US 11,952,880 B2
Method and system for rate of penetration optimization using artificial intelligence techniques
Ahmad Mohammad Al-AbdulJabbar, Dammam (SA); Salaheldin M. Elkatatny, Dhahran (SA); and Abdullah S. Al-Yami, Dhahran (SA)
Assigned to SAUDI ARABIAN OIL COMPANY, Dhahran (SA); and KING FAHD UNIVERSITY OF PETROLEUM & MINERALS, Dhahran (SA)
Filed by SAUDI ARABIAN OIL COMPANY, Dhahran (SA); and KING FAHD UNIVERSITY OF PETROLEUM & MINERALS, Dhahran (SA)
Filed on Mar. 26, 2021, as Appl. No. 17/213,845.
Prior Publication US 2022/0307365 A1, Sep. 29, 2022
Int. Cl. E21B 44/00 (2006.01); E21B 44/02 (2006.01); E21B 45/00 (2006.01); G06N 20/00 (2019.01)
CPC E21B 44/00 (2013.01) [E21B 44/02 (2013.01); E21B 45/00 (2013.01); G06N 20/00 (2019.01); E21B 2200/22 (2020.05)] 20 Claims
OG exemplary drawing
 
1. A method for automatic optimization of rate of penetration (ROP), the method comprising:
obtaining, by a computer processor, a plurality of drilling surface parameters for a field of interest;
identifying, by a computer processor, an undefined compressive strength (UCS) data for a targeted formation of interest based on well logs;
calculating, by a computer processor, a mechanical specific energy (MSE) data based on the identified UCS data for the targeted formation of interest;
filtering, by a computer processor, the calculated MSE data based on the identified UCS data with a range for the targeted formation of interest;
training, by a computer processor, a machine learning model using the drilling surface parameters as inputs;
outputting, by a computer processor, a plurality of weights for drilling parameters in a ROP equation derived by using the trained machine learning model for the field of interest, wherein the drilling surface parameters are used as inputs;
determining, by a computer processor, a plurality of weights for drilling parameters in a Teale's MSE equation for the field of interest, wherein the drilling surface parameters are used as inputs;
outputting, by a computer processor, a plurality of weights for drilling parameters in the Teale's MSE equation for the field of interest, wherein the drilling surface parameters are used as inputs;
combining, by a computer processor, the machine learning ROP equation with the Teale's MSE equation to form a set of two equations;
determining, by a computer processor, a plurality of optimum drilling parameters by simultaneously solving the set of machine learning ROP equation and the Teale's MSE equation;
generating, by a computer processor, a work order to adjust the drilling parameters based on the determined optimum drilling parameters and previous drilling parameters; and
causing, by a computer processor, display of the work order and the determined optimum drilling parameters in a user interface of a client device.