Halliburton Energy Services, Inc.

Halliburton Services

Xusong Wang

Xiang Wu

Junwen Dai

Christopher Michael Jones

Ahmed Elsayed Fouda

Dinesh Ananda Shetty

Srividhya Sridhar

Wei Zhang

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.

Three-dimensional inversion of multi-component electromagnetic measurements using a fast proxy model

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.

Real-time fracture monitoring, evaluation and control

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.

Multi-channel machine learning model-based inversion

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.

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Singapore, Singapore

Xusong Wang