Probabilistic Flow Modelling Approach for Kick Tolerance Calculations

2017 ◽  
Author(s):  
Dalila Gomes ◽  
Knut Steinar Bjørkevoll ◽  
Johnny Frøyen ◽  
Kjell Kåre Fjelde ◽  
Dan Sui ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Pore Pressure ◽  
Flow Model ◽  
Transient Flow ◽  
Probabilistic Approach ◽  
Numerical Approach ◽  
Free Form ◽  
Maximum Pressure ◽  
Fracture Pressure ◽  
Calculation Process

During drilling, there must be an evaluation of the maximum pressure that the formation can handle during a well kill scenario. This will depend on various parameters like fracture pressure, pore pressure, kick volume and several other factors. The depth of the next planned hole section will depend on if a kick of a certain size can be handled safely. This evaluation is often referred to as performing kick tolerances. When starting to drill a section, one will take a leak off test to get an indication of the fracture pressure at the last set casing shoe and this will be important information for the kick tolerance results. For HPHT wells the margin between pore and fracture pressures will be small, and one often has to resort to using transient flow models to perform the kick tolerances. However, there are many uncertain parameters that are affecting the results. Some examples here are pore pressure, type of kick and kick distribution. There is a need for trying to incorporate the uncertainty in the calculation process to give a better overview of possible outcomes. This approach has become more and more popular, and one example here is reliability based casing design. This paper will first describe the kick tolerance concept and its role in well design planning and operational follow up. An overview of all parameters that can affect the results will be given. In water based mud, the gas kick will be in free form yielding higher maximum casing shoe pressures compared to the situation when oil based mud is used where the kick can be fully dissolved. Then it will be shown how both an analytical and a transient flow model can be used in combination with the use of Monte Carlo simulations to generate a probabilistic kick tolerance calculation showing possible outcomes for maximum casing shoe pressure for different kick volumes. Here uncertain input parameters that can affect the calculation result will be drawn from statistical distributions and propagated through the flow model to estimate the casing shoe pressure. Multiple runs will be needed in the Monte Carlo simulation process to generate a distribution of the maximum casing shoe pressure. This will demand a rapid and robust flow model. The resulting maximum casing shoe pressure distribution will then be compared against the uncertainty in the fracture pressure at the last set casing shoe to yield a probability for inducing losses. The numerical approach for predicting well pressures and a schematic of the total calculation process will be given. Emphasis will also be put on discussing how this should be presented to the engineer with respect to visualization and communication. It will also be shown that one of the strengths of the probabilistic approach is that it is very useful for performing sensitivity analysis such that the most dominating factors affecting the calculation results can be identified. In that way, it can help in interpreting and improving the reliability of the kick tolerance simulation results.

Probabilistic Models for Kick Tolerance

2021 ◽  
Author(s):  
Martine Kristoffersen ◽  
Dalila Gomes ◽  
Kjell Kåre Fjelde
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Analytical Model ◽  
Flow Model ◽  
Transient Flow ◽  
Simulation Framework ◽  
Gas Distribution ◽  
Fracture Pressure ◽  
Open Hole

Abstract Kick tolerance is an evaluation of how large kick sizes i.e. kick volumes an open hole section can withstand without threatening the formation integrity at the shoe. If a certain kick size cannot be handled safely, the planned open hole section must be shortened, and the casing design must be altered. Three models for calculating the kick tolerances in a well will be compared for a long and short open hole section for various lengths of bottom hole assembly (BHA). The kick tolerance will be performed probabilistically by use of Monte Carlo simulations where important input parameters are considered as distributions. The paper will focus on where the models differ in their results and discuss various opportunities and challenges with using a probabilistic approach. The models will be integrated in a Monte Carlo simulation framework where the major input uncertainties will be pore pressure, fracture pressure and initial gas distribution in the well. The output will be a distribution of the casing pressure load that has to be compared to the fracture pressure distribution which results in a certain probability for fracturing for a given kick size. Only gas kick in water-based mud will be considered. First a transient model based on the single bubble concept was considered and integrated in the Monte Carlo simulation framework. This was first compared against an analytical model which calculates the maximum casing shoe pressure at static shut in conditions. The analytical model considers uncertainty in the initial gas distribution. A transient flow model based on the drift flux model was also considered. Both short and long open hole length were considered. BHA length and kick size were varied. The results show that the transient flow model provides the least conservative results but also the analytical model reduces the probability for fracturing compared to the single bubble model. In most cases, the maximum casing shoe pressure is achieved when kick is located at the BHA. This paper extends the application of methods for reliability-based casing design to also include probabilistic kick tolerances. This is a contribution related to how the well design process can become more risk based. Some challenges related to specification of tolerance requirements, required number of Monte Carlo simulations and computing time will be discussed. It also provides an overview of the differences between the models and which parameters that are most important for the results.

Assessment of cracked pipe by Monte Carlo method subject to transient flow

2018 ◽  
Vol 8 (1) ◽  
pp. 54-61 ◽  
Author(s):  
Mohamed Hassani ◽  
◽  
Omar Bouledroua ◽  
Mohammed Hadj Meliani ◽  
Mohamed Sadou ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Transient Flow

Influence of Surge Tank and Relief Valve on Transient Flow Behaviour in Hydropower Stations

2011 ◽  
Author(s):  
Alireza Riasi ◽  
Ahmad Nourbakhsh
Keyword(s):  
Power Plants ◽  
Transient Flow ◽  
Flow Analysis ◽  
Internal Boundary ◽  
Transient Condition ◽  
Maximum Pressure ◽  
Surge Tank ◽  
Small Hydropower ◽  
Relief Valve ◽  
Hydropower Stations

Unsteady flow analysis in water power stations is one of the most important issues in order to predict undesirable pressure variations in waterways and also probable changes in rotor speed for the power plants safe operation. Installation of surge tank and relief valve is the two main methods for controlling of hydraulic transient. The relief valve is used in several medium and small hydropower stations instead of the surge tank and mounted on the penstock near the powerhouse. The recent generation of relief valves are reliable and beneficial and consist of fully control system that directly conducted by governor. This paper presents a numerical method for transient flow in hydropower stations using surge tank and relief valve. For this purpose the governing equations of transient flow in closed conduit are solved using the method of characteristics (MOC) using unsteady friction. Hydraulic turbine, surge tank and relief valve are considered as internal boundary conditions. The influence of surge tank and also relief valve on the maximum pressure in spiral case and turbine over speed has been studied for a real case. The results show that the transient condition is considerably improved by using a relief valve and this device can be mounted in lieu of an expensive surge tank.

scholarly journals Generating Empirical Core Size Distributions of Hedonic Games Using a Monte Carlo Method

2021 ◽  
pp. 2250001
Author(s):  
Andrew J. Collins ◽  
Sheida Etemadidavan ◽  
Wael Khallouli
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Numerical Approach ◽  
Core Stability ◽  
Size Distributions ◽  
Core Size ◽  
Empirical Distributions ◽  
Research Findings ◽  
Hedonic Games ◽  
Partition Size

Hedonic games have gained popularity over the last two decades, leading to several research articles that have used analytical methods to understand their properties better. In this paper, a Monte Carlo method, a numerical approach, is used instead. Our method includes a technique for representing, and generating, random hedonic games. We were able to create and solve, using core stability, millions of hedonic games with up to 16 players. Empirical distributions of the hedonic games’ core sizes were generated, using our results, and analyzed for games of up to 13 players. Results from games of 14–16 players were used to validate our research findings. Our results indicate that core partition size might follow the gamma distribution for games with a large number of players.

scholarly journals ANALISIS PENJADWALAN PROYEK GEDUNG BERTINGKAT DENGAN METODE PERT DAN M-PERT MENGGUNAKAN SIMULASI MONTE CARLO

2020 ◽  
Vol 3 (3) ◽  
pp. 533
Author(s):  
Josua Guntur Putra ◽  
Jane Sekarsari
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Standard Deviation ◽  
Research Method ◽  
Probabilistic Approach ◽  
Deterministic Scheduling ◽  
Work Duration ◽  
The Mean ◽  
Building Project ◽  
Story Building

One of the keys to success in construction execution is timeliness. In fact, construction is often late than originally planned. It’s caused by project scheduling uncertainty. Deterministic scheduling methods use data from previous projects to determine work duration. However, not every project has same work duration. The PERT method provides a probabilistic approach that can overcome these uncertainties, but it doesn’t account for the increase in duration due to parallel activities. In 2017, the PERT method was developed into the M-PERT method. The purpose of this study is to compare the mean duration and standard deviation of the overall project between PERT and M-PERT methods and compare them in Monte Carlo simulation. The research method used is to calculate the mean duration of the project with the PERT, M-PERT, and Monte Carlo simulation. The study was applied to a three-story building project. From the results of the study, the standard deviation obtained was 5.079 for the M-PERT method, 8.915 for the PERT method, and 5.25 for the Monte Carlo simulation. These results show the M-PERT method can provide closer results to computer simulation result than the PERT method. Small standard deviation value indicates the M-PERT method gives more accurate results.ABSTRAKSalah satu kunci keberhasilan dalam suatu pelaksanaan konstruksi adalah ketepatan waktu. Kenyataannya, pelaksanaan konstruksi sering mengalami keterlambatan waktu dari yang direncanakan. Hal ini disebabkan oleh ketidakpastian dalam merencanakan penjadwalan proyek. Metode penjadwalan yang bersifat deterministik menggunakan data dari proyek sebelumnya untuk menentukan durasi pekerjaan. Akan tetapi, tidak setiap proyek memiliki durasi pekerjaan yang sama. Metode PERT memberikan pendekatan probabilistik yang dapat mengatasi ketidakpastian tersebut, tetapi metode ini tidak memperhitungkan pertambahan durasi akibat adanya kegiatan yang berbentuk paralel. Pada tahun 2017, metode PERT dikembangkan menjadi metode M-PERT. Tujuan dari penelitian ini adalah membandingkan mean durasi dan standar deviasi proyek secara keseluruhan antara metode PERT dan M-PERT dan membandingkan kedua metode tersebut dalam simulasi Monte Carlo. Metode penelitian yang dilakukan adalah menghitung mean durasi proyek dengan metode PERT, M-PERT, dan simulasi Monte Carlo. Penelitian diterapkan pada proyek gedung bertingkat tiga. Dari hasil penelitian, nilai standar deviasi diperoleh sebesar 5,079 untuk metode M-PERT, 8,915 untuk metode PERT, dan 5,25 untuk simulasi Monte Carlo. Hasil ini menunjukan metode M-PERT dapat memberikan hasil yang lebih mendekati hasil simulasi komputer daripada metode PERT. Nilai standar deviasi yang kecil menunjukan metode M-PERT memberikan hasil yang lebih akurat.

Modelling present and future Po river interactions with alluvial aquifers (Low Po River Plain, Italy)

2014 ◽  
Vol 5 (3) ◽  
pp. 457-471 ◽  
Author(s):  
M. Mastrocicco ◽  
N. Colombani ◽  
A. Gargini
Keyword(s):  
Groundwater Flow ◽  
Large Scale ◽  
Flow Model ◽  
Transient Flow ◽  
Scale Model ◽  
Groundwater Flow Model ◽  
Po River ◽  
Local Conditions ◽  
Aquifer System ◽  
Hydrological Conditions

A modelling study on a multi-layered confined/unconfined alluvial aquifer system was performed to quantify surface water/groundwater interactions. The calibrated groundwater flow model was used to forecast climate change impacts by implementing the results of a downscaled A1B model ensemble for the Po river valley. The modelled area is located in the north-western portion of the Ferrara Province (Northern Italy), along the eastern bank of the Po river. The modelling procedure started with a large scale steady state model followed by a transient flow model for the central portion of the domain, where a telescopic mesh refinement was applied. The calibration performance of both models was satisfactory, in both drought and flooding conditions. Subsequently, forecasted rainfall, evapotranspiration and Po river stage at 2050, were implemented in the calibrated large scale groundwater flow model and their uncertainties discussed. Three scenarios were run on the large scale model: the first simulating mean hydrological conditions and the other two simulating one standard deviation above and below the mean hydrological conditions. The forecasted variations in groundwater/Po river fluxes are relevant, with a general increase of groundwater levels due to local conditions, although there are large uncertainties in the predicted variables.

A numerical approach for modeling thermo-poro-elastic deformation sources in volcanic and hydrothermal regions.

2021 ◽  
Author(s):  
Massimo Nespoli ◽  
Maria Elina Belardinelli ◽  
Maurizio Bonafede
Keyword(s):  
Pore Pressure ◽  
Homogeneous Medium ◽  
Numerical Approach ◽  
Deformation Source ◽  
Campi Flegrei ◽  
Temperature Changes ◽  
Magmatic Body ◽  
Temperature Diffusion ◽  
Realistic Representation ◽  
Source Models

<p><span>The Thermo-Poro-Elastic (TPE) inclusions contribute to deformation and stress in volcanic and hydrothermal areas. Differently from other deformation source models (e.g. magma chambers), the TPE sources effects are due to pore-pressure and temperature changes of the fluid within the inclusion. So that the TPE inclusions can allow large deformations even in those volcanic environments in which there is no evidence of a shallow magmatic body. This kind of sources also provides large deviatoric stresses, promoting different types of focal mechanisms both inside and around them. With respect to a previous work, we propose a numerical model that allows for a more realistic representation of TPE sources: we can represent inclusions with an arbitrary geometry and we take into account the elastic stratification of the crust, thanks to a modified version of the EDGRN/EDCMP code. We can also represent the case of a depth dependent distribution of pore pressure and temperature changes within inclusions, as expected during the transient stage of fluid propagation and temperature diffusion. We found that elastic layering and transient changes of the TPE source can promote both normal and thrust earthquakes in its interior. For the 1982-84 unrest episode at Campi Flegrei the inversion of geodetic data leads to a lower misfit between modeled and measured deformation data, with respect to a homogeneous medium and the retrieved geometry and location of the thermo-poro-elastic are in good agreement with the observed distribution of seismicity.</span></p>

scholarly journals Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel

2019 ◽  
Vol 9 (18) ◽  
pp. 3708 ◽  
Author(s):  
Liguo Tan ◽  
Juncheng Wu ◽  
Xiaoyan Yang ◽  
Senmin Song
Keyword(s):  
Numerical Approach ◽  
Key Factors ◽  
Simulation Experiments ◽  
Accuracy Improvement ◽  
Flight Path Angle ◽  
Calculation Process ◽  
Aerial Vehicle ◽  
Heading Angle ◽  
Optimal Approach ◽  
Landing Trajectory

The location, velocity, and flight path angle of an autonomous unmanned aerial vehicle (UAV) landing on a moving vessel are key factors for an optimal landing trajectory. To tackle this challenge, this paper proposes a method for calculating the optimal approach landing trajectory between an UAV and a small vessel. A numerical approach (iterative method) is used to calculate the optimal approach landing trajectory, and the initial lead is introduced in the calculation process of the UAV trajectory for the inclination and heading angle for accuracy improvement, so that the UAV can track and calculate the optimal landing trajectory with high precision. Compared with the variational method, the proposed method can calculate an optimal turning direction angle for the UAV during the landing. Simulation experiments verify the effectiveness of the proposed algorithm and give optimal initialization values.

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