Using Artificial Neural Networks to Predict the Presence of Overpressured Zones in the Anadarko Basin,Oklahoma |
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Authors: | Constantin Cranganu |
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Institution: | (1) Brooklyn College of the City University of New York, 2900 Bedford Avenue, Brooklyn, NY, 11210, U.S.A |
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Abstract: | Many sedimentary basins throughout the world exhibit areas with abnormal pore-fluid pressures (higher or lower than normal
or hydrostatic pressure). Predicting pore pressure and other parameters (depth, extension, magnitude, etc.) in such areas
are challenging tasks. The compressional acoustic (sonic) log (DT) is often used as a predictor because it responds to changes
in porosity or compaction produced by abnormal pore-fluid pressures. Unfortunately, the sonic log is not commonly recorded
in most oil and/or gas wells. We propose using an artificial neural network to synthesize sonic logs by identifying the mathematical
dependency between DT and the commonly available logs, such as normalized gamma ray (GR) and deep resistivity logs (REID).
The artificial neural network process can be divided into three steps: (1) Supervised training of the neural network; (2)
confirmation and validation of the model by blind-testing the results in wells that contain both the predictor (GR, REID)
and the target values (DT) used in the supervised training; and 3) applying the predictive model to all wells containing the
required predictor data and verifying the accuracy of the synthetic DT data by comparing the back-predicted synthetic predictor
curves (GRNN, REIDNN) to the recorded predictor curves used in training (GR, REID). Artificial neural networks offer significant
advantages over traditional deterministic methods. They do not require a precise mathematical model equation that describes
the dependency between the predictor values and the target values and, unlike linear regression techniques, neural network
methods do not overpredict mean values and thereby preserve original data variability. One of their most important advantages
is that their predictions can be validated and confirmed through back-prediction of the input data. This procedure was applied
to predict the presence of overpressured zones in the Anadarko Basin, Oklahoma. The results are promising and encouraging. |
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Keywords: | Artificial neural networks overpressure Anadarko Basin logs |
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