ESTIMATING NET SAND FROM BOREHOLE IMAGES IN LAMINATED DEEPWATER RESERVOIRS WITH A NEURAL NETWORK
SPEAKER: Bo Gong - Chevron
Speaker Bio: Bo Gong is a research petrophysicist with Chevron ETC. She received her PhD degree in Electrical Engineering from the University of Houston in 2014. Her research interests include borehole imaging technologies, image processing and interpretation techniques, and electromagnetic logging tool modeling.
Authors: Bo Gong, Dustin Keele, Emmanuel Toumelin, Simon Clinch, Chevron
Abstract: Deepwater reservoirs often
consist of highly laminated sand shale sequences, where the formation layers
are too thin to be resolved by conventional logging tools. To better estimate
net sand and hydrocarbon volume in place, one usually needs to rely on
high-resolution borehole image logs, which can detect extremely fine layers
with thickness of several millimeters.
Traditionally, explicit sand
counting in thin beds has been done by applying a user-specified cutoff on a
1-D high-resolution resistivity curve extracted from electrical borehole
images. The workflow generally requires meticulous image QC, multiple
pre-processing steps and log calibration, and the results are often highly
sensitive to the cutoff selection, especially in high-salinity environments,
where resistivity in pay sand can be very close to that in shale. In oil-based
mud (OBM), accuracy of the cutoff method is further limited by the presence of
non-conductive mud cakes and possible tool artifacts.
This paper presents a new
method that estimates sand fractions directly from OBM borehole images without
extracting an image resistivity curve. The processing is based on an artificial
neural network, which takes a 2-D borehole image array as input, and predicts
sand fractions by applying a non-linear transformation to all the elements,
i.e., electrical measurements from all button electrodes. A cumulative sand
count can be computed after processing the borehole image logs foot by foot
along an entire well. The neural network is trained on a large dataset with
example images of various degrees of laminations, labeled with sand fractions
identified from core photos. Upon testing, a good match has been observed
between the prediction and the target output. The results have also shown
advantages against another sand counting method based on texture analysis.
The described method offers
new opportunities of quantifying thin sands in the absence of cores, which can
be used to improve petrophysical interpretation in laminated reservoirs. With
appropriate tuning, a pre-trained network model could also be generalized to applications
in new wells or even new fields with similar depositional environments.
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