Patent ID: 11952880
Assignee: KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS
Field: Civil engineering (Other fields)
Classification: CPC E  G | IPC E  G

Claim 13:
14. A system for automatic optimization of ROP, the system comprising:
an AI module comprising a plurality of machine learning algorithms and a processor configured to execute instructions stored in a non-transitory computer storage medium for performing a method for optimizing ROP comprising:
obtaining, a plurality of drilling surface parameters for the field of interest;
identifying, an UCS data for a targeted formation of interest based on well logs;
calculating, a MSE data based on the identified UCS values for the targeted formation of interest;
filtering, the calculated MSE data based on the identified UCS data with a range for the targeted formation of interest;
training, a machine learning model using the drilling surface parameters as inputs;
outputting, 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, 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, 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, the machine learning ROP equation with the Teale's MSE equation to form a set of two equations;
determining, a plurality of optimum drilling parameters by simultaneously solving the set of machine learning ROP equation and the Teale's MSE equation;
generating, a work order to adjust the drilling parameters based on the determined optimum drilling parameters and previous drilling parameters; and
causing, display of the work order and the determined optimum drilling parameters in a user interface of a client device.