Abstract: System and methods for automated seismic processing are provided. Historical seismic project data associated with one or more historical seismic projects is obtained from a data store. The historical seismic project data is transformed into seismic workflow model data. At least one seismic workflow model is generated using the seismic workflow model data. Responsive to receiving seismic data for a new seismic project, an optimized workflow for processing the received seismic data is determined based on the at least one generated seismic workflow model. Geophysical parameters for processing the seismic data with the optimized workflow are selected. The seismic data for the new seismic project is processed using the optimized workflow and the selected geophysical parameters.

Abstract: System and methods for automated seismic processing are provided. Historical seismic project data associated with one or more historical seismic projects is obtained from a data store. The historical seismic project data is transformed into seismic workflow model data. At least one seismic workflow model is generated using the seismic workflow model data. Responsive to receiving seismic data for a new seismic project, an optimized workflow for processing the received seismic data is determined based on the at least one generated seismic workflow model. Geophysical parameters for processing the seismic data with the optimized workflow are selected. The seismic data for the new seismic project is processed using the optimized workflow and the selected geophysical parameters.

Abstract: Disclosed are systems and methods for receiving historical production data associated with at least one hydraulic fracturing well, receiving time-series data associated with the at least one hydraulic fracturing well, the time-series data representing at least one type of data, receiving non-temporal data associated with the at least one hydraulic fracturing well, generating a machine learning model based on the historical production data, the time-series data associated with the at least one hydraulic fracturing well and based on an original job design during a first stage of the job at a particular hydraulic fracturing well, and the non-temporal data, determining an optimized job design for the particular hydraulic fracturing well having an objective function using a prediction based on the machine learning model, and implementing the optimized job design for the particular hydraulic fracturing well.

Abstract: A wellbore trajectory survey, having an ordered plurality of survey points, is acquired. Each of the plurality of survey points has a measured depth, an inclination, and a geographic location of the point in the wellbore corresponding to the survey point. The wellbore trajectory survey is ordered on measured depth. The processor identifies a minimum lateral measured depth (min_LMD), a maximum lateral measured depth (max_LMD). The processor identifies a mid-lateral point (mid_LMP) in the plurality of survey points whose measured depth (mid_LMD) is greater than min_LMD and less than max_LMD.

Abstract: A wellbore trajectory survey includes an ordered plurality of survey points, each of which has a measured depth, an inclination, and a geographic location. The wellbore trajectory survey is ordered on measured depth. A minimum lateral measured depth (min_LMD) as a measured depth of a first point in the ordered plurality of survey points for which the inclination exceeds an inclination threshold angle is identified. A maximum lateral measured depth (max_LMD) as a measured depth of a last point in the ordered plurality of survey points for which the inclination exceeds the inclination threshold angle is identified. The processor determines from the wellbore trajectory survey that the wellbore at measured depths between the min_LMD and the max_LMD exhibits porpoising into and out of a formation.

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: 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: A wellbore trajectory survey includes an ordered plurality of survey points, each of which has a measured depth, an inclination, and a geographic location. The wellbore trajectory survey is ordered on measured depth. A minimum lateral measured depth (min_LMD) as a measured depth of a first point in the ordered plurality of survey points for which the inclination exceeds an inclination threshold angle is identified. A maximum lateral measured depth (max_LMD) as a measured depth of a last point in the ordered plurality of survey points for which the inclination exceeds the inclination threshold angle is identified. The processor determines from the wellbore trajectory survey that the wellbore at measured depths between the min_LMD and the max_LMD exhibits porpoising into and out of a formation.

Abstract: Disclosed are systems and methods for receiving historical production data associated with at least one hydraulic fracturing well, receiving time-series data associated with the at least one hydraulic fracturing well, the time-series data representing at least one type of data, receiving non-temporal data associated with the at least one hydraulic fracturing well, generating a machine learning model based on the historical production data, the time-series data associated with the at least one hydraulic fracturing well and based on an original job design during a first stage of the job at a particular hydraulic fracturing well, and the non-temporal data, determining an optimized job design for the particular hydraulic fracturing well having an objective function using a prediction based on the machine learning model, and implementing the optimized job design for the particular hydraulic fracturing well.

Abstract: A wellbore trajectory survey, having an ordered plurality of survey points, is acquired. Each of the plurality of survey points has a measured depth, an inclination, and a geographic location of the point in the wellbore corresponding to the survey point. The wellbore trajectory survey is ordered on measured depth. The processor identifies a minimum lateral measured depth (min_LMD), a maximum lateral measured depth (max_LMD). The processor identifies a mid-lateral point (mid_LMP) in the plurality of survey points whose measured depth (mid_LMD) is greater than min_LMD and less than max_LMD.