New Approach to Stiff-String Torque and Drag Modeling for Well Planning

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Mayowa Oyedere ◽  
K. E. Gray
Keyword(s):  
Horizontal Wells ◽  
String Model ◽  
Drill Bit ◽  
Shear Forces ◽  
New Approach ◽  
Linear Ordinary Differential Equations ◽  
Contact Loads ◽  
Simplifying Assumptions ◽  
Torque Values ◽  
Rotational Power

Abstract During the process of drilling, the drillstring inadvertently comes in contact with the wellbore generating frictional losses in rotating moment (torque) and axial force (drag). These losses reduce the rotational power available at the drill bit, thus making adequate torque and drag modeling a critical piece in the drilling puzzle. The simplifying assumptions of the widely used soft-string model for torque and drag modeling make it less accurate for new complex well designs, therefore creating the need for the use of the more robust stiff-string model. This work focuses on a new approach for developing a stiff-string model that can be easily implemented for well planning. The stiff-string model addresses the pitfalls of the soft-string model by using cubic splines for its well-path trajectory and solving three coupled, non-linear ordinary differential equations that describe the motion of the drillstring at each survey point to account for the shear forces and bending stiffness. The stiff-string model is then applied to design 4 horizontal wells. In comparison with the soft-string model, results show that the stiff-string model is able to capture the extra contact loads as the drillstring goes through bends, consequently predicting higher hookload and torque values than the soft-string model. This paper also highlights how both models can be applied to well planning for improved results.

A New Approach for Optimization of Long Horizontal Wells’ Performances

2015 ◽  
Author(s):  
Abdullah Al Qahtani ◽  
Hasan Al Hashim ◽  
Hasan Al Yousef
Keyword(s):  
Horizontal Wells ◽  
New Approach

Research of actual information on well casing using machine learning and neural networks

2021 ◽  
pp. 108-119
Author(s):  
D. V. Shalyapin ◽  
D. L. Bakirov ◽  
M. M. Fattahov ◽  
A. D. Shalyapina ◽  
V. G. Kuznetsov
Keyword(s):  
Machine Learning ◽  
Probabilistic Models ◽  
Western Siberia ◽  
Network Modeling ◽  
Horizontal Wells ◽  
Categorical Variables ◽  
Neural Network Modeling ◽  
New Approach ◽  
Attachment Process

In domestic and world practice, despite the measures applied and developed to improve the quality of well casing, there is a problem of leaky structures in almost 50 % of completed wells. The study of actual data using classical methods of statistical analysis (regression and variance analyses) doesn't allow us to model the process with sufficient accuracy that requires the development of a new approach to the study of the attachment process. It is proposed to use the methods of machine learning and neural network modeling to identify the most important parameters and their synergistic impact on the target variables that affect the quality of well casing. The formulas necessary for translating the numerical values of the results of acoustic and gamma-gamma cementometry into categorical variables to improve the quality of probabilistic models are determined. A database consisting of 93 parameters for 934 wells of fields located in Western Siberia has been formed. The analysis of fastening of production columns of horizontal wells of four stratigraphic arches is carried out, the most weighty variables and regularities of their influence on target indicators are established. Recommendations are formulated to improve the quality of well casing by correcting the effects of acoustic and gamma-gamma logging on the results.

scholarly journals Glacial lake drainage: a stability analysis

1997 ◽  
Vol 24 ◽  
pp. 175-180
Author(s):  
Krzysztof Szilder ◽  
Edward P. Lozowski ◽  
Martin J. Sharp
Keyword(s):  
Differential Equations ◽  
Water Flow ◽  
Frictional Heating ◽  
Initial Perturbation ◽  
Tunnel Wall ◽  
Cross Sectional ◽  
Linear Ordinary Differential Equations ◽  
Simplifying Assumptions ◽  
Lake Drainage ◽  
The Stability

A model has been formulated to determine the stability regimes for water flow in a Subglacial conduit draining from a reservoir. The physics of the water flow is described with a set of differential equations expressing conservation of mass, momentum and energy. Non-steady flow of water in the conduit is considered, the conduit being simultaneously enlarged by frictional heating and compressed by plastic deformation in response to the pressure difference across the tunnel wall. With the aid of simplifying assumptions, a mathematical model has been constructed from two time-dependent, non-linear, ordinary differential equations, which describe the time evolution of the conduit cross-sectional area and the water depth in the reservoir. The model has been used to study the influence of conduit area and reservoir levels on the stability of the water flow for various glacier and ice-sheet configurations. The region of the parameter space where the system can achieve equilibrium has been identified. However, in the majority of cases the equilibrium is unstable, and an initial perturbation from equilibrium may lead to a catastrophic outburst of water which empties the reservoir.

A New Approach To Estimating Ultimate Recovery for Multistage Hydraulically Fractured Horizontal Wells by Utilizing Completion Parameters Using Machine Learning

2021 ◽  
pp. 1-16
Author(s):  
Sulaiman A. Alarifi ◽  
Jennifer Miskimins
Keyword(s):  
Oil And Gas ◽  
Horizontal Wells ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
New Approach ◽  
History Data ◽  
Well Completion ◽  
Production History ◽  
Early Production ◽  
Fractured Horizontal Wells

Summary Reserves estimation is an essential part of developing any reservoir. Predicting the long-term production performance and estimated ultimate recovery (EUR) in unconventional wells has always been a challenge. Developing a reliable and accurate production forecast in the oil and gas industry is mandatory because it plays a crucial part in decision-making. Several methods are used to estimate EUR in the oil and gas industry, and each has its advantages and limitations. Decline curve analysis (DCA) is a traditional reserves estimation technique that is widely used to estimate EUR in conventional reservoirs. However, when it comes to unconventional reservoirs, traditional methods are frequently unreliable for predicting production trends for low-permeability plays. In recent years, many approaches have been developed to accommodate the high complexity of unconventional plays and establish reliable estimates of reserves. This paper provides a methodology to predict EUR for multistage hydraulically fractured horizontal wells that outperforms many current methods, incorporates completion data, and overcomes some of the limitations of using DCA or other traditional methods to forecast production. This new approach is introduced to predict EUR for multistage hydraulically fractured horizontal wells and is presented as a workflow consisting of production history matching and forecasting using DCA combined with artificial neural network (ANN) predictive models. The developed workflow combines production history data, forecasting using DCA models and completion data to enhance EUR predictions. The predictive models use ANN techniques to predict EUR given short early production history data (3 months to 2 years). The new approach was developed and tested using actual production and completion data from 989 multistage hydraulically fractured horizontal wells from four different formations. Sixteen models were developed (four models for each formation) varying in terms of input parameters, structure, and the production history data period it requires. The developed models showed high accuracy (correlation coefficients of 0.85 to 0.99) in predicting EUR given only 3 months to 2 years of production data. The developed models use production forecasts from different DCA models along with well completion data to improve EUR predictions. Using completion parameters in predicting EUR along with the typical DCA is a major addition provided by this study. The end product of this work is a comprehensive workflow to predict EUR that can be implemented in different formations by using well completion data along with early production history data.

New Approach. Dual Casing Design For Horizontal Wells (Russian)

2017 ◽  
Author(s):  
N. Bravkova ◽  
A. Truba ◽  
A. Sandutsa ◽  
D. Zadvornov ◽  
O. Grachev ◽  
...  
Keyword(s):  
Horizontal Wells ◽  
New Approach

HORIZONTAL DRILLING, A NEW PRODUCTION METHOD

1988 ◽  
Vol 28 (1) ◽  
pp. 41
Author(s):  
Jacques Bosio
Keyword(s):  
World Wide ◽  
Horizontal Wells ◽  
Production Method ◽  
New Production ◽  
Horizontal Drilling ◽  
New Approach ◽  
Flow Geometry ◽  
The Future ◽  
Deviated Wells

Vertical or deviated wells cross a production formation for only short distances. Horizontal wells, however, are capable of remaining within a reservoir for distances up to several hundred metres in an effort to enhance production possibilities.A world-wide coverage of the method will be presented, with the latest information on drilling, logging, completion and costs.Application of horizontal wells will be described with an emphasis on the production results recently obtained and, more particularly, on the Rospo Mare field, offshore, in the Adriatic Sea.Horizontal wells are providing a new approach to the flow geometry within a production formation. There is no doubt that they will be used more and more in the future to optimise the drainage of most reservoirs.

A New Approach to Improving Oil Lubricity

2007 ◽  
Author(s):  
V. A. Levchenko ◽  
Z. V. Ignatieva ◽  
I. A. Buyanovsky ◽  
V. N. Matveenko
Keyword(s):  
Solid Substrate ◽  
Active Centers ◽  
Carbon Coatings ◽  
New Approach ◽  
Nitrogen Doped ◽  
Molecular Ordering ◽  
Contact Loads ◽  
Uncoated Steel ◽  
Antifriction Properties ◽  
Polycrystalline Structures

A new approach to oil lubricity improving is considered. It bases on changes in molecular structural ordering of adsorbed oil boundary layers governed by a highly oriented solid substrate. Among diamond-like coatings with varying structure there are some ones (with monocrystalline or polycrystalline structures) having orientation effect on molecular ordering of lube oils what leads to decrease in friction coefficient. It is shown that orientating and adsorption effects of carbon coatings can be intensified by additional active centers creation on their surfaces, particularly by nitrogen atoms doping. The tribotests had shown better antifriction properties of nitrogen doped coatings at various contact loads both under dry friction and under friction in lube oils as compared with that of amorphous carbon coating and uncoated steel.

scholarly journals CASCO: a simulator of load paths in 2D frames during progressive collapse

2020 ◽  
Vol 2 (9) ◽  
Author(s):  
Enrico Masoero
Keyword(s):  
Progressive Collapse ◽  
Structural Topology ◽  
Matlab Simulation ◽  
Concrete Frames ◽  
Dynamic Simulations ◽  
New Approach ◽  
Load Paths ◽  
Collapse Resistance ◽  
Simplifying Assumptions ◽  
Damage Tolerant

Abstract Modern structural design software can simulate complex collapse dynamics, but the main physical processes driving collapse propagation are often hidden among structure-specific details. As a result, it is still unclear which structural geometries and material properties should be preferred when approaching the design of a damage-tolerant structure. This manuscript presents a new approach to explore the relationships between structural geometry, local mechanical properties, and collapse propagation. The insight comes from a unique ability to trace the evolution of load paths during collapse, achieved by combining energy conservation with local mechanisms of plastic failure and a few simplifying assumptions. The method is implemented in a new simulator of collapse of 2D frames, called CASCO and programmed in MATLAB. Simulation results for reinforced concrete frames predict collapse loads and mechanisms in agreement with fully non-linear, dynamic simulations, while also providing a graphical description of the evolving structural topology during collapse. A first application of CASCO to mechanically homogeneous and heterogeneous frames, indicates certain evolutions in number and density of load paths during collapse that may be targetted to improve collapse resistance.

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