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 modified gathering manifold is disclosed, including a sampling header coupled to each of multiple production lines of wells, and a plurality of diverters, each coupled to one of the production lines, upstream of a relief header coupled to each of the plurality of production lines and a production header associated with the manifold and coupled to each of the plurality of production lines. The sampling header receives a production fluid diverted by a diverter in the open position. The manifold also includes a three-phase separator coupled to the sampling header downstream of the plurality of diverters that separates the production fluid into crude oil, water, and gas, and detects a volume flow rate for each. A return header passes the crude oil, the water, and the gas from the three-phase separator into the production header where they are combined into a hydrocarbon fluid flow.

A gas extraction assembly includes an agitator gas trap and a nipple apparatus. The nipple apparatus is coupled to a flowline containing a mud mixture. The agitator gas trap is also in communication with the mud mixture. An inline detector is in the nipple apparatus and configured to help separate the mud mixture from a gas. A tube passes between the nipple apparatus and the agitator gas trap to transport the gas from the inline detector to a gas detection and logging unit for sample recording.

A system can receive seismic data that can correlate to a subterranean formation. The system can derive a set of seismic attributes from the seismic data. The seismic attributes can include discontinuity-along-dip. The system can determine parameterized results by analyzing the seismic data and the seismic attributes using a deep learning neural network. The deep learning neural network can include a dilation module. The system can determine one or more fault probabilities of the subterranean formation using the parameterized results. The system can output the fault probabilities for use in a hydrocarbon exploration operation.

A system can receive seismic data that can correlate to a subterranean formation. The system can derive a set of seismic attributes from the seismic data. The seismic attributes can include discontinuity-along-dip. The system can determine parameterized results by analyzing the seismic data and the seismic attributes using a deep learning neural network. The deep learning neural network can include a dilation module. The system can determine one or more fault probabilities of the subterranean formation using the parameterized results. The system can output the fault probabilities for use in a hydrocarbon exploration operation.

A method for measuring an oil saturation value of a subterrain formation uses a tool having multiple dual-function detectors that detect neutrons and gamma rays. The method includes emitting neutrons into the formation, detecting neutrons and gamma ray signals form the formation using the detectors, determining formation parameters including the formation type and formation porosity, and further determining parameters such as C/O ratios at each of the detectors, a total neutron count rate at each of detectors, a fast neutron count rate at each of detectors, and a thermal neutron count rate at each of the three or more detector, and calculating the oil saturation value using the determined parameters.

A method for measuring an oil saturation value of a subterrain formation uses a tool having multiple dual-function detectors that detect neutrons and gamma rays. The method includes emitting neutrons into the formation, detecting neutrons and gamma ray signals form the formation using the detectors, determining formation parameters including the formation type and formation porosity, and further determining parameters such as C/O ratios at each of the detectors, a total neutron count rate at each of detectors, a fast neutron count rate at each of detectors, and a thermal neutron count rate at each of the three or more detector, and calculating the oil saturation value using the determined parameters.

A method for producing a well includes receiving production information associated with wells within a field; deriving a field specific model from the production information; receiving production information associated with the well; projecting production changes associated with installing artificial lift at the well at a projected date, the projecting using a production analysis engine applied to the field specific model, the projecting including determining a set of artificial lift parameters; and installing the artificial lift at the well in accordance with the artificial lift parameters.

A method for producing a well includes receiving production information associated with wells within a field; deriving a field specific model from the production information; receiving production information associated with the well; projecting production changes associated with installing artificial lift at the well at a projected date, the projecting using a production analysis engine applied to the field specific model, the projecting including determining a set of artificial lift parameters; and installing the artificial lift at the well in accordance with the artificial lift parameters.

A controller for a well system automatically profiles the system, detects a pre-charge of an associated pressurized storage tank, and automatically configures pressure-based control of a pump based on the detected pre-charge. The controller determines the pre-charge of the pressurized storage tank while the tank is connected to the system. While monitoring a system pressure, the controller activates the pump to initiate a filling operation of the pressurized storage tank. The controller analyzes a change in system pressure during the filling operation to determine the pre-charge of the pressurized storage tank. With the pre-charge determined, the controller automatically configures pressure settings for pressure-based control of the pump.