Halliburton Energy Services, Inc.

Sas Institute Inc.

Richa Chauhan

Harold Grayson Walters

Dwight David Fulton

Manisha Bhardwaj

Rama Subba Reddy Thavva

Ajish Sreeni Radhakrishnan Potty

Vikesh Singh Baghel

Tae Yoon Lee

Arnulfo D De Castro

Hemil Shah

Harish Yadav

Kanchan Kamat

In one example, a system can receive an input from a user indicating a target variable to be forecasted over a future time window. The system can then determine independent variables that influence the target variable and generate a set of candidate variables, including combinations of the independent variables. The system can then execute a random forest classifier to identify a subset of candidate variables having a threshold level of influence on the target variable. The system can then construct a machine-learning model configured to receive the identified subset of candidate variables as inputs and generate a forecast of the target variable. After constructing the machine-learning model, the system can train the machine-learning model using historical data and then execute the machine-learning model to generate the forecast.

Deep learning model for energy forecasting

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

Growing community of inventors

Pune, India

Richa Chauhan