Methods for Managing Pump-lifted Wells in Hydrocarbon Fields

Abstract

A methodology for integrated pump and well data for managing pump-lifted wells in hydrocarbon fields is provided. Rod pumps are widely applied to various types of wells to provide uplift and may play a dominant role in the flow of hydrocarbon from reservoir to surface. Assess the state of each well and its components (including the associated rod pump) is important for optimal well and reservoir management for fields with artificially lifted wells. As such, a methodology is disclosed that performs machine learning in order to generate a machine-learned model using pump card data and at least one of well data, reservoir data, flowline data, user input data, or data generated from analysis of one or more of the well data, the reservoir data, the flowline data, or the user input data.

What is claimed is: 
     
         1 . A computer-implemented method for managing one or more of a well, a rod pump, or a well pad for hydrocarbon extraction, the method comprising:
 performing machine learning in order to generate a machine-learned model using pump card data and at least one of well data, reservoir data, flowline data, user input data, or data generated from analysis of one or more of the well data, the pump card data, the reservoir data, the flowline data, or the user input data;   predicting, by the machine-learned model, at least one aspect of the well, the rod pump, or the well pad; and   using the predicted at least one aspect for hydrocarbon management.   
     
     
         2 . The method of  claim 1 , wherein using the machine-learned model in order to predict the at least one aspect of the well or the rod pump comprising generating a recommended action regarding the one or both of the well, the rod pump, or the well pad. 
     
     
         3 . The method of  claim 2 , wherein the recommended action regarding the rod pump comprises at least one of pump change, pump check, pump recalibration, modification of pump speed, or shutting in the well. 
     
     
         4 . The method of  claim 3 , wherein the recommended action comprises at least one of well stimulation, modifying operation of the rod pump in at least one aspect, or modifying a measurement tool that generates the pump card data. 
     
     
         5 . The method of  claim 1 , wherein the at least one aspect of the well and of the rod pump are predicted; and
 wherein the at least one aspect of the well and of the rod pump are used for hydrocarbon management.   
     
     
         6 . The method of  claim 1 , wherein the user input data comprises user-generated data related to servicing or modifying one or more of the rod pump, the well, or a pump card. 
     
     
         7 . The method of  claim 1 , wherein the user input data comprises one or more of servicing the rod pump with a service rig, replacing equipment using the service rig, modifying position of the rod pump, or cleaning a wellbore. 
     
     
         8 . The method of  claim 1 , wherein the machine learning uses pump card data and the data generated from analysis of one or more of the well data, the reservoir data, the flowline data, or the user input data; and
 wherein the data generated from analysis of the one or more of the well data, the reservoir data, the flowline data, or the user input data comprises one or both of trend data or difference data.   
     
     
         9 . The method of  claim 1 , wherein the machine learning uses pump card data and the data generated from analysis of two or more of the well data, the reservoir data, the flowline data, or the user input data; and
 wherein the data generated from analysis of the two or more of the well data, the reservoir data, the flowline data, or the user input data comprises trend data.   
     
     
         10 . The method of  claim 1 , wherein the machine learning uses pump card data and the data generated from analysis of three or more of the well data, the reservoir data, the flowline data, or the user input data; and
 wherein the data generated from analysis of the three or more of the well data, the reservoir data, the flowline data, or the user input data comprises trend data.   
     
     
         11 . The method of  claim 1 , wherein the machine learning is performed using the well data and the reservoir data;
 wherein the well data comprises sensor data associated with the well, well type, geometry of the well, and well test history; and   wherein the reservoir data comprises at least one of pressure, volume or temperature.   
     
     
         12 . The method of  claim 1 , wherein the machine learning is performed using the pump card data generated by a pump card, maintenance data for the pump card, and calibration data for the pump card. 
     
     
         13 . The method of  claim 1 , wherein the machine-learned model generates:
 predictive status of one or both of the well or a pump;   one or more recommended actions associated with the well or the pump; and   associated loss or gain in response to performing the one or more recommended actions.   
     
     
         14 . The method of  claim 1 , wherein the machine-learned model is retrained responsive to receipt of the user input data indicative of user input or sensor input regarding the predicted at least one aspect of the well or the rod pump. 
     
     
         15 . The method of  claim 1 , further comprising generating a user interface in order to label one or more of the well data, the reservoir data, the flowline data, or the user input data. 
     
     
         16 . The method of  claim 15 , wherein the user interface displays the pump card data relative to the one or more of the well data, the reservoir data, the flowline data, or the user input data. 
     
     
         17 . The method of  claim 16 , wherein the one or more of the well data, the reservoir data, the flowline data, or the user input data is displayed along with the pump card data in a time series with a same reference time scale. 
     
     
         18 . The method of  claim 16 , wherein the one or more of the well data, the reservoir data, the flowline data, or the user input data and the pump card data are superimposed on one another and displayed in a time series with a same reference time scale. 
     
     
         19 . The method of  claim 1 , wherein the machine-learned model is configured to perform a series of discrete tasks including at least: (1) determining whether pump cards are providing accurate measurements; and (2) responsive to determining that the pump card is providing accurate measurement, determining at least one aspect of performance of the well. 
     
     
         20 . The method of  claim 19 , wherein the series of discrete tasks further include: (3) responsive to (1) and (2), determining one or more actions to improve operations for the hydrocarbon extraction.