A method for evaluating a gas well productivity with eliminating an influence of liquid loading includes steps of: collecting basic data of a liquid loading gas well; according to a relative density of natural gas, a formation depth, and a casing pressure during a productivity test, determining a pressure generated by a static gas column in an annular space between a casing and a tubing from a well head to a bottomhole of the gas well, and obtaining a bottomhole pressure without liquid loading; according to a pseudo-pressure of a formation pore pressure, pseudo-pressures of the bottomhole pressure respectively under the conditions of liquid loading and no liquid loading, and a production rate under the condition of liquid loading, determining a production rate without liquid loading, and determining an absolute open flow rate with eliminating the influence of liquid loading.

Fluid production can be simulated using a reservoir model and a tubing model. For example, pressure data and saturation data can be received from a reservoir model simulating a hydrocarbon reservoir in a subterranean formation. A tubing model can be generated by performing nodal analysis using the pressure data and the saturation data. A well-test result can be received that indicates an amount of fluid produced by the wellbore at a particular time. A tuned tubing model can be generated by adjusting the tubing model such that a tubing-model estimate of the amount of fluid produced by the wellbore at the particular time matches the well-test result. An estimated amount of fluid produced by the wellbore can then be determined using the tuned tubing model. The estimated amount of fluid produced by the wellbore may be used for production allocation or controlling a well tool.

Fluid production can be simulated using a reservoir model and a tubing model. For example, pressure data and saturation data can be received from a reservoir model simulating a hydrocarbon reservoir in a subterranean formation. A tubing model can be generated by performing nodal analysis using the pressure data and the saturation data. A well-test result can be received that indicates an amount of fluid produced by the wellbore at a particular time. A tuned tubing model can be generated by adjusting the tubing model such that a tubing-model estimate of the amount of fluid produced by the wellbore at the particular time matches the well-test result. An estimated amount of fluid produced by the wellbore can then be determined using the tuned tubing model. The estimated amount of fluid produced by the wellbore may be used for production allocation or controlling a well tool.

Embodiments disclosed herein generally relate to a method and system for oil and gas predictive analytics. A computer system receives a set of production information for a well located in a region. The computing system generates a set of general reference groups comprising one or more reference wells for the region. The computing system determines whether the set of production information for the well includes the threshold amount of production information. The computing system selects a subset of reference wells from the general reference groups based on one or more traits of the well. The computing system generates a reference curve based on the set of production information associated with each reference well in the subset of reference wells. The computing system fits a decline curve to the reference curve, to determine an estimated ultimate recovery of the well.

A system includes a processing device and a non-transitory computer-readable medium having instructions stored thereon that are executable by the processing device to cause the system to perform operations. The operations include generating and running a reservoir simulation model. The reservoir and simulation model includes representative natural fracture or secondary porosity attributes for an area of interest for one or more wells. The operations also include generating a synthetic G-function response using results of the reservoir simulation model. Additionally, the operations include calibrating the synthetic G-function response from the reservoir simulation model to a field G-function response generated using results of a field diagnostic fracture injection test by changing natural fracture characteristics of the reservoir simulation model. Further, the operations include formulating a drilling plan, a completion plan, or both for a wellbore in the area of interest using the synthetic G-function response.

A system for determining completion parameters for a wellbore includes a sensor and a computing device. The sensor can be positioned at a surface of a wellbore to detect data prior to finishing a completion stage for the wellbore. The computing device can receive the data, perform a history match for simulation and production using the sensor data and historical data, generate inferred data for completion parameters using the historical data identified during the history match, predict stimulated area and production by inputting the inferred data into a neural network model, determine completion parameters for the wellbore using Bayesian optimization on the stimulated area and production from the neural network model, profit maximization, and output the completion parameters for determining completion decisions for the wellbore.

Methods and systems for predicting well site production are disclosed, including a computer system comprising one or more processor and a non-transitory computer memory storing processor readable instructions that when executed by the one or more processor cause the one or more processor to receive image data of a geographic region around and including a well site; receive well site location data of a location of the well site; analyze well site data to determine well pad location data of a location of a well pad including an area of observation extending beyond and around a well site; determine pixel data of the well pad within the image data for a particular time from the well pad location data; and analyze the pixel data of the well pad for a particular time to determine a volume of flared gas based on the pixel data.

Methods and systems for predicting well site production are disclosed, including a computer system comprising one or more processor and a non-transitory computer memory storing processor readable instructions that when executed by the one or more processor cause the one or more processor to receive image data of a geographic region around and including a well site; receive well site location data of a location of the well site; analyze well site data to determine well pad location data of a location of a well pad including an area of observation extending beyond and around a well site; determine pixel data of the well pad within the image data for a particular time from the well pad location data; and analyze the pixel data of the well pad for a particular time to determine a volume of flared gas based on the pixel data.

A method may include obtaining a real-time petrophysical data derived from a plurality of well logs during drilling and utilizing the real-time petrophysical data to quantify a formation damage profile using a resistivity tornado chart and a wellbore modeling. The method further includes utilizing the resistivity tornado chart to determine a depth of invasion inside a formation at each depth in a wellbore by using ratios between different resistivity logs obtained while drilling and creating a synthetic wellbore model by using a fluid flow equation for the wellbore modeling and calculating a time-specific invasion profile to determine a condition at a flowback time. The method further includes performing a computational fluid dynamics investigation in order to identify invaded fluid flow characteristics from the formation to the wellbore and calculating a duration needed to flowback an obtained invaded volume for removal of the formation damage based on a fluid flow behavior.

Systems and methods for operating wells of a field using a multi-objective genetic algorithm are disclosed. In one embodiment, a method of operating a plurality of wells within a field includes determining an oil rate for each well of the plurality of wells by a multi-objective genetic algorithm. The multi-objective genetic algorithm is defined by a multi-objective fitness function including a first objective function that meets a target oil rate for the field and a second objective function that maximizes bottom-hole reservoir pressure, maximizes a distance of the wells to a crest line of the field, and minimizes a water cut of the field. The multi-objective genetic algorithm outputs the oil rate for each well that satisfies the multi-objective fitness function. The method further includes operating the plurality of wells at the oil rate for each well.