US 12,259,517 B2
Methods for predicting and monitoring downhole salinity variations
Gong Li Wang, Sugar Land, TX (US); Wael Abdallah, Sugar Land, TX (US); Shouxiang Ma, Dhahran (SA); and Sherif Ghadiry, Dhahran (SA)
Assigned to SCHLUMBERGER TECHNOLOGY CORPORATION, Sugar Land, TX (US); and SAUDI ARABIAN OIL COMPANY, Dhahran (SA)
Appl. No. 18/717,453
Filed by Schlumberger Technology Corporation, Sugar Land, TX (US); and SAUDI ARABIAN OIL COMPANY, Dhahran (SA)
PCT Filed May 20, 2022, PCT No. PCT/US2022/030202
§ 371(c)(1), (2) Date Jun. 7, 2024,
PCT Pub. No. WO2023/224632, PCT Pub. Date Nov. 23, 2023.
Prior Publication US 2024/0418899 A1, Dec. 19, 2024
Int. Cl. G01V 3/38 (2006.01); G01V 3/18 (2006.01); G06N 20/00 (2019.01)
CPC G01V 3/38 (2013.01) [G01V 3/18 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method for characterizing connate water salinity and resistivity of a subsurface formation, comprising:
i) obtaining, via a processing system, well log data measured by at least one downhole tool disposed within a borehole that traverses the subsurface formation, wherein the well log data is acquired during a drilling operation performed by a drill string within the borehole, and wherein the well log data includes resistivity log data and spontaneous potential (SP) log data;
ii) performing, via the processing system, inversion operations that invert the resistivity log data of i) to determine a resistivity model of the subsurface formation, wherein the inversion operations of ii) are based on an assumption of a step profile invasion for mud filtrate invasion;
iii) determining, via the processing system, a connate water resistivity of the subsurface formation for an interval of the borehole associated with a depth range by minimizing a cost function of the SP log data, wherein the cost function comprises the SP log data, the depth range of the borehole, a resistivity of mud in the subsurface formation, a resistivity of mud filtrate in the subsurface formation, the resistivity model, and a plurality of properties associated with the borehole obtained from the well log data;
iv) determining, via the processing system, a connate water salinity of the subsurface formation based on the connate water resistivity of the subsurface formation for the interval of the borehole as determined in iii), wherein the connate water salinity of the subsurface formation corresponds to an invaded zone of the subsurface formation associated with the drilling operation; and
v) using, via the processing system, the connate water salinity of the subsurface formation of iv) as training data for training a machine learning model, wherein the machine learning model predicts connate water salinity of an undisturbed zone of the subsurface formation for future drilling operations based on a result of the training and an input data vector associated with the undisturbed zone of the subsurface formation, and wherein the undisturbed zone of the subsurface formation is different from the invaded zone of the subsurface formation.