scholarly journals Machine Learning Augmented Two-Fluid Model for Segregated Flow

Fluids ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 12
Author(s):  
Ayush Rastogi ◽  
Yilin Fan
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Fluid Model ◽  
Momentum Conservation ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Practical Applications ◽  
Segregated Flow ◽  
Two Fluid Model ◽  
Two Fluid

Segregated flow, including stratified and annular flows, is commonly encountered in several practical applications such as chemical, nuclear, refrigeration, and oil and gas industries. Accurate prediction of liquid holdup and the pressure gradient is of great importance in terms of system design and optimization. The current most widely accepted model for segregated flow is a physics-based two-fluid model that treats gas and liquid phases separately by incorporating mass and momentum conservation equations. It requires empirically derived closure relationships that have the limitation of being applicable only under a narrow range of input parameters under which they were developed. In this paper, we proposed a more generalized machine learning augmented two-fluid model, using a database that spans the range of various flowing conditions and fluid properties. Machine learning algorithms such as random forest, neural networks, and gradient boosting were tested for the best performing data-driven predictive model. The new model proposed in this work successfully captures the complex, dynamic, and non-linear relationships between the friction factor and flowing conditions. A comprehensive model evaluation against nineteen existing correlations shows the best results from the proposed model.

Analyses of Hydrodynamic Structure of Water Jet and Its Application to Jet Grouting

2003 ◽  
Author(s):  
Kenji Yoshida ◽  
Isao Kataoka ◽  
Hiroshi Yoshida ◽  
Mitsuru Yokoo ◽  
Kiyoshi Horii
Keyword(s):  
Constitutive Equations ◽  
Fluid Model ◽  
Momentum Conservation ◽  
Water Jet ◽  
Two Phase ◽  
Dispersed Flow ◽  
Two Fluid Model ◽  
Basic Equations ◽  
Physical Phenomena ◽  
Two Fluid

Analyses of water jet based on two-fluid model of two-phase dispersed flow have been carried out for single water jet and cross water jet in relation to the water jet technology in civil engineering. Mass and momentum conservation equations for liquid phase (droplet) gas phase (air) were formulated separately (two-fluid model formulation). Physical modeling of diffusion of droplets, drag coefficient of droplet in dispersed flow, shear stress at jet interface, etc has been carried out in detail and constitutive equations for these physical phenomena have been developed. Based on the two-fluid model basic equations and constitutive equations, one-dimensional analysis has been carried out considering simplified model. In the practical application of present analyses, some preliminary analyses on cross jet where two water jets collide with certain collision angles have been carried out and the predicted results reasonably explain the experimental results.

scholarly journals Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1293
Author(s):  
Shamil Islamov ◽  
Alexey Grigoriev ◽  
Ilia Beloglazov ◽  
Sergey Savchenkov ◽  
Ove Tobias Gudmestad
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Oil And Gas ◽  
New Technologies ◽  
Learning Algorithms ◽  
Gas Production ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Drilling Process ◽  
Well Drilling

This article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the process of drilling wells. Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays, companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work describes a comparison of machine learning algorithms for anomaly detection during well drilling. In particular, machine learning algorithms will make it possible to make decisions when determining the geometry of the grid of wells—the nature of the relative position of production and injection wells at the production facility. Development systems are most often subdivided into the following: placement of wells along a symmetric grid, and placement of wells along a non-symmetric grid (mainly in rows). The tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we used historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. The experiential results illustrate that a model based on gradient boosting can classify the complications in the drilling process better than other models.

Drilling Optimization Applying Machine Learning Regression Algorithms

2021 ◽  
Author(s):  
Freddy J. Marquez
Keyword(s):  
Machine Learning ◽  
Parameter Optimization ◽  
Oil And Gas ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Paper Machine ◽  
Drilling Performance ◽  
Sensitivity Parameter ◽  
Extreme Gradient Boosting

Abstract Machine Learning is an artificial intelligence subprocess applied to automatically and quickly perform mathematical calculations to data in order to build models used to make predictions. Technical papers related to machine learning algorithms applications have being increasingly published in many oil and gas disciplines over the last five years, revolutionizing the way engineers approach to their works, and sharing innovating solutions that contributes to an increase in efficiency. In this paper, Machine Learning models are built to predict inverse rate of penetration (ROPI) and surface torque for a well located at Gulf of Mexico shallow waters. Three type of analysis were performed. Pre-drill analysis, predicting the parameters without any data of the target well in the database. Drilling analysis, running the model every sixty meters, updating the database with information of the target well and predicting the parameters ahead the bit. Sensitivity parameter optimization analysis was performed iterating weight on bit and rotary speed values as model inputs in order identify the optimum combination to deliver the best drilling performance under the given conditions. The Extreme Gradient Boosting (XGBoost) library in Python programming language environment, was used to build the models. Model performance was satisfactory, overcoming the challenge of using drilling parameters input manually by drilling bit engineers. The database was built with data from different fields and wells. Two databases were created to build the models, one of the models did not consider logging while drilling (LWD) data in order to determine its importance on the predictions. Pre-drill surface torque prediction showed better performance than ROPI. Predictions ahead the bit performance was good both for torque and ROPI. Sensitivity parameter optimization showed better resolution with the database that includes LWD data.

Petrofacies classification using machine learning algorithms

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA101-WA113 ◽  
Author(s):  
Adrielle A. Silva ◽  
Mônica W. Tavares ◽  
Abel Carrasquilla ◽  
Roseane Misságia ◽  
Marco Ceia
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Water Saturation ◽  
Carbonate Reservoir ◽  
Machine Learning Algorithms ◽  
Carbonate Reservoirs ◽  
Training Data ◽  
Southeastern Brazil ◽  
Gradient Boosting ◽  
Data Set

Carbonate reservoirs represent a large portion of the world’s oil and gas reserves, exhibiting specific characteristics that pose complex challenges to the reservoirs’ characterization, production, and management. Therefore, the evaluation of the relationships between the key parameters, such as porosity, permeability, water saturation, and pore size distribution, is a complex task considering only well-log data, due to the geologic heterogeneity. Hence, the petrophysical parameters are the key to assess the original composition and postsedimentological aspects of the carbonate reservoirs. The concept of reservoir petrofacies was proposed as a tool for the characterization and prediction of the reservoir quality as it combines primary textural analysis with laboratory measurements of porosity, permeability, capillary pressure, photomicrograph descriptions, and other techniques, which contributes to understanding the postdiagenetic events. We have adopted a workflow to petrofacies classification of a carbonate reservoir from the Campos Basin in southeastern Brazil, using the following machine learning methods: decision tree, random forest, gradient boosting, K-nearest neighbors, and naïve Bayes. The data set comprised 1477 wireline data from two wells (A3 and A10) that had petrofacies classes already assigned based on core descriptions. It was divided into two subsets, one for training and one for testing the capability of the trained models to assign petrofacies. The supervised-learning models have used labeled training data to learn the relationships between the input measurements and the petrofacies to be assigned. Additionally, we have developed a comparison of the models’ performance using the testing set according to accuracy, precision, recall, and F1-score evaluation metrics. Our approach has proved to be a valuable ally in petrofacies classification, especially for analyzing a well-logging database with no prior petrophysical information.

On the Numerical Modeling of Slug and Intermittent Flows in Oil and Gas Production

2017 ◽  
Author(s):  
Angela O. Nieckele ◽  
João N. E. Carneiro
Keyword(s):  
Oil And Gas ◽  
Fluid Model ◽  
Dynamic Pressure ◽  
Time Discretization ◽  
Gas Production ◽  
Two Phase ◽  
Dynamic Simulations ◽  
Oil And Gas Production ◽  
Two Fluid Model ◽  
Two Fluid

Recent advances on the modeling of two-phase flows in pipes have shown that the accurate modeling of Two-Fluid equations allow the dynamic simulation of various regimes within a single numerical framework, diminishing the empiricism associated with the flow-pattern dependent closure relations. Such “Regime-Capturing” approaches have been traditionally called “Slug-Capturing”, as a reference to dynamic simulations of stratified-to-slug transition. In this paper, we will outline several examples of applications, ranging from horizontal stratified wavy, slug and annular flows, to vertical annular and intermittent flows. Vertical flow has been a bottleneck in Slug Capturing due to ill-posedness of the Two-Fluid Model. Ill-posedness of the model equations will be briefly addressed along with different regularization methods and stabilizing terms based on physical behavior, such as shape profile factors and dynamic pressure contributions. In order to numerically solve the governing system of equations, the finite volume method is employed with Upwind and second order TVD spatial discretization schemes, along with first order time discretization. Flow parameters such as temperature and pressure drop are determined as well as film thickness and wave characteristics of both annular and stratified flow, and slug velocity, length and frequency in slugging cases. Comparison with experimental data for annular, slug and stratified flows, with different fluids and pipeline configurations are presented, illustrating the good performance of the methodology.

Analyses Hydrodynamic Structure of Cross Water Jet and Its Performance of Soil Improvement

2005 ◽  
Author(s):  
Mitsuhiro Shibazaki ◽  
Hiroshi Yoshida ◽  
Kenji Yoshida ◽  
Kiyoshi Horii ◽  
Isao Kataoka
Keyword(s):  
Constitutive Equations ◽  
Fluid Model ◽  
Soil Improvement ◽  
Momentum Conservation ◽  
Water Jet ◽  
Two Phase ◽  
Dispersed Flow ◽  
Hydrodynamic Structure ◽  
Two Fluid Model ◽  
Two Fluid

Analyses of water jet based on two-fluid model of two-phase dispersed flow have been carried out for single water jet and cross water jet in relation to the water jet technology in civil engineering. Mass and momentum conservation equations for liquid phase (droplet) gas phase (air) were formulated separately (two-fluid model formulation). Physical modeling of diffusion of droplets, drag coefficient of droplet in dispersed flow, shear stress at jet interface, etc has been carried out in detail and constitutive equations for these physical phenomena have been developed. Based on the two-fluid model basic equations and constitutive equations, one-dimensional analysis has been carried out considering simplified mode and hydrodynamic structure of water jet was well predicted. In the practical application of present analyses, some preliminary analyses on cross jet where two water jets collide with certain collision angles have been carried out and the predicted results reasonably explain the experimental results.

Numerical Study of Two-Phase Flow in a Horizontal Pipeline Using an Unconditionally Hyperbolic Two-Fluid Model

2018 ◽  
Author(s):  
Raphael V. N. de Freitas ◽  
Carina N. Sondermann ◽  
Rodrigo A. C. Patricio ◽  
Aline B. Figueiredo ◽  
Gustavo C. R. Bodstein ◽  
...  
Keyword(s):  
Volume Fraction ◽  
Numerical Study ◽  
Fluid Model ◽  
Momentum Conservation ◽  
Design Stage ◽  
Two Phase Flows ◽  
Two Phase ◽  
Efficient Manner ◽  
Two Fluid Model ◽  
Two Fluid

Numerical simulation is a very useful tool for the prediction of physical quantities in two-phase flows. One important application is the study of oil-gas flows in pipelines, which is necessary for the proper selection of the equipment connected to the line during the pipeline design stage and also during the pipeline operation stage. The understanding of the phenomena present in this type of flow is more crucial under the occurrence of undesired effects in the duct, such as hydrate formation, fluid leakage, PIG passage, and valve shutdown. An efficient manner to model two-phase flows in long pipelines regarding a compromise between numerical accuracy and cost is the use of a one-dimensional two-fluid model, discretized with an appropriate numerical method. A two-fluid model consists of a system of non-linear partial differential equations that represent the mass, momentum and energy conservation principles, written for each phase. Depending on the two-fluid model employed, the system of equations may lose hyperbolicity and render the initial-boundary-value problem illposed. This paper uses an unconditionally hyperbolic two-fluid model for solving two-phase flows in pipelines in order to guarantee that the solution presents physical consistency. The mathematical model here referred to as the 5E2P (five equations and two pressures) comprises two equations of continuity and two momentum conservation equations, one for each phase, and one equation for the transport of the volume fraction. A priori this model considers two distinct pressures, one for each phase, and correlates them through a pressure relaxation procedure. This paper presents simulation cases for stratified two-phase flows in horizontal pipelines solved with the 5E2P coupled with the flux corrected transport method. The objective is to evaluate the numerical model capacity to adequately describe the velocities, pressures and volume fraction distributions along the duct.

A Hyperbolicity Analysis of the 1991 OLGA’s Model for Isothermal Flow

2018 ◽  
Author(s):  
Carina N. Sondermann ◽  
Raphael V. N. de Freitas ◽  
Rodrigo A. C. Patricio ◽  
Aline B. Figueiredo ◽  
Gustavo C. R. Bodstein ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Numerical Study ◽  
Fluid Model ◽  
Oil And Gas Industry ◽  
Isothermal Flow ◽  
Offshore Platforms ◽  
One Dimensional ◽  
Drift Flux Model ◽  
Two Fluid Model ◽  
Two Fluid

Multiphase flows are encountered in many engineering problems. Particularly in the oil and gas industry, many applications involve the transportation of a mixture of oil and natural gas in long pipelines from offshore platforms to the continent. Numerical simulations of steady and unsteady flows in pipelines are usually based on one-dimensional models, such as the two-fluid model, the drift-flux model and the homogeneous equilibrium model. The 1991’s version of the well-known and widely-used commercial software OLGA describes a system of non-linear equations of the two-fluid-model type, with an extra equation for the presence of liquid droplets. It is well known that one-dimensional formulations may be physically inconsistent due to the loss of hyperbolicity. In these cases, the associated eigenvalues become complex numbers and the model loses physical meaning locally. This paper presents a numerical study of the 1991’s version of the software OLGA, for an isothermal flow of stratified pattern, in a horizontal pipeline. For each point of interest in the stratified-pattern flow map, the eigenvalues are numerically calculated in order to verify if the eigenvalues are real and also to assess their signs. The results indicate that the model is conditionally hyperbolic and loses hyperbolicity in a vast area of the stratified region under certain flow conditions. Even though the model is not unconditionally hyperbolic, some simulations here performed for typical offshore pipeline flows are shown to be in the hyperbolic region.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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