Method of generating prompts for an industry-specific large language model recommendation system

Abstract

A system and method for modifying operation of a drilling platform controller. A prompt generator receives drilling operations data relevant to drilling platform controller operation, wherein the drilling operations data includes current drilling parameters of a selected drilling platform controller. The prompt generator generates a prompt for recommended changes in operation of the selected drilling platform controller and applies the prompt to a large language model (LLM) trained with drilling operations domain knowledge. The LLM generates a recommendation for one or more changes in operation of the selected drilling platform controller. Feedback on efficacy of the recommendation is received from the selected drilling platform controller and is used to modify operation of the prompt generator.

What is claimed is: 
     
         1 . A large language model (LLM) recommendation system, comprising:
 an LLM trained with drilling operations domain knowledge;   an LLM prompt generator connected to the LLM, the LLM prompt generator configured to receive drilling operations data, including current drilling parameters associated with a selected drilling platform controller, and to generate LLM prompts, the LLM prompts configured to request a recommendation from the LLM for changes to operation of the selected drilling platform controller based on the received data; and   a feedback system connected to the LLM prompt generator and the LLM, the feedback system configured:
 to receive feedback on efficacy of the recommendation, from the selected drilling platform controller, for each recommendation received by the selected drilling platform controller; and 
 to modify operation of the LLM prompt generator based on the feedback. 
   
     
     
         2 . The LLM recommendation system of  claim 1 , wherein the feedback system includes a recommendation archive and a recommendation feedback system, wherein the LLM stores each recommendation in the recommendation archive, and
 wherein the recommendation feedback system receives feedback, from the selected drilling platform, for each recommendation received by the selected drilling platform controller and associates the feedback with the respective stored recommendation.   
     
     
         3 . The LLM recommendation system of  claim 1 , wherein the feedback system includes a recommendation archive and a recommendation feedback system, wherein the LLM stores each recommendation in the recommendation archive, and
 wherein the recommendation feedback system receives feedback, from the selected drilling platform, for each recommendation received by the selected drilling platform controller and stores the feedback with the respective stored recommendation.   
     
     
         4 . The LLM recommendation system of  claim 3 , wherein the feedback system further includes a prompt engineering tool, the prompt engineering tool configured to receive a stored recommendation with its associated feedback and to modify operation of the LLM prompt generator based on the feedback. 
     
     
         5 . The LLM recommendation system of  claim 1 , wherein the LLM recommendation system further includes a data aggregator connected to one or more data sources and to the LLM prompt generator, the data aggregator configured to receive drilling operations data relevant to the LLM prompt generator. 
     
     
         6 . The LLM recommendation system of  claim 5 , wherein the data sources include one or more of real-time drilling stream data, well-site information transfer standard markup language (WITSML) data, or daily reports. 
     
     
         7 . The LLM recommendation system of  claim 6 , wherein the data aggregator includes one or more agents, the agents configured to query the data sources for drilling operations data relevant to the LLM prompt generator. 
     
     
         8 . The LLM recommendation system of  claim 1 , wherein the LLM includes domain knowledge obtained from transfer learning of domain knowledge sources. 
     
     
         9 . A method, comprising:
 receiving drilling operations data relevant to drilling platform controller operation, wherein the drilling operations data includes current drilling parameters of a selected drilling platform controller;   generating, at a prompt generator, a prompt for recommended changes in operation of the selected drilling platform controller;   applying the prompt to a large language model (LLM) trained with drilling operations domain knowledge;   receiving, from the LLM, a recommendation for one or more changes in operation of the selected drilling platform controller;   receiving, from the selected drilling platform controller, feedback on efficacy of the recommendation; and   modifying operation of the prompt generator based on the feedback.   
     
     
         10 . The method of  claim 9 , wherein receiving a recommendation includes storing the recommendation into a recommendation archive. 
     
     
         11 . The method of  claim 10 , wherein receiving feedback includes associating the feedback with the respective stored recommendation. 
     
     
         12 . The method of  claim 10 , wherein receiving feedback includes storing the feedback in the recommendation archive with the respective stored recommendation. 
     
     
         13 . The method of  claim 9 , wherein the feedback includes one or more of a grade or a snapshot of one or more of the drilling parameters for the selected drilling platform controller after implementing the recommended changes. 
     
     
         14 . The method of  claim 9 , wherein modifying operation of the prompt generator based on the feedback includes receiving a selected stored recommendation and the feedback associated with the selected stored recommendation and modifying operation of the prompt generator based on the selected stored recommendation and the feedback associated with the selected stored recommendation. 
     
     
         15 . The method of  claim 9 , wherein receiving drilling operations data includes querying data sources for drilling operations data relevant to the LLM prompt generator. 
     
     
         16 . The method of  claim 9 , the method further comprising receiving a query from the selected drilling platform controller and transmitting the recommendation to the selected drilling platform controller in response to the query, wherein the received query is a machine language query, and the response is a machine language response. 
     
     
         17 . The method of  claim 9 , the method further comprising receiving a query from the selected drilling platform controller and transmitting the recommendation to the selected drilling platform controller in response to the query, wherein the received query is a natural language query, and the response is a natural language response. 
     
     
         18 . A drilling platform controller, comprising:
 a processor; and   a memory connected to the processor, the memory including instructions that, when executed by the processor, cause the processor to:
 transmit current drilling parameters to a large language model (LLM) recommendation system; 
 receive, from the LLM recommendation system, a recommendation, the recommendation including recommended changes to one or more drilling parameters; 
 change one or more of the drilling parameters based on the recommended changes; and 
 transmit feedback on efficacy of the recommendation to the LLM recommendation system. 
   
     
     
         19 . The drilling platform controller of  claim 18 , wherein changing one or more of the current drilling parameters based on the recommendation includes changing one or more of Weight on Bit (WOB), Rotations per Minute (RPM) or flowrate. 
     
     
         20 . The drilling platform controller of  claim 18 , wherein the current drilling parameters include one or more of WOB, Rate of Penetration (ROP), RPM, flowrate, or inclination.