Calculation Method of Reasonable Water Injection Rate in Different Water Cut Periods of Bp Neural Network Flooding Oilfield

2022 ◽  
pp. 1439-1445
Author(s):  
Liu Xin
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Calculation Method ◽  
Water Injection ◽  
Injection Rate ◽  
Water Cut ◽  
Network Flooding

Applying BP Neural Network to Determine Proper Water Injection Intensity of Water Drive Oil Field

2013 ◽  
Vol 734-737 ◽  
pp. 1358-1361
Author(s):  
Wu Yi Shan ◽  
Wei Lin Cui ◽  
Yong Sheng Li
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Water Injection ◽  
Injection Rate ◽  
Influential Factors ◽  
Oil Field ◽  
Well Water ◽  
Network Data ◽  
Single Well

In this paper, combining previous research on methods of determining water injection rate, dividing coefficient is introduced into this process. Influential factors of water injection rate are also taken into consideration. Based on those theories mentioned above, an analysis on determining dividing coefficient is made by applying BP neural network. Data from one particular year of exploitation was chosen to build up a neural network model between the dividing coefficient and other factors to determine the dividing coefficient, and then single well water injection rate was determined.

Optimization of the Injection Rate of Well Group Water Injection Using BP Neural Network Method

2013 ◽  
Vol 734-737 ◽  
pp. 1219-1225
Author(s):  
Yun Lin Jia ◽  
Jin Cheng Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Water Injection ◽  
Injection Rate ◽  
Neural Network Method ◽  
Network Method ◽  
Development Data ◽  
Group Water ◽  
Single Well ◽  
One Year

This page considers split methods on well group water injection rate researched by predecessors. So the dividing coefficient is introduced in determining the proper well group water injection. Based on considering all factors that affect the well group water injection, it analyzed the factors that were considered in the course of determining well group split coefficient. It adopted BP neural network method to determine well group split coefficient. Choosing the development data of one year, it established the neural network model about split coefficient and other values to determine well group split coefficient. Then it obtained the single well water injection rate.

On-Line Calculation Method of Exhaust Steam Humidity Based on BP Neural Network for Steam Turbine

2013 ◽  
Author(s):  
Likun Zheng ◽  
Chang Chen ◽  
Danmei Xie ◽  
Hengliang Zhang ◽  
Yanzhi Yu
Keyword(s):  
Neural Network ◽  
Steam Turbine ◽  
Bp Neural Network ◽  
Calculation Method ◽  
Thermal Power ◽  
Engineering Application ◽  
Measuring Method ◽  
Factors Affecting ◽  
On Line ◽  
Turbine Exhaust

For condensing turbine, steam exhaust point is in wet steam area. The exhaust steam humidity of steam turbine is difficult to get due to lacking of effective measuring method. Calculation of exhaust steam humidity has always been one of the key parts of the analysis of thermal power units. The main factors affecting exhaust steam humidity are turbine load and turbine exhaust pressure etc, and they are of non-linearity. This paper develops a calculation method to calculate exhaust steam humidity based on BP neural network. Taking a N1000-25/600/600 ultra-supercritical (USC) steam turbine as an example, the exhaust steam humidity is calculated and the results show that the method has a good accuracy to meet the needs of the engineering application.

Research on Reliability of Urban Agglomeration Transport System after a Disaster

2014 ◽  
Vol 496-500 ◽  
pp. 2989-2995
Author(s):  
Zheng Mao Wei ◽  
Xiang Li Zou ◽  
Min Li ◽  
Wei Li ◽  
Cheng Bing Li
Keyword(s):  
Neural Network ◽  
Transport System ◽  
Bp Neural Network ◽  
Calculation Method ◽  
Road Network ◽  
Network Reliability ◽  
Urban Transport ◽  
Urban Agglomeration ◽  
Rate Analysis ◽  
Extent Of Damage

In view of the phenomenon that the affected area is broader, more severe losses and more difficult to relief in the urban agglomeration after major natural disasters,it is proposed that transport system reliability calculation method which based on BP neural network model.First of all,analyze the urban transport system network after a disaster, using the weight contribution rate analysis method to extract the key nodes in the network; Second, re-integrating the extracted nodes and establishing a new road network model which based on BP neural network;Then, using the cut set algorithm computing network reliability, and combined with the extent of damage of the road network after a disaster, putting forward the calculation method of urban agglomeration road network reliability after disasters; Finally, for example as changsha-zhuzhou-xiangtan urban agglomeration, examining the authenticity of the method.

Applying Analytic Hierarchy Process to Determine Separate Layer Water Injection Rate of Single Well

2013 ◽  
Vol 734-737 ◽  
pp. 1354-1357
Author(s):  
Wu Yi Shan ◽  
Wei Lin Cui ◽  
Yong Sheng Li
Keyword(s):  
Analytic Hierarchy Process ◽  
Water Injection ◽  
Injection Rate ◽  
Oil Field ◽  
Analytic Hierarchy ◽  
Practical Applications ◽  
Separate Layer ◽  
Single Well ◽  
Water Cut ◽  
Hierarchy Process

The separate layer water injection rate determines the result of water injection. In the past, in order to inject a proper amount of water into different intervals, most of the time people would rely on their experiences. By employing this method, the amount of work was enormous and the result was not necessarily accurate. In this paper, five factors that would affect the water injection rate are taken into consideration, such as, thickness of the main well, the connected thickness, permeability, number of connected wells and oil saturation. A method of determining the proper water injection rate of single wells based on analytic hierarchy process is proposed. This method has been proven to be simple and accurate, the test result of a mathematical model also shows it fits the requirements of the oil field exploitation with current water cut and it is of great value to practical applications.

A Field Pilot of Waterflooding Conformance Control in Tight Oil Reservoir with Biotechnology

2021 ◽  
Author(s):  
Songyuan Liu ◽  
Xiaochun Jin ◽  
Deji Liu ◽  
Hao Xu ◽  
Lidong Zhang ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Water Injection ◽  
Vertical Direction ◽  
Cost Effective ◽  
Injection Rate ◽  
Recovery Factor ◽  
Tight Oil ◽  
Index Test ◽  
Indigenous Bacteria ◽  
Water Cut

Abstract Traditional Microbial Enhanced Oil Recovery (MEOR) technology assumes the oil recovery is increased by the biosurfactant generating by the subsurface bacteria. However, we identified that increased recovery factor is mainly contributed by stimulating the indigenous bacteria to plug the preferred waterflooding channels, which was proved at laboratory and some high-permeable oilfield, but never implemented in the waterflooding of tight oilfield. This paper presents a comprehensive study on Bio-diversion technique by stimulating indigenous bacteria covering lab research and filed operation lasting 18 months. The lab research comprised: (1) feasibility research using modified recipe and field sample on the stimulation of indigenous microorganisms; and (2) Evaluation of effectiveness of the stimulation based on lab results. A field pilot, consisting of 10 injectors, 10 producers, injecting and producing from multi-zones, reservoir temperature is about 160 F, permeabilities range from 30 md to over 100 md, daily water injection rate is about 2,000 BWPD, pre-treatment water cut is over 90%. It is observed that the water cut has decreased from 98% to 80% gradually (3-6 months after injection). Besides, the water injection index test indicates that the injection profile becomes more evenly after 9 months of microbial nutrient injection because the stimulated bacteria reduce the permeability of more permeable zones and reduce the permeability heterogeneity in the vertical direction. Sharing the field results with the industry may inspire the operators to consider one alternative environmentally friendly and cost-effective approach to increase the recovery factor of tight oil reservoirs. From the technical viewpoint, the field pilot proves that the major mechanisms of MEOR is sweeping the unswept oil by injecting the microbial nutrient to the reservoir to stimulate the indigenous bacteria to block the preferred waterflooding channels.

Sign in / Sign up

Export Citation Format

Share Document