Apply neural network for improving production planning at Samarang petrol mine

2016 ◽  
Vol 9 (2) ◽  
pp. 126-143
Author(s):  
Quoc Trung Pham ◽  
Thi Kim Dung Phan
Keyword(s):  
Neural Network ◽  
Production Planning ◽  
Petroleum Industry ◽  
Allocation Problem ◽  
Production Performance ◽  
Well Test ◽  
Operational Parameters ◽  
Content Type ◽  
Well Production ◽  
Non Linear

Purpose – Artificial neural network (ANN) is considered a good solution for building non-linear relationship between input and output parameters, which is suitable for solving production back allocation, which is the most important step for production planning of petroleum mine. The purpose of this paper is to suggest a solution for solving production back allocation problem at Samarang petrol mine based on ANN approach. Design/methodology/approach – In this study, well operational parameters’ surveillance was conducted and ANN was used to build relationships between operation parameters and production rates. Experimental method is used for testing and evaluating the possibility of using ANN for supporting production planning at Samarang mine. Findings – Consequently, the proposed ANN solution can increase the accuracy of predicted values and could be used for supporting production planning at Samarang mine. Because ANN uses well test data for training and predicting (without adding new devices), it could be a feasible and cheap solution. Research limitations/implications – There is a need for applying other methods, such as: support machine vector, non-linear autoregressive models, etc. for better evaluation of ANN solution. Practical implications – The ANN models helped operation engineers to understand well production performance and make decision to improve production plan in timely manner. This solution could be generalized for the whole mine or to similar petroleum mines in practice. Originality/value – This paper aims to propose a solution based on ANN for solving production back allocation problem of petroleum industry. The solution is tested at Samarang mine.

Sparse Neural Networks for Inference of Interwell Connectivity and Production Prediction

SPE Journal ◽  
2021 ◽  
pp. 1-22
Author(s):  
Junjie Yu ◽  
Atefeh Jahandideh ◽  
Siavash Hakim-Elahi ◽  
Behnam Jafarpour
Keyword(s):  
Neural Network ◽  
Feature Learning ◽  
Production Performance ◽  
Real Field ◽  
Injection Wells ◽  
Well Production ◽  
Second Stage ◽  
Proxy Model ◽  
Connectivity Patterns ◽  
Interwell Connectivity

Summary A new neural network-based proxy model is presented for prediction of well production performance and interpretation of interwell connectivity in large oil fields. The workflow consists of two stages. The first stage uses feature learning to describe the general input-output relations that exist among the wells and to characterize the interwell connectivity. In the second stage, the identified interwell connectivity patterns are used as network topology to develop a multilayer neural network proxy model, with nonlinear activation functions, to predict the production performance of each producer. The estimation of connectivity patterns in the first stage serves as an interpretable feature-learning step to improve the effectiveness of the proxy model in the second stage. Identification of interwell connectivity is based on the selection property of the ℓ1-norm minimization by promoting sparsity in the estimated connectivity weights. The sparsity of the network is motivated by the domain knowledge that each production well is mainly supported by a few nearby injection wells. That is, a proxy model that allows each producer to communicate with all the other wells in the field is inherently redundant and must have an unknown sparse representation. The sparse structure of the connection weights in the resulting network is detected by promoting sparsity during the training process. Two synthetic numerical examples, with known solutions, are first used to demonstrate the functionality and effectiveness of ℓ1-norm regularization for interwell connectivity identification. The workflow is then applied to a real field waterflooding example in Long Beach to predict oil production and to infer interwell connectivity information. Overall, the workflow provides a proxy model that effectively combines the implicit physical information from simulated data with reservoir engineering insight to identify interwell connectivity and to predict well production trends.

Non-linear threshold algorithm based solution for the redundancy allocation problem considering multiple redundancy strategies

2019 ◽  
Vol 25 (3) ◽  
pp. 397-411 ◽  
Author(s):  
Nabil Nahas ◽  
Mohamed N. Darghouth ◽  
Abdul Qadar Kara ◽  
Mustapha Nourelfath
Keyword(s):  
Optimization Problem ◽  
Allocation Problem ◽  
Computational Time ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation ◽  
Content Type ◽  
Linear Threshold ◽  
Subsystem Level ◽  
Non Linear ◽  
Selection Of

Purpose The purpose of this paper is to introduce an efficient algorithm based on a non-linear accepting threshold to solve the redundancy allocation problem (RAP) considering multiple redundancy strategies. In addition to the components reliability, multiple redundancy strategies are simultaneously considered to vary the reliability of the system. The goal is to determine the optimal selection of elements, redundancy levels and redundancy strategy, which maximizes the system reliability under various system-level constraints. Design/methodology/approach The mixed RAP considering the use of active and standby components at the subsystem level belongs to the class of NP-hard problems involving selection of elements and redundancy levels, to maximize a specific system performance under a given set of physical and budget constraints. Generally, the authors recourse to meta-heuristic algorithms to solve this type of optimization problem in a reasonable computational time, especially for large-size problems. A non-linear threshold accepting algorithm (NTAA) is developed to solve the tackled optimization problem. Numerical results for test problems from previous research are reported and analyzed to assess the efficiency of the proposed algorithm. Findings The comparison with the best solutions obtained in previous studies, namely: genetic algorithm, simulated annealing, memetic algorithm and the particle swarm optimization for 33 different instances of the problem, demonstrated the superiority of the proposed algorithm in finding for all considered instances, a high-quality solution in a minimum computational time. Research limitations/implications Considering multiple redundancy strategies helps to achieve higher reliability levels but increases the complexity of the obtained solution leading to infeasible systems in term of physical design. Technological constraints must be integrated into the model to provide a more comprehensive and realistic approach. Practical implications Designing high performant systems which meet customer requirements, under different economic and functional constraints is the main challenge faced by the manufacturers. The proposed algorithm aims to provide a superior solution of the reliability optimization problem by considering the possibility to adopt multiple redundancy strategies at the subsystem level in a minimum computational time. Originality/value A NTAA is expanded to the RAP considering multiple redundancy strategies at the subsystem level subject to weight and cost constraints. A procedure based on a penalized objective function is developed to encourage the algorithm to explore toward the feasible solutions area. By outperforming well-known solving technique, the NTAA provides a powerful tool to reliability designers of complex systems where different varieties of redundancies can be considered to achieve high-reliability systems.

Artificial neural network based robust speed control of permanent magnet synchronous motors

2006 ◽  
Vol 25 (1) ◽  
pp. 220-234 ◽  
Author(s):  
Tomasz Pajchrowski ◽  
Konrad Urbański ◽  
Krzysztof Zawirski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Speed Control ◽  
Controller Synthesis ◽  
Control Surface ◽  
Content Type ◽  
Linear Characteristic ◽  
Non Linear ◽  
Artificial Neural

PurposeThe aim of the paper is to find a simple structure of speed controller robust against drive parameters variations. Application of artificial neural network (ANN) in the controller of PI type creates proper non‐linear characteristics, which ensures controller robustness.Design/methodology/approachThe robustness of the controller is based on its non‐linear characteristic introduced by ANN. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the ANN to form the proper shape of the control surface, which represents the non‐linear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change (RWC) procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behaviour of a laboratory speed control system is validated in the experimental set‐up. The control algorithms of the system are performed by a microprocessor floating point DSP control system.FindingsThe proposed controller structure with proper control surface created by ANN guarantees expected robustness.Originality/valueThe original method of robust controller synthesis was proposed and validated by simulation and experimental investigations.

An integrated system based on fuzzy genetic algorithm and neural networks for stock price forecasting

2014 ◽  
Vol 31 (3) ◽  
pp. 281-292 ◽  
Author(s):  
Noraddin Mousazadeh Abbassi ◽  
Mohammad Ali Aghaei ◽  
Mahdi Moradzadeh Fard
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Stock Market ◽  
Price Index ◽  
Stock Exchange ◽  
Linear Method ◽  
Integrated System ◽  
Content Type ◽  
Non Linear ◽  
Fuzzy Genetic

Purpose – The aim of this research is to predict the total stock market index of the Tehran Stock Exchange, using the compound method of fuzzy genetics and neural network, in order for the active participants of the finance market as well as macro decision makers to be able to predict the market trend. Design/methodology/approach – First, the prediction was done by neural network, then the output weight of optimum neural network was taken as standard to repeat this prediction using the genetic algorithm, and then the extracted pattern from the neural network was stated through discernible rules using fuzzy theory. Findings – The main attention of this paper is investors and traders to achieve a method for predicting the stock market. Concerning the results of previous research, which confirms the relative superiority of non-linear models in price index prediction, an appropriate model has been offered in this research by compounding the non-linear method such as fuzzy genetics and neural network. The results indicate superiority of the designed system in predicting price index of the Tehran Stock Exchange. Originality/value – This paper states its originality and value by compounding the non-linear method issues pattern to predict stock market, to encourage further investigation by academics and practitioners in the field.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

A benchmarking study of Indian seaports

2019 ◽  
Vol 13 (1) ◽  
pp. 88-102
Author(s):  
Sajeev Abraham George ◽  
Anurag C. Tumma
Keyword(s):  
Financial Performance ◽  
Future Research ◽  
Data Envelopment ◽  
Operational Parameters ◽  
Two Stage ◽  
Content Type ◽  
The Public ◽  
Port Authorities ◽  
Financial Performance Measures ◽  
The Government

Purpose The purpose of this paper is to benchmark the operational and financial performances of the major Indian seaports to help derive useful insights to improve their performance. Design/methodology/approach A two-stage data envelopment analysis (DEA) methodology has been used with the help of data collected on the 13 major seaports of India. The first stage of the DEA captured the operational efficiencies, while the second stage the financial performance. Findings A window analysis over a period of three years revealed that no port was able to score an overall average efficiency of 100 per cent. The study identified the better performing units among their peers in both the stages. The contrasting results of the study with the traditional operational and financial performance measures used by the ports helped to derive useful insights. Research limitations/implications The data used in the study were majorly limited to the available sources in the public domain. Also, the study was limited to the major seaports which are under the Government of India and no comparisons were carried out with other local or international ports. Practical implications There is a need to prioritize investments and improvement efforts where they are most needed, instead of following a generalized approach. Once the benchmark ports are identified, the port authorities and other relevant stakeholders should work in detail on the factors causing inefficiencies, for possible improvements in performance. Originality/value This paper carried out a two-stage DEA that helped to derive useful insights on operational efficiency and financial performance of the India seaports. A combination of the financial and operational parameters, along with a comparison of the DEA results with the traditional measures, provided a different perspective on the Indian seaport performance. Considering the scarcity of research papers reported in the literature on DEA-based benchmarking studies of seaports in the Indian context, it has the potential to attract future research in this field.

scholarly journals A Hybrid Hidden Markov Model for Pipeline Leakage Detection

2021 ◽  
Vol 11 (7) ◽  
pp. 3138
Author(s):  
Mingchi Zhang ◽  
Xuemin Chen ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Deep Neural Network ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
State Sequence ◽  
Non Linear

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.

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