields. As a way of reducing workover costs, an ESP system may be installed at the well bottom or on the seabed. A critical factor, however, in deep-water production is the low temperature at the seabed. In fact, these low temperatures constitute the main source for many flow-assurance problems, such as the increase in friction losses due to high viscosity. Oil viscosity impacts pump performance, reducing the head and increasing the shaft power. This study investigates the influence of a temperature increase of ultra-heavy oil on ESP performance and the heating effect through a 10-stage ESP. Using several flow rates, tests are performed at four rotational speeds and with four viscosity levels. At each rotational speed curve, researchers keep constant the inlet temperature and viscosity. The study compares the resulting data with a simple heat model developed to estimate the oil outlet temperature as functions of ESP performance parameters. The experimental data is represented by a one-dimensional model that also simulates a 100-stage ESP. The simulations demonstrate that as the oil heat flows through the pump, the pump’s efficiency increases.

Experimental and Numerical Characterization of the Stress-Strain Behavior of Weak Sandstone Formations for Sanding Assessment

In this study an extensive experimental program has been carried out in order to characterize the mechanical behavior of two weak sandstone formations of an offshore field for application to sand production modeling. The experimental tests included Scratch tests, Triaxial tests and Advanced thick wall cylinder tests (ATWC) where the sand production initiation and the cumulative sand produced were registered. Numerical simulations of experimental tests were then performed using an advanced elasto-plastic constitutive model. Triaxial tests simulations allowed calibrating the constitutive model parameters. These parameters were employed for the numerical simulation of the ATWC in order to determine the equivalent plastic strain threshold required to the onset of sand production observed in laboratory for sanding assessment. Results obtained highlight the importance to use a realistic representation of the rock behavior focusing on post-yield behavior in order to build confidence in model predictions.