An Artificial Neural Network Approach to Identify the Well Test Interpretation Model: Applications

Al-Kaabi, Abdulaziz; Lee, W.J.

doi:10.2118/20552-ms

Cited by 46 publications

(16 citation statements)

References 8 publications

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“…5, with reservoir data given by Al-Kabbi and Lee. 5 The results show excellent agreements, as Table 1 shows. In this result, permeability was estimated as 0.124 md with a confidence interval of 0.01 md, while Al-Kabbi and Lee 5 reported the permeability as 0.115 md.…”

Section: Resultsmentioning

confidence: 54%

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Development of the HT-BP Neural Network System for the Identification of a Well-Test Interpretation Model

Sung

Yoo

Park

1996

SPE Computer Applications

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The back propagation (BP) neural network approach has been the subject of recent focus because it can identify models for incomplete or distorted data without performing data preparation procedures. However, this approach uses only partial sets of data to reduce computing time and memory, and it may miss the points representing characteristics of the curve shape. Therefore, the resulted model may not be correct, forcing one to use sequential neural nets to find the correct model.We present the Hough Transform (HT) method combined with the BP neural network to improve this problem. With the aid of an HT, we can extract one simple pattern, including noisy and extraneous points, from the full-set data. A number of exercises also have been conducted for the published well-test data with the artificial intelligence neural network identification system (ANNIS) we developed. The results show that ANNIS is quite reliable, especially for the incomplete or distorted data. They also demonstrate that the modified Levenberg-Marquart interpretation model, also developed in this work, successfully estimates reservoir parameters.

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Section: Resultsmentioning

confidence: 54%

“…5 In this example, the extracted pattern was first obtained and an activation level of 0.8206 resulted in comparison to a standard pattern (Fig. 2) for an HOMO-IF system, as illustrated in Fig.…”

Section: Resultsmentioning

confidence: 99%

Development of the HT-BP Neural Network System for the Identification of a Well-Test Interpretation Model

Sung

Yoo

Park

1996

SPE Computer Applications

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“…Large positive values asymptotically approach 1, while large negative values are squashed to 0. The sigmoid is given by 1 5 is a typical plot of the sigmoid function. In essence, the activation function acts as a nonlinear gain for the processing element.…”

Section: Activation Rulementioning

confidence: 99%

“…Among other applications to petroleum engineering, NN's have been used for pattern recognition in well test interpretation 5 and for prediction in phase behavior. 6 Artificial neural networks are an information processing technology inspired by the studies of the brain and nervous system.…”

Section: Introductionmentioning

confidence: 99%

Predicting Natural Gas Production Using Artificial Neural Network

Al-Fattah

Startzman

2001

All Days

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe industrial and residential market for natural gas produced in the United States has become increasingly significant. Within the past ten years the wellhead value of produced natural gas has rivaled and sometimes exceeded the value of crude oil. Forecasting natural gas supply is an economically important and challenging endeavor. This paper presents a new approach to predict natural gas production for the United States using an artificial neural network.We developed a neural network model to forecast U.S. natural gas supply to the Year 2020. Our results indicate that the U.S. will maintain its 1999 production of natural gas to 2001 after which production starts increasing. The network model indicates that natural gas production will increase during the period 2002 to 2012 on average rate of 0.5%/yr. This increase rate will more than double for the period 2013 to 2020.The neural network was developed with an initial large pool of input parameters. The input pool included exploratory, drilling, production, and econometric data. Preprocessing the input data involved normalization and functional transformation. Dimension reduction techniques and sensitivity analysis of input variables were used to reduce redundant and unimportant input parameters, and to simplify the neural network. The remaining input parameters of the reduced neural network included data of gas exploratory wells, oil/gas exploratory wells, oil exploratory wells, gas depletion rate, proved reserves, gas wellhead prices, and growth rate of gross domestic product. The three-layer neural network was successfully trained with yearly data starting from 1950 to 1989 using the quick-propagation learning algorithm. The target output of the neural network is the production rate of natural gas. The agreement between predicted and actual production rates was excellent. A test set, not used to train the network and containing data from 1990 to 1998, was used to verify and validate the network performance for prediction. Analysis of the test results shows that the neural network approach provides an excellent match of actual gas production data. An econometric approach, called stochastic modeling or time series analysis, was used to develop forecasting models for the neural network input parameters. A comparison of forecasts between this study and other forecast is presented.The neural network model has use as a short-term as well as a long-term predictive tool of natural gas supply. The model can also be used to examine quantitatively the effects of the various physical and economic factors on future gas production.

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“…Among other applications 4 to petroleum engineering, NNs have been used for pattern recognition in well-test interpretation 5 and for prediction in well logs 4 and phase behavior. 6 Artificial NNs are an information-processing technology inspired by studies of the brain and nervous system.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Approach Predicts U.S. Natural Gas Production

Al-Fattah

Startzman

2003

SPE Production &Amp; Facilities

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The industrial and residential market for natural gas produced in the United States has become increasingly significant. Within the past 10 years, the wellhead value of produced natural gas has rivaled and sometimes exceeded the value of crude oil. Forecasting natural gas supply is an economically important and challenging endeavor. This paper presents a new approach to predict natural gas production for the United States with an artificial neural network (NN).We developed an NN model to forecast the U.S. natural gas supply to 2020. Our results indicate that the U.S. will maintain its 1999 production of natural gas until 2001, after which production increases. The network model indicates that natural gas production will increase by an average rate of 0.5%/yr from 2002 to 2012. This increase will more than double from 2013 to 2020.The NN was developed with a large initial pool of input parameters. The input pool included exploratory, drilling, production, and econometric data. Preprocessing the input data involved normalization and functional transformation. Dimension-reduction techniques and sensitivity analysis of input variables were used to reduce redundant and unimportant input parameters and to simplify the NN. The remaining parameters included data from gas exploratory wells, oil/gas exploratory wells, oil exploratory wells, gas depletion rate, proved reserves, gas wellhead prices, and growth rate of the gross domestic product. The three-layer NN was successfully trained with yearly data from 1950 to 1989 using the quick-propagation learning algorithm. The NN's target output is the production rate of natural gas. The agreement between predicted and actual production rates was excellent. A test set not used to train the network and containing data from 1990 to 1998 was used to verify and validate the network prediction performance. Analysis of the test results showed that the NN approach provides an excellent match with actual gas production data. An econometric approach, called stochastic modeling or time-series analysis, was used to develop forecasting models for NN input parameters. A comparison of forecasts between this study and another is presented.The NN model has use as a short-term as well as a long-term predictive tool for natural gas supply. The model can also be used to quantitatively examine the effects of the various physical and economic factors on future gas production.

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An Artificial Neural Network Approach to Identify the Well Test Interpretation Model: Applications

Cited by 46 publications

References 8 publications

Development of the HT-BP Neural Network System for the Identification of a Well-Test Interpretation Model

Development of the HT-BP Neural Network System for the Identification of a Well-Test Interpretation Model

Predicting Natural Gas Production Using Artificial Neural Network

Neural Network Approach Predicts U.S. Natural Gas Production

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