Abstract: Aspects of the subject technology relate to systems, methods, and computer readable media for applying generative machine learning for performing statistical inversion in borehole sensing. A method can comprise implementing an inversion workflow for borehole sensing. The method can also comprise applying a generative machine learning network technique to sample candidate material variables as part of the inversion workflow.

Abstract: Aspects of the disclosed technology provide solutions for analyzing and interpreting geophysical and petrophysical data and in particular, for using generative machine learning models to characterize and predict reservoir properties. A process of the disclosed technology can include steps for providing a set of formation measurement data to a generative machine learning model and generating, via the generative machine learning model, a set of latent space data corresponding to the set of formation measurement data. The process can further include steps for clustering the set of latent space data to generate a set of clusters and determining a petrophysical interpretation based on the clusters. Systems and machine-readable media are also provided.

Abstract: The present disclosure provides techniques to identify material properties from measured physical response data in ways that are more efficient than conventional numerical inversion. Systems and techniques of the present disclosure may use machine learning (ML) techniques to generate mappings that map specific physical responses to specific material properties based on knowledge of factors that may be inherent to certain types of sensing equipment. Data generated by a ML computer model may be constrained, altered, or filtered to more closely correspond to characteristics known to be associated with a certain type of sensing equipment. Such a constrained model may identify fewer possible results as compared to an unconstrained model, thereby, solving problems that may be encountered when using ML techniques to identify material properties from measured responses.

Abstract: Described herein are systems and techniques for an improved method for determining and evaluating an impedance of an annulus associated with a casing of a wellbore. For example, aspects of the present disclosure relate to systems and techniques for performing two-dimensional (2D) and/or three-dimensional (3D) simulations (e.g., 2D and 3D numerical modeling) for predicting physical properties of a material or sample and determining calibration functions used to improve the efficiency and accuracy of the determined impedance results.

Abstract: Described herein are systems and techniques for predicting sample characteristics in a wellbore. An example method can include determining a set of values of estimated characteristics of a sample in a wellbore; determining, via a proxy model, a predicted ultrasonic wave response corresponding to the set of values of the estimated characteristics of the sample; based on a comparison of the predicted ultrasonic wave response with a measured ultrasonic wave response associated with the sample, determining an error associated with the predicted ultrasonic wave response; determining whether the error associated with the predicted ultrasonic wave response is below a threshold; and determining whether to update the set of values of the estimated characteristics of the sample based on determining whether the error is below the threshold.

Abstract: Described herein are systems and techniques for monitoring for monitoring and evaluating conditions associated with a wellbore and wellbore operations that use neural operators instead of computationally intensive iterative differential equations. Such systems and techniques allow for determinations to be made as operations associated with a wellbore are performed. Instead of having to wait for computationally intensive tasks to be performed or take risks of proceeding with a wellbore operation without real-time evaluations being performed, these wellbore operations may be continued while determinations are timely made, thus improving operation of computing systems that perform evaluations and that make decisions regarding safely and efficiently performing wellbore operations such as drilling a wellbore, cementing wellbore casings in place, or injecting fluids into formations of the Earth.

Abstract: Systems and methods generally relate to monitoring, evaluating, and controlling fracture geometry during a hydraulic fracturing operation, in real time. A method comprises measuring a signal representing a condition in a wellbore; inputting the signal into a model for estimating a dimension of a dominant fracture; determining the dimension of the dominant fracture; determining a target dimension for the dominant fracture; and minimizing a difference between the dimension of the dominant fracture and the target dimension in real time, by adjusting at least an injection pressure or flow rate of a hydraulic fracturing fluid into the wellbore.

Abstract: A method for estimating a pipe property may include disposing an electromagnetic (EM) logging tool in a wellbore. The EM logging tool may comprise a transmitter disposed on the EM logging tool and a receiver disposed on the EM logging tool. The method may further include transmitting an electromagnetic field from the transmitter into a tubular string to energize the tubular string with the electromagnetic field thereby producing an eddy current that emanates from the tubular string. The method may further include measuring the eddy current in the tubular string with the receiver on at least one channel to obtain one or more measurements from the EM logging tool. The method may further include forming an EM log from the one or more measurements, forming a relationship between a set of one or more previously analyzed measurements and at least one pipe property, and estimating the at least one pipe property from the formed relationship and the one or more measurements from the EM logging tool.

Abstract: A method and non-transitory storage computer-readable medium for performing a neural operator on one or more wellbore measurements. The method may comprise o obtaining one or more measurements, performing a measurement normalization on the one or more measurements to form one or more normalized measurements, forming a material function with the one or more normalized measurements, and forming a neural operator generated physical response with a neural operator and the material function. The method may further comprise forming a beamforming map with the one or more measurements, and forming a neural operator leak source location map with a neural operator and the one or more measurements.

Abstract: Described herein are systems and techniques for monitoring for monitoring and evaluating conditions associated with a wellbore and wellbore operations that use neural operators instead of computationally intensive iterative differential equations. Such systems and techniques allow for determinations to be made as operations associated with a wellbore are performed. Instead of having to wait for computationally intensive tasks to be performed or take risks of proceeding with a wellbore operation without real-time evaluations being performed, these wellbore operations may be continued while determinations are timely made, thus improving operation of computing systems that perform evaluations and that make decisions regarding safely and efficiently performing wellbore operations such as drilling a wellbore, cementing wellbore casings in place, or injecting fluids into formations of the Earth.

Abstract: A method for identifying a collar using machine learning may include acquiring one or more measurements from one or more depth points within a wellbore including a tubular string, training a machine learning model using a training dataset to create a trained machine learning model, and identifying at least one hyperparameter using the trained machine learning model. The method may further include creating a synthetic model, wherein the synthetic model is defined by one or more pipe attributes, minimizing a mismatch between the one or more measurements and the synthetic model utilizing the at least one hyperparameter, updating the synthetic model to form an updated synthetic model, and repeating the minimizing the mismatch with the updated synthetic model until a threshold is met.

Abstract: A method for identifying a collar using machine learning may include acquiring one or more measurements from one or more depth points within a wellbore including a tubular string, training a machine learning model using a training dataset to create a trained machine learning model, and identifying at least one hyperparameter using the trained machine learning model. The method may further include creating a synthetic model, wherein the synthetic model is defined by one or more pipe attributes, minimizing a mismatch between the one or more measurements and the synthetic model utilizing the at least one hyperparameter, updating the synthetic model to form an updated synthetic model, and repeating the minimizing the mismatch with the updated synthetic model until a threshold is met.

Abstract: Systems and methods of the present disclosure generally relate to monitoring, evaluating, and controlling fracture geometry during a hydraulic fracturing operation, in real time. A method comprises measuring a signal representing a condition in a wellbore; inputting the signal into a model for estimating a dimension of a dominant fracture; determining the dimension of the dominant fracture; determining a target dimension for the dominant fracture; and minimizing a difference between the dimension of the dominant fracture and the target dimension in real time, by adjusting at least an injection pressure or flow rate of a hydraulic fracturing fluid into the wellbore.

Abstract: Systems and methods of the present disclosure generally relate to monitoring, evaluating, and controlling fracture geometry during a hydraulic fracturing operation, in real time. A method comprises measuring a signal representing a condition in a wellbore; inputting the signal into a model for estimating a dimension of a dominant fracture; determining the dimension of the dominant fracture; determining a target dimension for the dominant fracture; and minimizing a difference between the dimension of the dominant fracture and the target dimension in real time, by adjusting at least an injection pressure or flow rate of a hydraulic fracturing fluid into the wellbore.

Abstract: Systems and methods of the present disclosure generally relate to monitoring, evaluating, and controlling fracture geometry during a hydraulic fracturing operation, in real time. A method comprises measuring a signal representing a condition in a wellbore; inputting the signal into a model for estimating a dimension of a dominant fracture; determining the dimension of the dominant fracture; determining a target dimension for the dominant fracture; and minimizing a difference between the dimension of the dominant fracture and the target dimension in real time, by adjusting at least an injection pressure or flow rate of a hydraulic fracturing fluid into the wellbore.