The Ising limit of the double‐well model

1980 ◽  
Vol 21 (4) ◽  
pp. 881-890 ◽  
Author(s):  
Florin Constantinescu ◽  
Berthold Ströter
Keyword(s):  
Well Model ◽  
Ising Limit

scholarly journals Sharp tunneling estimates for a double-well model in infinite dimension

2021 ◽  
Vol 281 (3) ◽  
pp. 109029
Author(s):  
Morris Brooks ◽  
Giacomo Di Gesù
Keyword(s):  
Infinite Dimension ◽  
Well Model

Photoluminescence of ozone oxidized and HF etched porous silicon and the multiple source quantum well model

1997 ◽  
Vol 71 (10) ◽  
pp. 1391-1393 ◽  
Author(s):  
L. Jia ◽  
S. P. Wong ◽  
I. H. Wilson ◽  
S. K. Hark ◽  
S. L. Zhang ◽  
...  
Keyword(s):  
Porous Silicon ◽  
Quantum Well ◽  
Multiple Source ◽  
Well Model

A Back Allocation Methodology to Estimate the Real-Time Flow and Assist Production Monitoring

2021 ◽  
Author(s):  
Gabriela Chaves ◽  
Danielle Monteiro ◽  
Virgilio José Martins Ferreira
Keyword(s):  
Real Time ◽  
Input Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Time Flow ◽  
Well Model ◽  
Operational Information ◽  
The Individual

Abstract Commingle production nodes are standard practice in the industry to combine multiple segments into one. This practice is adopted at the subsurface or surface to reduce costs, elements (e.g. pipes), and space. However, it leads to one problem: determine the rates of the single elements. This problem is recurrently solved in the platform scenario using the back allocation approach, where the total platform flowrate is used to obtain the individual wells’ flowrates. The wells’ flowrates are crucial to monitor, manage and make operational decisions in order to optimize field production. This work combined outflow (well and flowline) simulation, reservoir inflow, algorithms, and an optimization problem to calculate the wells’ flowrates and give a status about the current well state. Wells stated as unsuited indicates either the input data, the well model, or the well is behaving not as expected. The well status is valuable operational information that can be interpreted, for instance, to indicate the need for a new well testing, or as reliability rate for simulations run. The well flowrates are calculated considering three scenarios the probable, minimum and maximum. Real-time data is used as input data and production well test is used to tune and update well model and parameters routinely. The methodology was applied using a representative offshore oil field with 14 producing wells for two-years production time. The back allocation methodology showed robustness in all cases, labeling the wells properly, calculating the flowrates, and honoring the platform flowrate.

Connecting Data, Model and Process to Streamline and Enhance Single-Point Production Well Test Validation

2021 ◽  
Author(s):  
Mohamad Mustaqim Mokhlis ◽  
Nurdini Alya Hazali ◽  
Muhammad Firdaus Hassan ◽  
Mohd Hafiz Hashim ◽  
Afzan Nizam Jamaludin ◽  
...  
Keyword(s):  
Test Data ◽  
Single Point ◽  
Database Systems ◽  
Test Validation ◽  
Well Test ◽  
Production Well ◽  
Well Test Analysis ◽  
Test Analysis ◽  
Digital Ecosystem ◽  
Well Model

Abstract In this paper we will present a process streamlined for well-test validation that involves data integration between different database systems, incorporated with well models, and how the process can leverage real-time data to present a full scope of well-test analysis to enhance the capability for assessing well-test performance. The workflow process demonstrates an intuitive and effective way for analyzing and validating a production well test via an interactive digital visualization. This approach has elevated the quality and integrity of the well-test data, as well as improved the process cycle efficiency that complements the field surveillance engineers to keep track of well-test compliance guidelines through efficient well-test tracking in the digital interface. The workflow process involves five primary steps, which all are conducted via a digital platform: Well Test Compliance: Planning and executing the well test Data management and integration Well Test Analysis and Validation: Verification of the well test through historical trending, stability period checks, and well model analysis Model validation: Correcting the well test and calibrating the well model before finalizing the validity of the well test Well Test Re-testing: Submitting the rejected well test for retesting and final step Integrating with corporate database system for production allocation This business process brings improvement to the quality of the well test, which subsequently lifts the petroleum engineers’ confidence level to analyze well performance and deliver accurate well-production forecasting. A well-test validation workflow in a digital ecosystem helps to streamline the flow of data and system integration, as well as the way engineers assess and validate well-test data, which results in minimizing errors and increases overall work efficiency.

Comparative analysis of production potential in Bulla-deniz and Umid gas-condensate fields

2020 ◽  
pp. 21-26
Author(s):  
E.H. Ahmadov ◽  
Keyword(s):  
Oil And Gas ◽  
Reduction Rate ◽  
Chemical Properties ◽  
Gas Production ◽  
Layer System ◽  
Reservoir Properties ◽  
Technical Parameters ◽  
Recovery Curves ◽  
Well Model ◽  
Drilling Technology

The paper studies the reduction rate of gas production in the wells of Bulla-deniz field drilled to VIII horizon. With this purpose, geological (reservoir properties, oil-gas saturation, net thickness, formation pressure and temperature, formation heterogeneity, multi-layer system, tectonic faults, physical-chemical properties of oil and gas etc.) and technological (well structure, measuring and transportation system, well operation regime, drilling technology etc.) conditions of formation were analyzed and the well model of VII and VIII horizons of Bulla-deniz field using these geological and technical parameters developed as well. For the estimation of impact of geological and technical aspects on production, sensitivity analysis was carried out on the models. The suggestions for elaboration of uncertainty of geological and technical parameters affecting production dynamics were developed. To reveal the reasons for production differences of the wells, it was proposed to install borehole manometers, to obtain the data on pressure recovery curves, drainage area, skin-effect impact, permeability and to develop a study plan of bottomhole zone with acid.

Use of Horizontal Drift-Flux Models For Simulating Wellbore Flow in SAGD Operations

2021 ◽  
Author(s):  
Mohammad Heidari ◽  
Christopher Istchenko ◽  
William Bailey ◽  
Terry Stone
Keyword(s):  
Flux Flow ◽  
Pressure Drops ◽  
Steam Assisted Gravity Drainage ◽  
Drift Flux ◽  
Pressure Profiles ◽  
Well Model ◽  
Inclination Angles ◽  
Operation Pressure ◽  
Reservoir Simulator ◽  
High Degree

Abstract The paper examines new horizontal drift-flux correlations for their ability to accurately model phase flow rates and pressure drops in horizontal and undulating wells that are part of a Steam-Assisted Gravity Drainage (SAGD) field operation. Pressure profiles within each well correlate to the overall performance of the pair. SAGD is a low-pressure process that is sensitive to reservoir heterogeneity and other factors, hence accurate simulation of in situ wellbore pressures is critical for both mitigating uneven steam chamber evolution and optimizing wellbore design and operation. Recently published horizontal drift-flux correlations have been implemented in a commercial thermal reservoir simulator with a multi-segment well model. Valid for horizontally drilled wells with undulations, they complement previously reported drift-flux models developed for vertical and inclined wells down to approximately 5 degrees from horizontal. The formulation of these correlations has a high degree of nonlinearity. These models are tested in simulations of SAGD field operations. First, an overview of drift-flux models is discussed. This differentiates those based on vertical flow with gravity segregation to those that model horizontal flow with stratified and slug flow regimes. Second, the most recent and significant drift-flux correlation by Bailey et al. (2018, and hereafter referred to as Bailey-Tang-Stone) was robustly designed to be used in the well model of a reservoir simulator, can handle all inclination angles and was optimized to experimental data from the largest available databases to date. This and earlier drift-flux models are reviewed as to their strengths and weaknesses. Third, governing equations and implementation details are given of the Bailey-Tang-Stone model. Fourth, six case studies are presented that illustrate homogeneous and drift-flux flow model differences for various well scenarios.

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