uOutline

uFigure captions

uExamples

  uData preparation

  uData for training intervals

  uFluid substitution

uSummary

uOutline

uFigure captions

uExamples

  uData preparation

  uData for training intervals

  uFluid substitution

uSummary

uOutline

uFigure captions

uExamples

  uData preparation

  uData for training intervals

  uFluid substitution

uSummary

uOutline

uFigure captions

uExamples

  uData preparation

  uData for training intervals

  uFluid substitution

uSummary

Figure Captions

Figure 1: Comparing neural network models using the raw data with a model using despiked data.

Figure 2: Comparing synthetic sonics created using specific regions as the training intervals for a neural network, with a model using the entire well as the training interval.

Figure 3: Comparisons of three pseudo sonic logs with the original log.

Figure 4: Comparing synthetic seismograms using four different synthetic sonic approaches.

Examples 

The examples are from a well in the Wamsutter area of southwest Wyoming.

1) The Importance of Data Preparation 

Two different neural networks are used to create a sonic log from density, neutron, and gamma ray logs:

a) Using unedited (raw) data -- the system ‘‘learns’’ intervals of bad hole and faithfully reproduces sonic ‘‘spikes.”

b) Using edited data -- corrected for bad hole -- the system reproduces the edited data, and is much more reliable.

In Figure 1, track #1 shows the comparison of the original sonic log with the despiked log, highlighting the spikes removed. The Synthetic Sonic #1 log was created using the original density, neutron, and gamma ray logs to predict the original sonic. Note that the spikes in the original sonic are faithfully reproduced by the neural network. The Synthetic Sonic #2 log was generated using the gamma ray and despiked density and neutron logs to model the despiked sonic. The result is a more appropriate model. The red bars highlight regions where spikes were reproduced in the first model, but are corrected in the second. The blue bar indicates a region where the logs used to model the sonic do not exhibit character sufficient to model the sonic in either case. 

The example demonstrates the importance of preparing the data prior to using any neural network technique. The neural network will reproduce whatever the input data demonstrates. If the input includes bad data, the neural network will ‘‘learn’’ to predict bad data. Thus it is crucial to take measures to ensure the input data is valid and consistent.

2) Using Appropriate Amounts of Data for the Training Intervals 

Different neural networks are used to predict a sonic log from density, neutron, and gamma ray logs. In each case, different training intervals are used. The differences can be quite large if the training points are not chosen extremely wisely (very subjective). A final case demonstrates using far more of the data to create a better model which more appropriately models the reservoir. 

In Figure 2, the Synthetic Sonic #1 was created using only the training regions highlighted in yellow, whereas Synthetic Sonic #2 used the entire well as the training region. As expected, the first model is extremely accurate over the training regions. The problems arise over the other regions, for which it becomes obvious that the training intervals did not fully represent the data that was being modeled. As highlighted in red, there are now regions where the first model generated erroneous spikes in the sonic due to insufficient data being provided initially. This type of selective interval approach can lead to many such problems. Although selecting more training intervals can help resolve specific issues, it becomes a very subjective model. A better approach is to begin with as much data as possible and let the neural network incorporate the maximum amount of valid data.

3) Differences using Fluid Substitution 

Pseudo sonic logs, calculated deterministically and including the effects of gas substitution:

a) Liquid-filled

b) Residual gas

c) Gas remote from the wellbore

It is clear in Figure 3 that in the gas-bearing sand, the sonic ‘‘sees’’ no gas. Density/Neutron and Resistivity responses clearly indicate gas, at depths of investigation beyond those measured by the sonic log. 

The synthetic seismograms in Figure 4 show significant differences dependent on the pseudo sonic used. The lesson of the example is that if you do not know what fluid the sonic log is measuring (and it is not always residual gas), then any synthetic seismograms using the sonic will also have problematic meaning. 

Additionally, if a missing log is a shallow-reading device (such as a sonic log) but is generated from deeper reading devices (such as the deep resistivity, neutron, or density), then the resulting pseudo log has questionable value.

Summary 

In each case, missing data can be generated using different methods. However, care must be used to ensure that the generated information has integrity and is appropriate for the reservoir. 

1) Steps must be taken to clean-up and validate all input data to any synthetic generation method. This rather obvious step is often one of the easiest to overlook. 

2) It is important for the interpreter to have an understanding of what the correct answer might be. When generating synthetic data, it is conceivable to generate nearly any answer. It is up to the interpreter to ensure the answer used makes geological and geophysical sense.

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