Hydraulic Fracturing Model Based on a Three-Dimensional Closed Form: Tests and Analysis of Fracture Geometry and Containment

1988 ◽  
Vol 3 (04) ◽  
pp. 445-454 ◽  
Author(s):  
M.J. Bouteca
Keyword(s):  
Hydraulic Fracturing ◽  
Closed Form ◽  
Three Dimensional ◽  
Fracture Geometry ◽  
Model Based

A novel three-dimensional hydraulic fracturing model based on continuum–discontinuum element method

2021 ◽  
Vol 383 ◽  
pp. 113887
Author(s):  
Xinguang Zhu ◽  
Chun Feng ◽  
Pengda Cheng ◽  
Xinquan Wang ◽  
Shihai Li
Keyword(s):  
Hydraulic Fracturing ◽  
Three Dimensional ◽  
Model Based ◽  
Element Method

scholarly journals Multi-Size Proppant Pumping Schedule of Hydraulic Fracturing: Application to a MP-PIC Model of Unconventional Reservoir for Enhanced Gas Production

Processes ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 570 ◽  
Author(s):  
Prashanth Siddhamshetty ◽  
Shaowen Mao ◽  
Kan Wu ◽  
Joseph Sang-Il Kwon
Keyword(s):  
Hydraulic Fracturing ◽  
Shale Gas ◽  
Computational Cost ◽  
Three Dimensional ◽  
Sensitivity Study ◽  
Gas Production ◽  
Field Scale ◽  
Fracture Geometry ◽  
Fracture Conductivity ◽  
Proppant Transport

Slickwater hydraulic fracturing is becoming a prevalent approach to economically recovering shale hydrocarbon. It is very important to understand the proppant’s transport behavior during slickwater hydraulic fracturing treatment for effective creation of a desired propped fracture geometry. The currently available models are either oversimplified or have been performed at limited length scales to avoid high computational requirements. Another limitation is that the currently available hydraulic fracturing simulators are developed using only single-sized proppant particles. Motivated by this, in this work, a computationally efficient, three-dimensional, multiphase particle-in-cell (MP-PIC) model was employed to simulate the multi-size proppant transport in a field-scale geometry using the Eulerian–Lagrangian framework. Instead of tracking each particle, groups of particles (called parcels) are tracked, which allows one to simulate the proppant transport in field-scale geometries at an affordable computational cost. Then, we found from our sensitivity study that pumping schedules significantly affect propped fracture surface area and average fracture conductivity, thereby influencing shale gas production. Motivated by these results, we propose an optimization framework using the MP-PIC model to design the multi-size proppant pumping schedule that maximizes shale gas production from unconventional reservoirs for given fracturing resources.

Application of Deep Learning for Planar 3D Hydraulic Fracturing Simulation

2021 ◽  
Author(s):  
Victoria Dochkina ◽  
Ilia Perepechkina ◽  
Natalia Zavialova ◽  
Sergei Negodiaev
Keyword(s):  
Neural Network ◽  
Hydraulic Fracturing ◽  
Three Dimensional ◽  
Accurate Estimation ◽  
Fracture Aperture ◽  
Fracture Geometry ◽  
Simulation And Optimization ◽  
Wide Range ◽  
Optimal Injection ◽  
Production Problems

<p><strong>    </strong>Nowadays, none of widely used hydraulic fracturing simulators can simultaneously provide high calculation speed and sufficient physical reliability, which is crucial in engineering problems. Hence, an optimization of hydraulic fracturing simulation in terms of speed and accuracy is needed. It is possible to create a tool that will simultaneously solve the above-mentioned problems using Machine Learning methods. In that case, the simulation will have an accuracy close to the Planar3D model and almost instantaneous speed of calculation. The development of such a tool will simplify a selection of optimal injection parameters.<br>    This paper presents a Neural Network that approximates a planar three-dimensional hydraulic fracturing model. A feature of the proposed approximator is that it predicts the evolution of two-dimensional fracture aperture field. This is a key difference of this model from other approximators that predict well-defined parameters of the fracture geometry, such as half-length, height, etc. The availability of complete fracture geometry information allows highly accurate estimation of production and possible complications during hydraulic fracturing.<br>    The paper presents an ability of creating a Neural Network that will cover a wide range of production problems: from express simulation and optimization to accurate and physically reliable modeling.</p>

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