Abstract: Certain aspects of the disclosure provide a method that includes obtaining one or more hybrid modeling frameworks based on a model, each hybrid modeling framework comprising one or more hybrid models; performing triage to select a hybrid modeling framework of the one or more hybrid modeling frameworks that is applicable to the system of interest based on associated metrics and the input data; training each respective hybrid model of the one or more hybrid models of the hybrid modeling framework based on the input data to obtain one or more trained hybrid models configured to model behavior of the system of interest; and determining a rank order of the one or more trained hybrid models based on a benchmark set of data associated with the system of interest.

Abstract: Systems and methods for evaluating remaining useful life (RUL) prediction algorithms, for example, in the absence of run-to-failure ground truth data, are presented herein. For example, the systems and methods presented herein are configured to receive data relating to operation of equipment from one or more sensors associated with the equipment; predict an RUL of the equipment based at least in part on the received data; and evaluate an accuracy of the predicted RUL of the equipment during operation of the equipment.

Abstract: The description is directed to a method of visualization, involving capturing a multitude of two-dimensional images, generating transforms for each of the multitude of two-dimensional images, generating a three-dimensional representation of an object or scene based on the transforms, where the three-dimensional representation is a novel view, and rendering the three-dimensional representation of the object or scene.

Abstract: A method for performing production engineering tasks includes receiving a question or instruction related to an oil and/or gas industry. The question or instruction is received by a large language model (LLM) agent. The method also includes selecting one or more tools from a plurality of tools using the LLM agent based upon the question or instruction. The tools include a data tool, a retrieval augmented generation (RAG) tool, a simulation tool, and an analytical tool. The method also includes generating output data related to the question or instruction using the one or more identified tools. The method also includes generating a response to the question or instruction using the LLM agent based upon output data.

Abstract: A method for simulating a process in a new or existing oil and/or gas processing facility includes receiving a user prompt from a user with an application. The method also includes transmitting the user prompt from the application to a trained large language model (LLM) agent. The method also includes transmitting suggested tool values and/or suggested parameter values from the trained LLM agent to the application based upon the user prompt. The method also includes transmitting a tool request from the application to a simulator based upon and/or in response to the suggested tool values and/or the suggested parameter values. The method also includes transmitting a tool output from the simulator to the application to the trained LLM agent based upon and/or in response to the tool request. The method also includes generating a response based upon the user prompt and the tool output using the trained LLM agent.

Abstract: A method for performing generative artificial intelligence (AI)-enabled multimodal prompt querying on subsurface models includes receiving input data. The input data includes seismic data that represents a subsurface formation. The method also includes generating a plurality of images based upon the input data. The method also includes extracting first image embeddings based upon the plurality of images. The method also includes storing the first image embeddings in a vector database. The method also includes receiving an input prompt. The method also includes extracting a prompt embedding based upon the input prompt. The method also includes storing the prompt embedding in the vector database. The method also includes identifying a similar one of the images based upon the prompt embedding.

Abstract: A method may include receiving, via one or more processors, a set of image data representative of equipment configured to distribute a gas. The method may then involve determining a type of equipment depicted in the first set of image data, retrieving a leak detection model corresponding to the type of equipment depicted in the first set of image data, and determining that a gas leak is present on the equipment based on the set of image data and the leak detection model. After determining that the gas leak is present, the method may include sending a notification to a computing device in response to detecting the gas leak.

Abstract: Certain aspects of the disclosure provide systems and methods for AutoPHM. A method includes receiving system of interest data associated with an system of interest to generate a simulation model output; receiving system of interest production data from a production SOI to generate an anomaly detection model output; receiving at least one of the simulation model output, the system of interest production data, a hypothesized future input, or the anomaly detection model output to generate an estimated future output; and determining, a remaining useful life prediction of the production system of interest based on the estimated future output.

Abstract: Systems and methods for adapting models of physical systems using transfer learning or adaptation techniques (e.g., Jacobian Feature Regression) in both online and offline modes are presented herein. The systems and methods presented herein extend implementation of transfer learning or adaptation techniques to physics-informed neural networks modeled using a state-space formulation, demonstrate that transfer learning or adaptation techniques is more sustainable than other retraining and transfer learning methods, demonstrate how an offline adaptation approach may be modified into an online adaptation technique, and demonstrate the application of online and offline adaptation algorithms on applications relevant to the oil and gas industry, such as membranes, compressors, and so forth.

Abstract: Systems and methods for estimating Remaining Useful Life (RUL) of equipment based on adaptive system representation, for example, using Jacobian Feature Adaptation are presented herein. The systems and methods presented herein demonstrate a workflow to utilize an adaptation technique (offline as well as online) in order to have a more accurate and robust estimation of RUL of equipment for data-driven and hybrid models without needing to build multiple fault propagation models or having to retrain the model from scratch after collecting a sufficient amount of failure data, and demonstrate the application of the online and offline adaptation algorithms on applications relevant to the oil and gas industry, such as membranes, compressors, and so forth.

Abstract: Dynamic offset well analysis includes receiving a selection of a set of parameters on a well plan comparison tool displayed in a graphical user interface (GUI). For each parameter that is selected, a selection of a set of weights is received from the well plan comparison tool. Dynamic offset well analysis also includes weighting parameter values for objects according to the set of weights. Dynamic offset well analysis additionally includes analyzing the objects according to the weighted parameter values to form clusters. The clusters are then displayed on the well plan comparison tool in the GUI.

Abstract: A method includes generating one or more hybrid physics models each configured to predict a value for a drilling condition based on training data, training a machine learning model to predict a drilling condition severity based on the training data and the value of the drilling condition predicted by the one or more hybrid physics models, receiving sensor data representing present drilling data, predicting the drilling condition, based at least in part on the sensor data, using the hybrid physics model, and predicting the drilling condition severity, based at least in part on the drilling condition that was predicted and the sensor data, using machine learning model that was trained.

Abstract: A method may include receiving, via one or more processors, a set of image data representative of equipment configured to distribute a gas. The method may then involve determining a type of equipment depicted in the first set of image data, retrieving a leak detection model corresponding to the type of equipment depicted in the first set of image data, and determining that a gas leak is present on the equipment based on the set of image data and the leak detection model. After determining that the gas leak is present, the method may include sending a notification to a computing device in response to detecting the gas leak.

Abstract: Systems and methods for generating digital gamma-ray logs for target wells based on combined physics and machine learning model using real-time information (e.g., drilling parameters, survey data, gamma-ray logs, and so forth) obtained from offset wells analogous to the subject well in terms of gamma-ray readings. The systems and methods may provide solutions that may lower the cost of Measuring While Drilling (MWD) and/or Logging While Drilling (LWD) process and facilitate the users (e.g., drillers, geoscientists, and so forth) to make enhanced data driven decisions.

Abstract: The description is directed to a method of visualization, involving capturing a multitude of two-dimensional images, generating transforms for each of the multitude of two-dimensional images, generating a three-dimensional representation of an object or scene based on the transforms, where the three-dimensional representation is a novel view, and rendering the three-dimensional representation of the object or scene.