US 11,900,658 B2
Method for automated stratigraphy interpretation from borehole images
Marie LeFranc, Cambridge, MA (US); Zikri Bayraktar, Wilmington, MA (US); Morten Kristensen, Cambridge, MA (US); Philippe Marza, Montpellier (FR); Isabelle Le Nir, Clamart (FR); Michael Prange, Somerville, MA (US); and Josselin Kherroubi, Clamart (FR)
Assigned to SCHLUMBERGER TECHNOLOGY CORPORATION, Sugar Land, TX (US)
Appl. No. 17/593,011
Filed by Schlumberger Technology Corporation, Sugar Land, TX (US)
PCT Filed Mar. 11, 2020, PCT No. PCT/US2020/022131
§ 371(c)(1), (2) Date Sep. 3, 2021,
PCT Pub. No. WO2020/185918, PCT Pub. Date Sep. 17, 2020.
Claims priority of provisional application 62/816,466, filed on Mar. 11, 2019.
Prior Publication US 2022/0164594 A1, May 26, 2022
Int. Cl. G06V 10/772 (2022.01); G06F 18/2431 (2023.01); G06F 18/214 (2023.01); G06F 18/2415 (2023.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 10/44 (2022.01)
CPC G06V 10/772 (2022.01) [G06F 18/2148 (2023.01); G06F 18/2415 (2023.01); G06F 18/2431 (2023.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 10/454 (2022.01)] 14 Claims
OG exemplary drawing
 
1. A method for automated stratigraphy interpretation from borehole images comprising:
constructing, using at least one processor, a training set of images corresponding to a borehole, wherein the training set includes one or more of synthetic images, real images, and modified images;
automatically classifying, using the at least one processor, the training set into one or more individual sedimentary geometries using a machine learning model that has been trained based on images generated from wells with multiple deviations to automatically recognize one or more sedimentary geometries from one or more borehole images, regardless of borehole deviation, wherein the automatically classifying comprises:
identifying a longer than standard borehole image in the training set of images; and
applying a sliding window as a spatial sampling technique based on the identifying the longer than standard borehole image, wherein the spatial sampling technique includes providing a plurality of cropped images, corresponding to the sliding window, from the longer than standard borehole image as inputs to the machine learning model; and
automatically classifying, using the at least one processor, the training set into one or more priors for depositional environments, wherein the automatically classifying into the one or more priors includes:
building one or more tables of sedimentary geometry successions that represent one or more depositional environments; and
automatically obtaining, using the one or more tables, depositional environments from the training set of images.