Landmark Graphics Corporation

Halliburton Energy Services, Inc.

Youli Mao

Ashwani Dev

Satyam Priyadarshy

Bhaskar Mandapaka

Srinath Madasu

Keshava Prasad Rangarajan

Jianfu Ma

Dwight David Fulton

Fan Jiang

Shang Zhang

Suresh Venugopal

Feifei Zhang, Jr

Jianlei Sun

Phil Norlund

Hewei Tang

Zhili Wei

Meng Meng

Raja Vikram Pandya

Peng Zhang

Feifei Zhang

An abnormal trend detection system for detecting one or more hazards may provide for a safe and effective drilling operation as any of the one or more hazards may be avoided. Several indicators may be defined including a first, second, third and fourth indicator. The indicators are used to identify in a trend analysis abnormal trends. One or more thresholds may be defined. When a trend analysis indicates that a threshold has been reached or exceeded an alarm may be triggered, a drilling operation may be altered or a combination thereof.

Automatic abnormal trend detection of real time drilling data for hazard avoidance

Hydrocarbon exploration and extraction can be facilitated using machine-learning models. For example, a system described herein can receive seismic data indicating locations of geological bodies in a target area of a subterranean formation. The system can provide the seismic data as input to a trained machine-learning model for determining whether the target area of the subterranean formation includes one or more types of geological bodies. The system can receive an output from the trained machine-learning model indicating whether or not the target area of the subterranean formation includes the one or more types of geological bodies. The system can then execute one or more processing operations for facilitating hydrocarbon exploration or extraction based on the seismic data and the output from the trained machine-learning model.

Facilitating hydrocarbon exploration and extraction by applying a machine-learning model to seismic data

Disclosed are systems and methods for obtaining an input sequence of input data features associated with a well for at least one time stamp during a period of time including well production rates for the well and well operation constraints for the well, dividing the input data features into a training data subset, a validation data subset, and a test data subset, building a well production model for the well using machine learning based on the training data subset, the validation data subset, and the test data subset, and generating a forecast for the well for a future period of time using the well production model, the forecast comprising a future well production rate for the well including at least one of an oil rate, a gas rate, and a water rate.

Systems and methods for forecasting well productivity

A method for determining a position of a geological feature in a formation includes acquiring a seismic dataset, wherein the seismic dataset is based on signals of one or more seismic sensors and determining a set of indicators of candidate discontinuities in the formation based on the seismic dataset. The method also includes labeling a subset of the set of indicators of candidate discontinuities using a neural network with a label based on the set of indicators of candidate discontinuities, wherein the label distinguishes an indicator of a candidate discontinuity between being an indicator of a target discontinuity or being an indicator of a non-target discontinuity and determining the position of the geological feature in the formation, wherein the geological feature in the formation is associated with at least one target discontinuity based on the subset of the set of indicators of candidate discontinuities.

Fault detection based on seismic data interpretation

A method includes receiving a training selection of a first set of faults located in a first subset of a seismic dataset for a subsurface geologic formation, detecting a second set of faults in the seismic dataset based on fault interpretation operations using a first set of interpretation parameters, and determining a difference between the first set of faults and the second set of faults. The method also includes generating a second set of interpretation parameters for the fault interpretation operations based on the difference between the first set of faults and the second set of faults, and determining a feature of the subsurface geologic formation based on fault interpretation operations using the second set of interpretation parameters.

Hybrid optimization of fault detection and interpretation

Growing community of inventors

Houston, TX, United States of America

Youli Mao