Abstract: For each well of the plurality of wells, a plurality of model outputs are generated for the well. A range of productivity is determined for the well by plotting the plurality of model outputs in the form of a probability distribution function, projecting an actual production indicator result for the well onto the probability distribution function, comparing the actual production indicator result and the probability distribution function to identify a well production potential for the well, and determining a quality of the well production potential based on a plurality of categorization rules including a lowest quality category and a highest quality category. All of the wells in the plurality of wells are analyzed to identify wells in the highest quality category based on the determined quality of well production potentials for the plurality of wells for hydraulic refracturing.

Abstract: Various examples are provided for gas leakage detection from geologic storage sites. In one example, a system for detection of gas leakage from a geologic storage site includes permanent down-hole gauges (PDGs) disposed at different depths within a well and an evaluation system that can determine a three-dimensional (3D) location of the gas leakage from the geologic storage site and a leakage indicator. The 3D location and leakage indicator can be determined using pressure data provided by the PDGs. The geologic storage site can store, e.g., CO2, natural gas or other type of gas. In another example, a method for detecting gas leakage from a geologic storage site includes receiving pressure data provided by PDGs disposed within one or more wells associated with the geologic storage site, determining key performance indicators using the pressure data and determining a 3D location and a leakage indicator using the key performance indicators.

Abstract: A computer system, comprising: one or more processors; and a memory coupled to the one or more processors, wherein the computer system is programmed with a set of computer-readable instructions configured to implement a method for determining a drill site in a shale, tight gas sands, coal bed methane or other unconventional well field for an oil or gas well and one or more parameter settings therefore, the computer-readable instructions comprising: one or more data mining instructions for data mining of a first part of a set of measurement or objective data parameters from existing oil or gas wells in the well field that, when executed, produce a ranking of each parameter and its respective effect on oil or gas well production from the well field; one or more training instructions that, when executed, produce a model comprising an artificial neural network (ANN) trained with the first part of the set of measurement or objective data parameters and/or the ranking that is useful for producing a group of optimiz

Abstract: Generating a map of a Natural Fracture Network (“NFN”) of a shale reservoir is provided. A set of rules are defined to output an NFN density value, the rules being based on parameters including a well volume/completion parameter, a well stimulation parameter, and a well production parameter, each parameter divided into at least two fuzzy (or hard) clusters. The rules include combinations made up of the plurality of parameters and the fuzzy (or hard) cluster of each parameter, each combination being assigned an NFN density value. Historical data related to a plurality of wells drilled in the shale reservoir is analyzed, where the data includes volume/completion data, stimulation data, and production data. Corresponding values are assigned to the historical data based on the set of rules to determine the NFN density value. The NFN density values are mapped on a graph to identify the NFN of the shale reservoir.

Abstract: For each well of the plurality of wells, a plurality of model outputs are generated for the well. A range of productivity is determined for the well by plotting the plurality of model outputs in the form of a probability distribution function, projecting an actual production indicator result for the well onto the probability distribution function, comparing the actual production indicator result and the probability distribution function to identify a well production potential for the well, and determining a quality of the well production potential based on a plurality of categorization rules including a lowest quality category and a highest quality category. All of the wells in the plurality of wells are analyzed to identify wells in the highest quality category based on the determined quality of well production potentials for the plurality of wells for hydraulic refracturing.

Abstract: Various examples are provided for gas leakage detection from geologic storage sites. In one example, a system for detection of gas leakage from a geologic storage site includes permanent down-hole gauges (PDGs) disposed at different depths within a well and an evaluation system that can determine a three-dimensional (3D) location of the gas leakage from the geologic storage site and a leakage indicator. The 3D location and leakage indicator can be determined using pressure data provided by the PDGs. The geologic storage site can store, e.g., CO2, natural gas or other type of gas. In another example, a method for detecting gas leakage from a geologic storage site includes receiving pressure data provided by PDGs disposed within one or more wells associated with the geologic storage site, determining key performance indicators using the pressure data and determining a 3D location and a leakage indicator using the key performance indicators.

Abstract: A computer system, comprising: one or more processors; and a memory coupled to the one or more processors, wherein the computer system is programmed with a set of computer-readable instructions configured to implement a method for determining a drill site in a shale, tight gas sands, coal bed methane or other unconventional well field for an oil or gas well and one or more parameter settings therefore, the computer-readable instructions comprising: one or more data mining instructions for data mining of a first part of a set of measurement or objective data parameters from existing oil or gas wells in the well field that, when executed, produce a ranking of each parameter and its respective effect on oil or gas well production from the well field; one or more training instructions that, when executed, produce a model comprising an artificial neural network (ANN) trained with the first part of the set of measurement or objective data parameters and/or the ranking that is useful for producing a group of optimiz

Abstract: A system for providing real-time assistance to a drilling operation drilling a bore in the earth, comprising: a computer; a first non-transitory computer-readable medium storing a first program that, when executed by the computer, causes the computer to: receive real-time raw data from sensors monitoring the drilling operation and/or bore; cleanse the real-time raw data including removing any real-time raw data sensed while a drill string of the drilling operation was in a mode of: bit-off-bottom, tripping-in, tripping-out, reaming forward, reaming backward and/or cyclic reaming to produce cleansed data; apply at least a portion of the cleansed data to a neural network that has been trained with information concerning the drilling operation comprising geological information for a part of the earth in which the bore is being drilled; receive from the neural network a prediction in real-time as to the probability of the drilling operation experiencing a condition in the future; and display the prediction.