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DPS June 2020 Meeting and AGM
June 4 @ 4:00 pm - 7:00 pm CESTFree
We will be holding the DPS June meeting and AGM on Thursday 4th June 2020. Because of the current restrictions we’ll be holding this as an online virtual meeting: The link will be sent to all registered attendees close to the date.
Doors open at 15:30 with the talks beginning at 16:00. Followed by a virtual social hour, though you’ll have to supply your own refreshments this time!
SPWLA Distinguished Lecturer Bo Gong will be presenting her talk:
“Estimating Net Sand From Borehole Images in Laminated Deepwater Reservoirs With A Neural Network”.
The talk abstract and Bo’s biography is below.
To join the meeting, register here . Once registered you will be sent a direct link to join. As always attendance is free for DPS members.
Hope to see you on 4th June.
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 may need to leverage the high resolutions offered by borehole image logs. Traditionally, explicit sand counting in thin beds has been done by applying a user-specified cutoff on a 1D resistivity curve extracted from electrical borehole images. These workflows require multiple preprocessing steps and log calibration, and the results are often highly sensitive to the cutoff selection, especially in high-salinity environments.
This paper presents a new method that estimates sand fractions directly from electrical borehole images without extracting an image resistivity curve or applying any preselected cutoffs. The processing is based on an artificial neural network, which takes the 2D borehole image array as input, and predicts sand fractions with the measurements from all button electrodes. A cumulative sand count can be computed after processing the borehole image logs along an entire well by summing up the estimated net sands. The neural network is trained and tested on a large dataset from wells in a deepwater reservoir with various degrees of laminations, and validated with sand fractions identified from core photos. Upon testing, a good match has been observed between the prediction and the target output. The results were also compared against another sand-counting method based on texture analysis, and showed advantages of yielding unbiased estimations and a lower margin of error.
Bo Gong is a research petrophysicist with Chevron ETC. She received her Ph.D. 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.