Accelerated Compositional Simulation of Tight Oil and Shale Gas Reservoirs Using Proxy Flash Calculation

2019 ◽  
Author(s):  
Shihao Wang ◽  
Nicolas Sobecki ◽  
Didier Ding ◽  
Yu-Shu Wu ◽  
Lingchen Zhu
Keyword(s):  
Shale Gas ◽  
Tight Oil ◽  
Compositional Simulation ◽  
Gas Reservoirs ◽  
Shale Gas Reservoirs ◽  
Flash Calculation

Geomechanical Characterisation and Modelling Help to Unlock Shale Gas Reservoirs in the Southern Sichuan Basin

2013 ◽  
Author(s):  
Roger Yuan ◽  
Fa Dwan ◽  
Navpreet Singh ◽  
Liang Jin ◽  
Danny Soo ◽  
...  
Keyword(s):  
Shale Gas ◽  
Sichuan Basin ◽  
Gas Reservoirs ◽  
Shale Gas Reservoirs

scholarly journals Prediction of Shale Gas Reservoirs Using Fluid Mobility Attribute Driven by Post-Stack Seismic Data: A Case Study from Southern China

2020 ◽  
Vol 11 (1) ◽  
pp. 219
Author(s):  
Jing Zeng ◽  
Alexey Stovas ◽  
Handong Huang ◽  
Lixia Ren ◽  
Tianlei Tang
Keyword(s):  
Shale Gas ◽  
Seismic Data ◽  
Spectral Decomposition ◽  
Southern China ◽  
Gas Reservoirs ◽  
Seismic Attribute ◽  
Longmaxi Formation ◽  
Shale Gas Reservoirs ◽  
The Sichuan Basin ◽  
Shale Reservoirs

Paleozoic marine shale gas resources in Southern China present broad prospects for exploration and development. However, previous research has mostly focused on the shale in the Sichuan Basin. The research target of this study is expanded to the Lower Silurian Longmaxi shale outside the Sichuan Basin. A prediction scheme of shale gas reservoirs through the frequency-dependent seismic attribute technology is developed to reduce drilling risks of shale gas related to complex geological structure and low exploration level. Extracting frequency-dependent seismic attribute is inseparable from spectral decomposition technology, whereby the matching pursuit algorithm is commonly used. However, frequency interference in MP results in an erroneous time-frequency (TF) spectrum and affects the accuracy of seismic attribute. Firstly, a novel spectral decomposition technology is proposed to minimize the effect of frequency interference by integrating the MP and the ensemble empirical mode decomposition (EEMD). Synthetic and real data tests indicate that the proposed spectral decomposition technology provides a TF spectrum with higher accuracy and resolution than traditional MP. Then, a seismic fluid mobility attribute, extracted from the post-stack seismic data through the proposed spectral decomposition technology, is applied to characterize the shale reservoirs. The application result indicates that the seismic fluid mobility attribute can describe the spatial distribution of shale gas reservoirs well without well control. Based on the seismic fluid mobility attribute section, we have learned that the shale gas enrich areas are located near the bottom of the Longmaxi Formation. The inverted velocity data are also introduced to further verify the reliability of seismic fluid mobility. Finally, the thickness map of gas-bearing shale reservoirs in the Longmaxi Formation is obtained by combining the seismic fluid mobility attribute with the inverted velocity data, and two favorable exploration areas are suggested by analyzing the thickness, structure, and burial depth. The present work can not only be used to evaluate shale gas resources in the early stage of exploration, but also help to design the landing point and trajectory of directional drilling in the development stage.

Multidisciplinary Approach for Detailed Characterization of Shale Gas Reservoirs: A Netherlands Showcase

2013 ◽  
Author(s):  
Yvonne Anja Schavemaker ◽  
Mart Zijp ◽  
Jan ter Heege ◽  
Susanne Nelskamp ◽  
Johan Ten Veen
Keyword(s):  
Shale Gas ◽  
Multidisciplinary Approach ◽  
Gas Reservoirs ◽  
Detailed Characterization ◽  
Shale Gas Reservoirs

scholarly journals Review of Formation and Gas Characteristics in Shale Gas Reservoirs

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5427
Author(s):  
Boning Zhang ◽  
Baochao Shan ◽  
Yulong Zhao ◽  
Liehui Zhang
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Shale Gas ◽  
Physical Parameters ◽  
Gas Reservoirs ◽  
Shale Formations ◽  
Adsorbed Gas ◽  
Shale Gas Reservoirs ◽  
Longmaxi Shale

An accurate understanding of formation and gas properties is crucial to the efficient development of shale gas resources. As one kind of unconventional energy, shale gas shows significant differences from conventional energy ones in terms of gas accumulation processes, pore structure characteristics, gas storage forms, physical parameters, and reservoir production modes. Traditional experimental techniques could not satisfy the need to capture the microscopic characteristics of pores and throats in shale plays. In this review, the uniqueness of shale gas reservoirs is elaborated from the perspective of: (1) geological and pore structural characteristics, (2) adsorption/desorption laws, and (3) differences in properties between the adsorbed gas and free gas. As to the first aspect, the mineral composition and organic geochemical characteristics of shale samples from the Longmaxi Formation, Sichuan Basin, China were measured and analyzed based on the experimental results. Principles of different methods to test pore size distribution in shale formations are introduced, after which the results of pore size distribution of samples from the Longmaxi shale are given. Based on the geological understanding of shale formations, three different types of shale gas and respective modeling methods are reviewed. Afterwards, the conventional adsorption models, Gibbs excess adsorption behaviors, and supercritical adsorption characteristics, as well as their applicability to engineering problems, are introduced. Finally, six methods of calculating virtual saturated vapor pressure, seven methods of giving adsorbed gas density, and 12 methods of calculating gas viscosity in different pressure and temperature conditions are collected and compared, with the recommended methods given after a comparison.

Triple-continuum modeling of shale gas reservoirs considering the effect of kerogen

2015 ◽  
Vol 24 ◽  
pp. 252-263 ◽  
Author(s):  
Min Zhang ◽  
Jun Yao ◽  
Hai Sun ◽  
Jian-lin Zhao ◽  
Dong-yan Fan ◽  
...  
Keyword(s):  
Shale Gas ◽  
Continuum Modeling ◽  
Gas Reservoirs ◽  
Shale Gas Reservoirs

Brittleness index prediction in shale gas reservoirs based on efficient network models

2016 ◽  
Vol 35 ◽  
pp. 673-685 ◽  
Author(s):  
Xian Shi ◽  
Gang Liu ◽  
Yuanfang Cheng ◽  
Liu Yang ◽  
Hailong Jiang ◽  
...  
Keyword(s):  
Shale Gas ◽  
Network Models ◽  
Brittleness Index ◽  
Gas Reservoirs ◽  
Shale Gas Reservoirs
Sign in / Sign up

Export Citation Format

Share Document