Artificial Neural Networks Identification of Lithology-Types in Complex Carbonate from Well Logs, Block K, in Uzbekistan

2013 ◽  
Vol 756-759 ◽  
pp. 2396-2400 ◽  
Author(s):  
Ya Ping Lin ◽  
Tie Zhuang Wu ◽  
Xiao Feng Sheng ◽  
Chun Sheng Wang ◽  
Tian Wei Zhou
Keyword(s):  
Quantitative Evaluation ◽  
Carbonate Rocks ◽  
Oil And Gas ◽  
Positive Role ◽  
Oil And Gas Exploration ◽  
Response Characteristics ◽  
Lithology Identification ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural ◽  
Mud Logging

In oil and gas exploration of Block K in Amu Darya basin Uzbekistan, the reservoir lithologies are mainly in different carbonate rocks, the more types of rocks, the more various reservoir space is, as a result, it brings some difficulties to the reservoir quantitative evaluation. Therefore, according to this situation that the difficulty in identification of complex carbonate lithologies is, in this study block, artificial neural network analysis method is used in this paper. The method combines mud logging, cutting, core data, well logging, studies logging response characteristics of the different types of carbonate rocks, establishes lithology identification index. In this study, the method is used in identifying the types of carbonate rocks, the identified result compared to actual rocks displays about 70.51~87.23%, and it plays the positive role for reservoir quantitative evaluation.

scholarly journals Formation environments and mechanisms of multistage paleokarst of Ordovician carbonates in Southern North China Basin

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Haitao Zhang ◽  
Guangquan Xu ◽  
Mancai Liu ◽  
Minhua Wang
Keyword(s):  
Carbonate Rocks ◽  
North China ◽  
Oil And Gas ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Geochemical Data ◽  
Near Surface ◽  
Oil And Gas Exploration ◽  
Pore Types ◽  
North China Basin

AbstractWith the reduction of oil and gas reserves and the increase of mining difficulty in Northern China, the carbonate rocks in Southern North China Basin are becoming a significant exploration target for carbonate reservoirs. However, the development characteristics, formation stages, formation environments and mechanisms of the carbonate reservoirs in Southern North China Basin are still unclear, which caused the failures of many oil and gas exploration wells. This study focused on addressing this unsolved issue from the Ordovician carbonate paleokarst in the Huai-Fu Basin, which is located in the southeast of Southern North China Basin and one of the key areas for oil and gas exploration. Based on petrology, mineralogy and geochemical data, pore types, distribution characteristics, and formation stages of the Ordovician paleokarst were analyzed. Then, in attempt to define the origins of porosity development, the formation environments and mechanisms were illustrated. The results of this study showed that pore types of the Ordovician carbonates in the Huai-Fu Basin are mainly composed of intragranular pores, intercrystalline (intergranular) pores, dissolution pores (vugs), fractures, channels, and caves, which are usually in fault and fold zones and paleoweathering crust. Furthermore, five stages and five formation environments of the Ordovician paleokarst were identified. Syngenetic karst, eogenetic karst, and paleoweathering crust karst were all developed in a relatively open near-surface environment, and their formations are mainly related to meteoric water dissolution. Mesogenetic karst was developed in a closed buried environment, and its formation is mainly related to the diagenesis of organic matters and thermochemical sulfate reduction in the Permian-Carboniferous strata. Hydrothermal (water) karst was developed in a deep-buried and high-temperature environment, where hydrothermal fluids (waters) migrated upward through structures such as faults and fractures to dissolve carbonate rocks and simultaneously deposited hydrothermal minerals and calcites. Lastly, a paleokarst evolution model, combined with the related porosity evolution processes, nicely revealed the Ordovician carbonate reservoir development. This study provides insights and guidance for further oil and gas exploration in the Southern North China Basin, and also advances our understanding of the genesis of carbonate paleokarst around the world.

Machine learning technique for the quantitative evaluation of tight sandstone reservoirs using high-pressure mercury-injection data

2020 ◽  
Author(s):  
JingJing Liu ◽  
JianChao Liu
Keyword(s):  
Machine Learning ◽  
High Pressure ◽  
Quantitative Evaluation ◽  
Oil And Gas ◽  
Tight Sandstone ◽  
Reservoir Evaluation ◽  
Oil And Gas Exploration ◽  
Unconventional Oil ◽  
Unconventional Oil And Gas ◽  
Sandstone Reservoirs

<p>In recent years, China's unconventional oil and gas exploration and development has developed rapidly and has entered a strategic breakthrough period. At the same time, tight sandstone reservoirs have become a highlight of unconventional oil and gas development in the Ordos Basin in China due to its industrial and strategic value. As a digital representation of storage capacity, reservoir evaluation is a vital component of tight-oil exploration and development. Previous work on reservoir evaluation indicated that achieving satisfactory results is difficult because of reservoir heterogeneity and considerable risk of subjective or technical errors. In the data-driven era, this paper proposes a machine learning quantitative evaluation method for tight sandstone reservoirs based on K-means and random forests using high-pressure mercury-injection data. This method can not only provide new ideas for reservoir evaluation, but also be used for prediction and evaluation of other aspects in the field of oil and gas exploration and production, and then provide a more comprehensive parameter basis for “intelligent oil fields”. The results show that the reservoirs could be divided into three types, and the quantitative reservoir-evaluation criteria were established. This method has strong applicability, evident reservoir characteristics, and observable discrimination. The implications of these findings regarding ultra-low permeability and complex pore structures are practical.</p>

scholarly journals Reactive Transport Simulation of Cavern Formation along Fractures in Carbonate Rocks

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 38
Author(s):  
Ruiqi Duan ◽  
Genhua Shang ◽  
Chen Yu ◽  
Qiang Wang ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Carbonate Rocks ◽  
Reactive Transport ◽  
Oil And Gas ◽  
Characteristic Length ◽  
Fracture Network ◽  
Oil And Gas Exploration ◽  
The Matrix ◽  
Complex Fracture Network ◽  
Matrix Porosity ◽  
Porosity Heterogeneity

Karst cavities and caves are often present along fractures in limestone reservoirs and are of significance for oil and gas exploration. Understanding the formation and evolution of caves in fractured carbonate rocks will enhance oil and gas exploration and development. Herein, a reactive transport model was established considering both the matrix and fractures. Different factors affecting the dissolution along fractures were considered in the simulation of matrix–fracture carbonate rocks, including the magnitude and characteristic length of the matrix porosity heterogeneity, intersecting fractures, and complex fracture network. The results show that a strong heterogeneity of the matrix porosity significantly affects the cave formation along the fracture and the existence of fractures increases the heterogeneity due to the high permeability as well as the dissolution area. The characteristic length of the matrix porosity heterogeneity affects the cave location and shape. The larger permeability of intersecting fractures or the matrix greatly increases the cave size, leading to the formation of large, connected cave areas. A complex fracture network leads to more developed karst dissolution caves. The topology of the fracture network and preferential flow dominate the distribution of caves and alleviate the effect of the matrix heterogeneity.

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Control System Design of the Vehicle Mounted Hammer Source Based on PLC

2012 ◽  
Vol 619 ◽  
pp. 302-305
Author(s):  
Hong Yan Wang ◽  
Wen Sheng Xiao ◽  
Xiu Juan Lin ◽  
Xian Feng Wang
Keyword(s):  
Control System ◽  
System Design ◽  
Software Design ◽  
High Velocity ◽  
Simple Structure ◽  
Oil And Gas ◽  
Basic Structure ◽  
Control System Design ◽  
Oil And Gas Exploration ◽  
Gas Consumption

Considering the pollution on the environment using dynamite source in oil and gas exploration, harm and damage to people and building, the vehicle mounted hammer source which can replace dynamite source is presented. This paper describes briefly the basic structure and working principles of the vehicle mounted hammer source. A typical pneumatic circuit is researched and designed. And the pneumatic circuit is designed with the powerful functions of PLC, the hardware and software design are introduced. The system has advantages of strong striking force, high velocity, small gas consumption, simple structure and convenient control.

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