Halliburton Energy Services, Inc.

Jianlei Sun

Jianfu Ma

Dwight David Fulton

Tirumani N Swaminathan

Travis Hope Larsen

Terry Wayne Wong

Aaron Gene Russell

Cesar Edmundo Meza

Paul Ashcraft

Youli Mao

Ajish Sreeni Radhakrishnan Potty

Ron Dusterhoft

Peng Zhang

Disclosed are systems and methods for obtaining input data comprising properties associated with at least one parent well and a child well associated with the at least one parent well, dividing the input data into a training data subset, a validation data subset, and a test data subset, selecting at least one machine learning model using the training data subset, the validation data subset, and the test data subset based on k-fold cross validation, tuning hyper parameters for each of the at least one machine learning model, and generating a learning output using the at least one machine learning model and the hyper parameters for each of the at least one machine learning model, the learning output indicating a test root-mean-square error (RMSE) and a training RMSE.

Systems and methods for forecasting well interference

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

System and methods of optimizing proppant placement for stimulation treatments are provided. Properties of a reservoir formation and a treatment fluid to be injected into the formation are determined for a multistage stimulation treatment to be performed along a wellbore drilled within the formation. A proppant transport model uses the properties to determine a proppant profile for a fractured area of the formation for each treatment stage along the wellbore. A proppant pack conductivity for each stage is determined based on a proppant conductivity model and the proppant profile for that stage. A rate of hydrocarbon production expected from the wellbore is estimated based on a well production model and the proppant pack conductivity. A distribution of proppant to be injected into the formation during the treatment is determined, based on the production rate and one or more constraints. The stimulation treatment is performed based on the determined proppant distribution.

Optimizing proppant placement for fracturing operations

Certain aspects and features relate to a system that projects an optimized foam-fluid-application scenario for to stimulate production from a hydrocarbon well. The optimized scenario can include a recommended chemical make-up for the foam entity as well as application parameters such those related to timing and duration. A hybrid discrete fracture network and multi-porosity (DFN-MP) model for fluid interaction with the formation where the well is located can be produced by a processing device. The hybrid DFN-MP model can be optimized using field simulation data for the formation. The optimized hybrid DFN-MP model can be used by the processing device to produce an optimized foam-fluid-application scenario, which can be communicated to at least one well and can be utilized to stimulate the well for increased production.

Optimized foam application for hydrocarbon well stimulation

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Katy, TX, United States of America

Jianlei Sun