An Approach for Predicting the Effective Stress Field in Low-Permeability Reservoirs Based on Reservoir-Geomechanics Coupling

Liu, Yuyang; Zhang, Xiaowei; Guo, Wei; Kang, Lixia; Yu, Rong; Sun, Yize

doi:10.3390/pr10040633

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Yuyang Liu et al proposed a method for calculating effective stress in low Permeability (K) reservoirs by combining the mechanical and flow models in 2022. The results show that by using 3D geological models paired with various numerical approaches, the proposed methods may be utilized to create the effective stress (σe) and pore pressure fields in a reservoir and then forecast the σe value in the reservoir [42]. When some researchers utilize this technology, it begins to be employed in the oil fields of Iraq.…”

Section: -Reservoir Finite Element Techniquesmentioning

confidence: 99%

Application of Finite Element Technique: A Review Study

Faraj

Hussein

2023

IJCPE

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The finite element approach is used to solve a variety of difficulties, including well bore stability, fluid flow production and injection wells, mechanical issues and others. Geomechanics is a term that includes a number of important aspects in the petroleum industry, such as studying the changes that can be occur in oil reservoirs and geological structures, and providing a picture of oil well stability during drilling. The current review study concerned about the advancements in the application of the finite element method (FEM) in the geomechanical field over a course of century. Firstly, the study presented the early advancements of this method by development the structural framework of stress, make numerical computer solution for 2D thermal stress then stress analysis of the airplane. The second part focused on the most recent developments of FEM, and this method generates new techniques for solving these problems, such as the 1D, 2D, and 3D finite element models; the dynamic program method (DPM); the finite discrete element method (FDEM); and the finite element extended method (FEXM). The third part of this study presented the reservoir finite element simulation used for injection well testing inside unconsolidated oil sand reservoirs. Also improvement of the FE software program for the analyses, finite element extended approach to convert a 3D fault model were introduced. In addition, the study explored the development of a 3D and 4D model utilizing Visage for FEM analysis for geomechanics investigations, and the software eclipse for pressure drop prediction in carbonate reservoir weak formation and presented the Finite-Element Smoothed Particle Method (FESPM).

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Section: -Reservoir Finite Element Techniquesmentioning

confidence: 99%

Application of Finite Element Technique: A Review Study

Faraj

Hussein

2023

IJCPE

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“…Nowadays, due to the depletion of "traditional" reserves, the share of oil extracted from weakly cemented reservoirs with high viscosity is gradually increasing [1,2]. Therefore, the main difficulty in the development of such reservoirs is the large number of failures associated with the penetration of sand into the deep well pumping (DWP) system.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Impurities Carry-Over from Horizontal Heavy Oil Production Well

Dengaev,

Shishulin,

Drozdov

et al. 2023

Processes

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Currently, a global attention has been paid to the development and exploitation of shallow depth heavy oil reservoirs. Such oilfields are rationally developed via a network of horizontal wells. However, the weakly cemented reservoir rock beds lead to the high sand production during well flow. Removing mechanical impurities is still challenging even with the application of sophisticated techniques and a variety of filters. In this study, we propose an analysis method for describing the removal of mechanical impurity particles from horizontal wells containing heavy oil. We employed a simulation model of typical well in OLGA program, and several calculations was made for different values of the flow rate. Moreover, deep sand samples were isolated from a well of Uchebny field to compare the real data with the estimated one. Calculations are used to estimate the quality of mechanical impurity removal for various diameters, and a relationship between the critical diameter and well flow rate is built.

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“…Many researchers have applied AI technology to logging, physical exploration, drilling and completion, reservoir engineering, CCUS, etc. [1][2][3][4][5][6][7][8][9][10][11]. However, there are still some problems, such as the small sample size, which cannot meet the requirements of deep learning, the difficulty of algorithm exploration, and application [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Research on the Construction Method of a Training Image Library Based on cDCGAN

Yao,

Liu,

Pan

2023

Applied Sciences

Self Cite

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There is a close relationship between the size and property of a reservoir and the production and capacity. Therefore, in the process of oil and gas field exploration and development, it is of great importance to study the macro distribution of oil–gas reservoirs, the inner structure, the distribution of reservoir parameters, and the dynamic variation of reservoir characteristics. A reservoir model is an important bridge between first-hand geologic data and other results such as ground stress models and fracture models, and the quality of the model can influence the evaluation of the sweet spots, the deployment of a horizontal well, and the optimization of the well network. Reservoir facies modeling and physical parameter modeling are the key points in reservoir characterization and modeling. Deep learning, as an artificial intelligence method, has been shown to be a powerful tool in many fields, such as data fusion, feature extraction, pattern recognition, and nonlinear fitting. Thus, deep learning can be used to characterize the reservoir features in 3D space. In recent years, there have been increasing attempts to apply deep learning in the oil and gas industry, and many scholars have made attempts in logging interpretation, seismic processing and interpretation, geological modeling, and petroleum engineering. Traditional training image construction methods have drawbacks such as low construction efficiency and limited types of sedimentary facies. For this purpose, some of the problems of the current reservoir facies modeling are solved in this paper. This study constructs a method that can quickly generate multiple types of sedimentary facies training images based on deep learning. Based on the features and merits of all kinds of deep learning methods, this paper makes some improvements and optimizations to the conventional reservoir facies modeling. The main outcomes of this thesis are as follows: (a) the construction of a training image library for reservoir facies modeling is realized. (b) the concept model of the typical sedimentary facies domain is used as a key constraint in the training image library. In order to construct a conditional convolutional adversarial network model, One-Hot and Distributed Representation is used to label the dataset. (c) The method is verified and tested with typical sedimentary facies types such as fluvial and delta. The results show that this method can generate six kinds of non-homogeneous and homogeneous training images that are almost identical to the target sedimentary facies in terms of generation quality. In terms of generating result formats, compared to the cDCGAN training image generation method, traditional methods took 31.5 and 9 times longer. In terms of generating result formats, cDCGAN can generate more formats than traditional methods. Furthermore, the method can store and rapidly generate the training image library of the typical sedimentary facies model of various types and styles in terms of generation efficiency.

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An Approach for Predicting the Effective Stress Field in Low-Permeability Reservoirs Based on Reservoir-Geomechanics Coupling

Cited by 5 publications

References 28 publications

Application of Finite Element Technique: A Review Study

Application of Finite Element Technique: A Review Study

Mechanical Impurities Carry-Over from Horizontal Heavy Oil Production Well

Research on the Construction Method of a Training Image Library Based on cDCGAN

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