# US 2025/0084751 A1 · Artificial intelligence generated synthetic sensor data for drilling > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0084751 A1 - Document ID: US20250084751A1 - Status: Published application - Kind: A1 - Application number: 18/827,082 - Filed: 2024-09-06 - Published: 2025-03-13 - Earliest claimed: 2023-09-08 - Assignees: Schlumberger Technology Corporation - Applicants: Schlumberger Technology Corporation - Inventors: Carlos Urdaneta; Cheolkyun Jeong; Shunfeng Zheng - Web page: https://patentgrep.com/patent/US20250084751A1 ## CPC - E21B 44/00: Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions - E21B 21/08: Controlling or monitoring pressure or flow of drilling fluid, e.g. automatic filling of boreholes, automatic control of bottom pressure (valve arrangements therefor E21B21/10) - E21B 47/06: Measuring temperature or pressure - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 20/00: Machine learning - G06N 20/20: Ensemble learning - E21B 2200/20: Computer models or simulations, e.g. for reservoirs under production, drill bits - E21B 2200/22: Fuzzy logic, artificial intelligence, neural networks or the like - E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method for generating synthetic sensor data during a drill operation includes training an artificial intelligence based backup sensor model, deploying the trained backup sensor model at a drilling location, and evaluating sensor data acquired from a plurality of sensors while drilling with the trained backup sensor model to synthesize backup sensor data. ## Claims ### Claim 1 A method for synthesizing downhole sensor data while drilling a subterranean wellbore, the method comprising: training a deep learning model with historical drilling data to obtain a trained backup sensor model; deploying the trained backup sensor model at a drilling location; acquiring sensor measurements from a plurality of sensors while drilling the subterranean wellbore; and synthesizing a backup sensor measurement for at least one other sensor using the acquired sensor measurements and the trained backup sensor model. ### Claim 2 (depends on 1) The method of claim 1, further comprising: adjusting at least one drilling parameter while drilling the subterranean wellbore in response to the synthesized backup sensor measurement. ### Claim 3 (depends on 1) The method of claim 1, wherein the trained backup sensor model is deployed in a surface computer at the drilling location. ### Claim 4 (depends on 1) The method of claim 1, wherein the trained backup sensor model is deployed in a downhole tool deployed in the subterranean wellbore. ### Claim 5 (depends on 1) The method of claim 1, wherein the training the deep learning model comprises identifying relationships and/or correlations between sensor data obtained from different sensor channels in the historical drilling data. ### Claim 6 (depends on 1) The method of claim 1, wherein the deep learning model comprises a random forest model or an extra trees model. ### Claim 7 (depends on 1) The method of claim 1, further comprising retraining the trained backup sensor model using data obtained at the drilling location. ### Claim 8 (depends on 1) The method of claim 1, further comprising computing an uncertainty of the synthesized backup sensor measurement. ### Claim 9 (depends on 1) The method of claim 1, further comprising evaluating at least one of a number of detected sensor failure modes, a comparison of uncertainty of the synthesized backup sensor measurements and an uncertainty of a corresponding sensor measurement, and a criticality of a failed sensor to drilling the subterranean wellbore. ### Claim 10 (depends on 9) The method of claim 9, further comprising accepting the synthesized backup sensor measurements for use in the drilling operation when at least one of the following conditions is met: (i) the number of detected sensor failure modes is less than a threshold, (ii) the uncertainty of the synthesized backup sensor measurements is within a threshold of the uncertainty of a corresponding sensor measurement, and (iii) the criticality of the failed sensor is low. ### Claim 11 A system for synthesizing downhole sensor data while drilling a subterranean wellbore, the system comprising: a downhole tool including a plurality of sensors; a trained backup sensor model deployed at a drilling location, the trained backup sensor model trained using historical drilling data; and a processor configured to: acquire sensor measurements from the plurality of sensors in the downhole tool while drilling the subterranean wellbore; and synthesize a backup sensor measurement for at least one other sensor in the downhole tool using the acquired sensor measurements and the trained backup sensor model. ### Claim 12 (depends on 11) The system of claim 11, wherein the processor is deployed at a surface location. ### Claim 13 (depends on 12) The system of claim 12, wherein: the downhole tool is a measurement while drilling tool deployed in a coiled tubing string; and the trained backup sensor comprises at least one of an internal pressure sensor, an external pressure sensor, and a weight on bit sensor. ### Claim 14 (depends on 11) The system of claim 11, wherein: the processor is deployed in the downhole tool; and the downhole tool comprises a measurement while drilling tool, a logging while drilling tool, or a rotary steerable tool. ### Claim 15 (depends on 14) The system of claim 14, wherein: the trained backup sensor comprises at least one of an internal pressure sensor, an external pressure sensor, and a weight on bit sensor when the downhole tool is a measurement while drilling tool or a rotary steerable tool; and the trained backup sensor comprises at least one of a resistivity sensor, a porosity sensor, a density sensor, or a gamma ray sensor when the downhole tool is a logging while drilling tool. ### Claim 16 A method for synthesizing measurement while drilling (MWD) sensor data while drilling a subterranean wellbore, the method comprising: training a deep learning model with historical drilling data to obtain a trained MWD backup sensor model; deploying the trained MWD backup sensor model at a drilling location; acquiring surface sensor measurements and downhole sensor measurements from a plurality of corresponding sensors while drilling the subterranean wellbore; and synthesizing a backup sensor measurement for at least one other MWD sensor using the acquired sensor measurements and the trained MWD backup sensor model. ### Claim 17 (depends on 16) The method of claim 16, wherein the at least one other MWD sensor comprises an internal pressure sensor, an external pressure sensor, or a weight on bit sensor. ### Claim 18 (depends on 16) The method of claim 16, wherein the deep learning model comprises a random forest model or an extra trees model. ### Claim 19 (depends on 16) The method of claim 16, wherein the downhole sensor measurements are made using MWD sensors deployed in a coiled tubing string in use in a coiled tubing drilling operation. ### Claim 20 (depends on 16) The method of claim 16, further comprising: evaluating at least one of a number of detected sensor failure modes, a comparison of uncertainty of the synthesized backup sensor measurements and an uncertainty of a corresponding sensor measurement, and a criticality of a failed sensor to drilling the subterranean wellbore; and accepting the synthesized backup sensor measurements for use in the drilling operation when at least one of the following conditions is met: (i) the number of detected sensor failure modes is less than a threshold, (ii) the uncertainty of the synthesized backup sensor measurements is within a threshold of the uncertainty of a corresponding sensor measurement, and (iii) the criticality of the failed sensor is low.