US 12,442,284 B2
Hydraulic fracturing job plan real-time revisions utilizing collected time-series data
Peyman Heidari, College Station, TX (US); Manisha Bhardwaj, Houston, TX (US); Harold Grayson Walters, Tomball, TX (US); and Dwight David Fulton, Cypress, TX (US)
Assigned to Halliburton Energy Services, Inc., Houston, TX (US)
Appl. No. 17/297,310
Filed by Halliburton Energy Services, Inc., Houston, TX (US)
PCT Filed Dec. 27, 2018, PCT No. PCT/US2018/067688
§ 371(c)(1), (2) Date May 26, 2021,
PCT Pub. No. WO2020/139346, PCT Pub. Date Jul. 2, 2020.
Prior Publication US 2022/0025753 A1, Jan. 27, 2022
Int. Cl. E21B 44/00 (2006.01); E21B 43/26 (2006.01); E21B 49/00 (2006.01)
CPC E21B 44/00 (2013.01) [E21B 43/26 (2013.01); E21B 49/005 (2013.01); E21B 2200/20 (2020.05); E21B 2200/22 (2020.05)] 24 Claims
OG exemplary drawing
 
1. A method to revise a hydraulic fracturing (HF) job plan for directing operations of well site equipment for a well, comprising:
processing a final first data set, wherein the final first data set is computed from a preliminary first data set comprising estimated production values, reporting issues of a production well, and one or more key performance indicators (KPIs);
automatically detecting, utilizing a first trained machine learning model, presence and timeframes of HF events in a time-series data set that includes HF pumping data from surface equipment of the well generated from execution of the HF job plan at the well, wherein the HF pumping data includes data at regular and irregular time intervals up to a designated time t, the timeframes are the start and end times of the HF events determined from the time-series data set, and one or more of the HF events are automatically detected using a grouping of the time-series data;
building a predictive model, utilizing a second trained machine learning model and a predictive data set comprising both of the final first data set and the HF events and timeframes detected from the time-series data set, wherein the predictive model identifies different combinations of controllable features from the time-series pumping data that impact different ones of the one or more KPIs; and
revising, utilizing the predictive model, the HF job plan at a time T during the execution of the HF job plan at the well, wherein T>t and the revising includes adjusting one of the different combinations of controllable features for a particular one of the different ones of the one or more KPIs.