Halliburton Energy Services, Inc.

Ajish Sreeni Radhakrishnan Potty

Dwight David Fulton

Harold Grayson Walters

Jianfu Ma

Zheng Lu

Jody Marie Burks

Manisha Bhardwaj

Jianlei Sun

Rama Subba Reddy Thavva

Vikesh Singh Baghel

Richa Chauhan

The disclosure is directed to methods to design and revise hydraulic fracturing (HF) job plans. The methods can utilize one or more data sources from public, proprietary, confidential, and historical sources. The methods can build mathematical, statistical, machine learning, neural network, and deep learning models to predict production outcomes based on the data source inputs. In some aspects, the data sources are processed, quality checked, and combined into composite data sources. In some aspects, ensemble modeling techniques can be applied to combine multiple data sources and multiple models. In some aspects, response features can be utilized as data inputs into the modeling process. In some aspects, time-series extracted features can be utilized as data inputs into the modeling process. In some aspects, the methods can be used to build a HF job plan prior to the start of work at a well site. In other aspects, the methods can be used to revise an existing HF job plan in real-time, such as after a treatment cycle, a pumping stage, or a time interval.

Hydraulic fracturing operation planning using data-driven multi-variate statistical machine learning modeling

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

A method of flow testing can include dispensing at least one solid material and at least one liquid into each of multiple wells in a filter plate, and then heating the filter plate with the solid material and liquid therein to an elevated temperature. A flow testing system can include a multiple well filter plate, each of the wells having disposed therein a selected combination of formation particles, a fracturing fluid and a liquid hydrocarbon, and a heating device that heats the filter plate to an elevated temperature.

Multiple well elevated temperature column flow testing

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Houston, TX, United States of America

Ajish Sreeni Radhakrishnan Potty