Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed are systems and methods for receiving surface data and downhole sensor data associated with at least one first hydraulic fracturing well, generating a prediction model for the at least one first hydraulic fracturing well that determines a prediction for a subsurface value for the at least one first hydraulic fracturing well, generating an error model for the at least one first hydraulic fracturing well that determines an estimated prediction error between the prediction for the subsurface value for the at least one first hydraulic fracturing well and an actual subsurface value for the at least one first hydraulic fracturing well, determining a status of at least one feature associated with the estimated prediction error between a prediction for a subsurface value for at least one second hydraulic fracturing well and an actual subsurface value for the at least one second hydraulic fracturing well, and collecting additional downhole sensor data.

Abstract: 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.

Abstract: 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.

Abstract: 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.