ESTIMATING NET SAND FROM BOREHOLE IMAGES IN LAMINATED DEEPWATER RESERVOIRS WITH A NEURAL NETWORK
Downtown Houston
Speaker:
Seminar Date: Feb 13 2020
Registration Opens: Feb 08 2020 - Feb 12 2020
Time: 11:30 AM - 01:00 PM (US CDT)
Admission/Registration Link: None
Meeting/Webinar Link: None
Contact: Hyungjoo Lee ( Houston, TX, USA, SPWLA Houston Chapter)
Corresponding: vpdowntown@spwla-houston.org
Fees:For Student: $25
For Member: $25
For Non-member: $15
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 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 1-D resistivity curve extracted from electrical borehole images. These workflows require multiple pre-processing 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 pre-selected cutoffs. The processing is based on an artificial neural network, which takes the 2-D 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. NOTE: Lunch is included for registered attendants. Register and pay before Tuesday 11th, 5 PM (one day before the meeting) to guarantee your lunch. Walk-ins are welcome but no lunch guaranteed. Lunch will be served at 11:30 AM, the introduction will be done at 11:55 AM and the presentation will start at 12PM.
BIOGRAPHY:
Parking Info :
Complimentary parking garage inside the building. Visitors can park anywhere not reserved (3rd floor and above).
Walk-ins are welcome but advanced registration is preferred. Please register and pay by Feb 11th, 2020 @ 5pm to reserve lunch.
Contact : Javier Miranda
Corresponding : vpdowntown@spwla-houston.org // 713.273.8304