A method for recording data relating to the performance of an oil and gas flow network uses statistical data to represent raw data in a compact form. Categories are assigned to time intervals in the data. The method comprises: (1) gathering data covering a period of time, wherein the data relates to the status of one or more control point(s) within the flow network and to one or more flow parameter(s) of interest in one or more flow path(s) of the flow network; (2) identifying multiple time intervals in the data during which the control points and the flow parameter(s) can be designated as being in a category selected from multiple categories; (3) assigning a selected category of the multiple categories to each one of the multiple datasets that are framed by the multiple time intervals; and (4) extracting statistical data representative of some or all of the datasets identified in step (2) to thereby represent the original data from step (1) in a compact form including details of the category assigned to each time interval in step (3).

A method for recording data relating to the performance of an oil and gas flow network uses statistical data to represent raw data in a compact form. Categories are assigned to time intervals in the data. The method comprises: (1) gathering data covering a period of time, wherein the data relates to the status of one or more control point(s) within the flow network and to one or more flow parameter(s) of interest in one or more flow path(s) of the flow network; (2) identifying multiple time intervals in the data during which the control points and the flow parameter(s) can be designated as being in a category selected from multiple categories; (3) assigning a selected category of the multiple categories to each one of the multiple datasets that are framed by the multiple time intervals; and (4) extracting statistical data representative of some or all of the datasets identified in step (2) to thereby represent the original data from step (1) in a compact form including details of the category assigned to each time interval in step (3).

A method and apparatus for forecasting oil production from an oil well in a geological formation includes receiving a plurality of sets of predicted geological data, for each of the plurality of sets of predicted geological data, determining a probability for the predicted geological data of the formation, iteratively selecting one of the plurality of sets of predicted geological data using Monte Carlo sampling based on the determined probabilities, assigning the selected set of predicted geological data to a cluster of historical data, and for each set of historical data of the cluster generating a predicted oil production rate as a function of time utilizing a machine learning based oil model, generating, based on the predicted oil production rates, a forecasted oil production rate, determining, based on the forecasted oil production rate, a preferred operating parameter for the well, and operating based on the preferred operating parameter.

A method and apparatus for forecasting oil production from an oil well in a geological formation includes receiving a plurality of sets of predicted geological data, for each of the plurality of sets of predicted geological data, determining a probability for the predicted geological data of the formation, iteratively selecting one of the plurality of sets of predicted geological data using Monte Carlo sampling based on the determined probabilities, assigning the selected set of predicted geological data to a cluster of historical data, and for each set of historical data of the cluster generating a predicted oil production rate as a function of time utilizing a machine learning based oil model, generating, based on the predicted oil production rates, a forecasted oil production rate, determining, based on the forecasted oil production rate, a preferred operating parameter for the well, and operating based on the preferred operating parameter.

A method of probabilistically estimating a velocity of a plunger of a beam pump may comprise continuously monitoring well acoustics using a plurality of passive acoustic sensors attached to external structures of the beam pump; digitizing outputs of the plurality of passive acoustic sensors and sending the digitized outputs to a computing device for storage and processing; and using the digitized outputs of the plurality of passive acoustic sensors, estimating a probability of the velocity of the plunger using a hidden Markov model (HMM) to represent a probability of a position and the probability of the velocity of the plunger, the HMM comprising a state space model and an observational model.

A method of probabilistically estimating a velocity of a plunger of a beam pump may comprise continuously monitoring well acoustics using a plurality of passive acoustic sensors attached to external structures of the beam pump; digitizing outputs of the plurality of passive acoustic sensors and sending the digitized outputs to a computing device for storage and processing; and using the digitized outputs of the plurality of passive acoustic sensors, estimating a probability of the velocity of the plunger using a hidden Markov model (HMM) to represent a probability of a position and the probability of the velocity of the plunger, the HMM comprising a state space model and an observational model.

A system and method controls a plurality of artificial lift units at a plurality of wellsites. Processing equipment installs at a plurality of the wellsites. Operating parameters of each of the artificial lift units are obtained with sensing equipment at the wellsites and are communicated in real-time from the wellsites to the installed processing equipment at the plurality of the wellsites. A modelling function of the processing equipment analyzes a trend of the operating parameters of the artificial lift units, and automated machine learning of the processing equipment predicts a condition of at least one of the artificial lift units based on the analyzed trend. The processing equipment determines at least one automated control for the determined condition of the at least one artificial lift unit and counters the determined condition by implementing the at least one automated control at the at least one artificial lift unit.

A system includes a vertical charge pump assembly. The vertical charge pump assembly includes a top portion adjacent to a first end of the vertical charge pump assembly and a bottom portion adjacent to a second end of the vertical charge pump assembly. A pump motor is disposed in the top portion and an impeller is disposed in the bottom portion within a bowl casing. A shaft is disposed within a central passageway and connects the pump motor with the impeller. The vertical charge pump assembly also includes an inlet at the second end below the bowl casing. The pump inlet and the bowl casing are configured to be immersed in a fluid, and the vertical charge pump assembly is configured to pump the fluid into the inlet and upwards through the central passageway by rotation of the impeller. A vibration sensor is disposed on an external surface of the bottom portion, on or proximate to the bowl casing and the pump inlet. The vibration sensor includes a substrate comprising a polymer and a resonant layer disposed on a surface of the substrate. The resonant layer comprises an electrically conductive nanomaterial and is configured to produce a resonant response in response to receiving a radio frequency signal.

A system includes a vertical charge pump assembly. The vertical charge pump assembly includes a top portion adjacent to a first end of the vertical charge pump assembly and a bottom portion adjacent to a second end of the vertical charge pump assembly. A pump motor is disposed in the top portion and an impeller is disposed in the bottom portion within a bowl casing. A shaft is disposed within a central passageway and connects the pump motor with the impeller. The vertical charge pump assembly also includes an inlet at the second end below the bowl casing. The pump inlet and the bowl casing are configured to be immersed in a fluid, and the vertical charge pump assembly is configured to pump the fluid into the inlet and upwards through the central passageway by rotation of the impeller. A vibration sensor is disposed on an external surface of the bottom portion, on or proximate to the bowl casing and the pump inlet. The vibration sensor includes a substrate comprising a polymer and a resonant layer disposed on a surface of the substrate. The resonant layer comprises an electrically conductive nanomaterial and is configured to produce a resonant response in response to receiving a radio frequency signal.

A device for controlling injection of treatment fluids in plunger lift systems includes a lubricator cap coupleable to a lubricator. The lubricator cap defines a fluid inlet coupleable to a fluid injection system. A sensor coupled to the lubricator cap detects arrival of a plunger within the lubricator a controller communicatively coupled to the sensor transmits an indicator corresponding to arrival of plunger to a fluid injection control system. In response, the fluid injection system injects fluid into the fluid inlet to recharge the plunger. In certain implementations, the sensor detects arrival of the plunger at the lubricator by detecting movement of a lubricator spring or a component of a lubricator spring, such as a spring follower.