Large Language Model Interface for Wellbore Cement Job Design

Abstract

A method may include: providing one or more inputs to a hybrid data generator, wherein one of the one or more inputs is based at least in part on a wellsite location, wherein the hybrid data generator comprises a large language model, and wherein the large language model is based at least in part on a machine learning algorithm; utilizing an information handling system to generate a cement job design based at least in part on the one or more inputs and the hybrid data generator; performing at least a portion of a cementing operation based at least in part on the cement job design; and collecting at least one measurement from at least one sensor during the cementing operation.

What is claimed is: 
     
         1 . A method comprising:
 providing one or more inputs to a hybrid data generator, wherein one of the one or more inputs is based at least in part on a wellsite location, wherein the hybrid data generator comprises a large language model, and wherein the large language model is based at least in part on a machine learning algorithm;   utilizing an information handling system to generate a cement job design based at least in part on the one or more inputs and the hybrid data generator; and   performing at least a portion of a cementing operation based at least in part on the cement job design.   
     
     
         2 . The method of  claim 1 , wherein the large language model is trained using a dataset comprising at least one type of data selected from the group consisting of engineering data, geological data, geo-mechanical data, geo-physical data, data from lab-based tests, data modelled from simulations, data modelled from empirical models, data modelled from physics-based models, data from physics-informed neural networks, operational data from current cementing operations, operational data from previous cementing operations, measurements collected from current cementing operations, measurements collected from previous cementing operations, information collected from previous cementing reports, previously created cement job design, logging data, available equipment in a given region, and combinations thereof. 
     
     
         3 . The method of  claim 1 , wherein the one or more inputs further comprises at least one input selected from the group consisting of engineering data, geological data, geo-mechanical data, geo-physical data, data from lab-based tests, data modelled from simulations, data modelled from empirical models, data modelled from physics-based models, data from physics-informed neural networks, operational data from current cementing operations, operational data from previous cementing operations, measurements collected from current cementing operations, measurements collected from previous cementing operations, information collected from previous cementing reports, previously created cement job design, logging data, available equipment in a given region, and combinations thereof. 
     
     
         4 . The method of  claim 1 , wherein training the large language model further comprises reinforcement learning. 
     
     
         5 . The method of  claim 1 , wherein the machine learning algorithm is utilized in a transformer architecture. 
     
     
         6 . The method of  claim 5 , wherein the transformer architecture includes at least one architecture component selected from the group consisting of an encoder, a decoder, and combinations thereof. 
     
     
         7 . The method of  claim 1 , wherein the machine learning algorithm comprises a deep learning algorithm further comprising at least one type of algorithm selected from the group consisting of convolutional neural networks, long short term memory networks, recurrent neural networks, generative adversarial networks, attention neural networks, zero-shot models, fine-tuned models, domain-specific models, multi-modal models, transformer architectures, radial basis function networks, multilayer perceptrons, self-organizing maps, deep belief networks, and combinations thereof. 
     
     
         8 . The method of  claim 1 , further comprising updating the cement job design using the at least one measurement collected from the at least one sensor during the cementing operation, wherein the at least one measurement is added to the inputs provided to the hybrid data generator. 
     
     
         9 . The method of  claim 8 , wherein updating the cement job design comprises updating the cement job design using at least one method selected from the group consisting of continuously updating the cement job design, updating the cement job design at set intervals of time, updating the cement job design when manually executed, updating the cement job design when a threshold is met, or combinations thereof. 
     
     
         10 . The method of  claim 1 , wherein the large language model is optimized for at least one operational feature, wherein the at least one operational feature is at least one feature selected from the group consisting of fluid displacement, centralizer location, centralizer type, fluid composition, fluid volume, fluid rate, rheology, density, mixability, pumpability, thickening time, compressive strength, set time, fluid loss, fluid compatibility, temperature resistance, compatibility between subsequently pumped fluids, maximizing hole stability, minimizing total cementing cost, minimizing cost per wellbore section, minimizing time spent on each wellbore section, operational safety, and combinations thereof. 
     
     
         11 . The method of  claim 1  further comprising collecting at least one measurement from at least one sensor during the cementing operation. 
     
     
         12 . A system comprising:
 a hybrid data generator comprising a large language model, wherein the large language model is based at least in part on a machine learning algorithm;   an information handling system configured to execute the hybrid data generator to generate a cement job design, wherein the generated cement job design is based at least in part on one or more inputs and wherein at least one of the one or more inputs is based at least in part on a wellsite location; and   a sensor in communication with the information handling system, wherein the sensor measures at least one measurement during a cementing operation.   
     
     
         13 . The system of  claim 12 , wherein the large language model is trained using a dataset comprising at least one type of data selected from the group consisting of engineering data, geological data, geo-mechanical data, geo-physical data, data from lab-based tests, data modelled from simulations, data modelled from empirical models, data modelled from physics-based models, operational data from current cementing operations, operational data from previous cementing operations, measurements collected from current cementing operations, measurements collected from previous cementing operations, information collected from previous cementing reports, previously created cement job design, logging data, available equipment in a given region, and combinations thereof. 
     
     
         14 . The system of  claim 12 , wherein the one or more inputs further comprises at least one input selected from the group consisting of engineering data, geological data, geo-mechanical data, geo-physical data, data from lab-based tests, data modelled from simulations, data modelled from empirical models, data modelled from physics-based models, operational data from current cementing operations, operational data from previous cementing operations, measurements collected from current cementing operations, measurements collected from previous cementing operations, information collected from previous cementing reports, previously created cement job design, logging data, available equipment in a given region, and combinations thereof. 
     
     
         15 . The system of  claim 12 , wherein training the large language model further comprises reinforcement learning. 
     
     
         16 . The system of  claim 15 , wherein the machine learning algorithm is utilized in a transformer architecture and wherein the transformer architecture includes at least one architecture component selected from the group consisting of an encoder, a decoder, and combinations thereof. 
     
     
         17 . The system of  claim 12 , wherein the machine learning algorithm comprises a deep learning algorithm further comprising at least one type of algorithm selected from the group consisting of convolutional neural networks, long short term memory networks, recurrent neural networks, generative adversarial networks, attention neural networks, zero-shot models, fine-tuned models, domain-specific models, multi-modal models, transformer architectures, radial basis function networks, multilayer perceptrons, self-organizing maps, deep belief networks, and combinations thereof. 
     
     
         18 . The system of  claim 12 , wherein the information handling system is configured to update the cement job design based at least in part on the one measurement collected by the sensor during the cementing operation. 
     
     
         19 . The system of  claim 18 , wherein the information handling system is configured to update the cement job design using at least one method selected from the group consisting of continuously updating the cement job design, updating the cement job design at set intervals, updating the cement job design when manually executed, updating the cement job design when a threshold is met, or combinations thereof. 
     
     
         20 . The system of  claim 12 , wherein the large language model is optimized for at least one operational feature, wherein the at least one operational feature is at least one feature selected from the group consisting of fluid displacement, centralizer location, centralizer type, fluid composition, fluid volume, fluid rate, rheology, density, mixability, pumpability, thickening time, compressive strength, set time, fluid loss, fluid compatibility, temperature resistance, compatibility between subsequently pumped fluids, maximizing hole stability, minimizing total cementing cost, minimizing cost per wellbore section, minimizing time spent on each wellbore section, operational safety, and combinations thereof.