Application of BP Neural Network in Oil Field Production Prediction

2010 ◽  
Author(s):  
Lei Sun ◽  
Yange Bi ◽  
Guorong Lu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Oil Field ◽  
Field Production ◽  
Production Prediction

BP Neural Network Based Animation Production Prediction

2014 ◽  
Vol 539 ◽  
pp. 475-478
Author(s):  
Ran Tao ◽  
Da Chao Yuan ◽  
Gang Yi Hu
Keyword(s):  
Neural Network ◽  
Relative Error ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Coefficient Of Determination ◽  
Mean Relative Error ◽  
Training Samples ◽  
The Mean ◽  
Production Prediction

In order to research the basic condition of animation production, this article chooses BP Neural Network to predict the animation production. We select 13 test samples, selected nine of them randomly as training samples, and the remaining four as the test samples. The coefficient of determination is 0.99839 and the mean relative error is 0.186125. The result shows that BP Neural Network is an effective prediction method.

scholarly journals Permeability Prediction of Tight Sandstone Reservoirs Using Improved BP Neural Network

2015 ◽  
Vol 8 (1) ◽  
pp. 288-292 ◽  
Author(s):  
Peng Zhu ◽  
Chengyan Lin ◽  
Peng Wu ◽  
Ruifeng Fan ◽  
Hualian Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Water Saturation ◽  
Controlling Factors ◽  
Convergence Speed ◽  
Oil Field ◽  
Tight Sandstone ◽  
Irreducible Water Saturation

By analyzing the permeability controlling factors of tight sandstone reservoir in Wuhaozhuang Oil Field, the permeability is considered to be mainly controlled by porosity, clay content, irreducible water saturation and diagenetic coefficient. Because the conventional BP algorithm has its drawbacks such as slow convergence speed and easy falling into the local minimum value, an improved three-layer feed-forward BP neural network model is built by MATLAB neural network toolbox to predict permeability according to the four permeability controlling factors, while studying samples of model are selected based on the representative core analysis data. The simulation based on improved neural network model shows that the improved model has a faster convergence speed and better accuracy. The consistency between model prediction value and lab test value is good and the mean squared error is less. Therefore, the new model can meet the needs of the development geology research of oil field better in the future.

Pressure Prediction Technology of the Deep Strata Based on BP Neural Network

2010 ◽  
Vol 143-144 ◽  
pp. 28-31 ◽  
Author(s):  
Wei Li ◽  
Tie Yan ◽  
Ying Jie Liang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Gamma Ray ◽  
Forecast Model ◽  
Oil Field ◽  
The Neural Network ◽  
Natural Gamma ◽  
Gamma Ray Log ◽  
Deep Strata ◽  
Very High

. The accurate prediction of strata pressure is the base for safely, quality and efficiently drilling, decreasing hole problems and reasonable development of the reservoir. Because of the high cost, long cycle of the formation pressure measured method, which may influence the safety of drilling operation, thus a new method for predicting strata pressure, based on the BP neural network, is presented in this paper, and establishing process of the neural network forecast model are discussed in detail. This method takes the acoustic time, natural potential, natural gamma ray log data and pipe pressure test data as study sample, which has a very high accuracy. The paper predicts strata pressure of the Saertu oil field and Xingshugang oil field in Daqing, and the results show that relative error between the predicted data and experimental data is less than ±8.9%.

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.

scholarly journals Prediction of Coalbed Methane Production Based on BP Neural Network

2019 ◽  
Vol 131 ◽  
pp. 01059
Author(s):  
Tianxiang Zhang ◽  
Yifang Tang ◽  
Jianjun Wu ◽  
Zixi Guo ◽  
Bing Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Methane Production ◽  
Coalbed Methane ◽  
Coal Bed ◽  
Coal Bed Methane ◽  
Correlation Degree ◽  
Coalbed Methane Production ◽  
Exploration And Development ◽  
Production Prediction

The low average daily gas production per well and the poor economic benefit of exploration and development have become the main problems restricting the exploration and development of coalbed methane in China. Combining multiple coal seam geological parameters to predict the high-yield area of the block can not only provide guidance for the exploitation of coal-bed methane, but also bring enormous economic benefits. Aiming at the difficulty of coalbed methane dessert discrimination and production prediction, a method of coal-bed methane production prediction based on BP neural network is proposed in this paper. Starting from the average daily production of coalbed methane single well, we use the method of grey correlation degree to get the main controlling factors of coalbed methane production. For the main control factors, we use BP neural network with high fitting accuracy and get a good prediction result.

Expert System for Acidizing Based on BP Neural Network

2012 ◽  
Vol 548 ◽  
pp. 438-443
Author(s):  
Heng Xue ◽  
Ping Li Liu ◽  
Nian Yin Li ◽  
Zhi Feng Luo ◽  
Li Qiang Zhao
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Bp Neural Network ◽  
Petroleum Industry ◽  
Field Application ◽  
Oil Field ◽  
Mathematical Technique ◽  
Huge Amount ◽  
The Neural Network ◽  
Training Samples

The technique of acidizing stimulation is one of the most critical measures in petroleum industry to enhance production. As acidizing technique being an integrated course which combines science, practice and experience in one, it cannot be explained by mathematical technique precisely. For conventional acidizing, the workload is extremely huge and complicated, since it has built an extensive database with the help of a huge amount of the application samples. The Neural Network has the generalization ability, which not only has the most consistency with training samples, but also is a dependable network for predication of test samples, whose data distribution is similar to the previous ones. Expert system for acidizing based on the BP Neural Networks can predict a favorable acidizing fluids system and suitable dosage reasonably, effectively and accurately with a large pool of initial input parameters. Thereby this expert system can assist field application and realize the systematization and intelligence in oil field.

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