Abstract: A computer-assisted method for optimizing a drilling tool assembly, the method comprising defining a desired drilling plan; determining current drilling conditions; determining current drilling tool parameters of at least two drilling tool assembly components; analyzing the current drilling conditions and the current drilling tool parameters to define a base drilling condition; comparing the base drilling condition to the desired drilling plan; determining a drilling tool parameter to adjust to achieve the desired drilling plan; and adjusting at least one drilling tool parameter of at least one of the two drilling tool assembly components based on the comparing the base drilling condition to the desired drilling plan.

Abstract: A method for providing assistance to a drilling site including receiving, by a remote system, an assistance request from a quick-link communication device, wherein the quick-link communication device is located at the drilling location. The method also including obtaining sensor data from the rig based on the assistance request, analyzing, by the remote system, the sensor data to identify a condition of the rig, and providing assistance to the drilling site for the condition of the rig.

Abstract: A method of optimizing a drilling tool assembly including inputting well data into an optimization system, the optimization system having an experience data set and an artificial neural network. The method further including comparing the well data to the experience data set and developing an initial drilling tool assembly based on the comparing the well data to the experience data, wherein the drilling tool assembly is developed using the artificial neural network. Additionally, the method including simulating the initial drilling tool assembly in the optimization system and creating result data in the optimization system based on the simulating.

Abstract: A method of optimizing a drilling operating parameter or a drilling system parameter for a drilling assembly employing at least first and second distinct cutting structures includes entering at least one design parameter for each of the cutting structures into a trained artificial neural network. At least one of the design parameters of the first cutting structure may be optionally combined with at least one of the design parameters of the second cutting structure. The combined design parameter may also be entered into the artificial neural network.

Abstract: A method of optimizing a drilling tool assembly including inputting well data into an optimization system, the optimization system having an experience data set and an artificial neural network. The method further including comparing the well data to the experience data set and developing an initial drilling tool assembly based on the comparing the well data to the experience data, wherein the drilling tool assembly is developed using the artificial neural network. Additionally, the method including simulating the initial drilling tool assembly in the optimization system and creating result data in the optimization system based on the simulating.

Abstract: A computer-assisted method for optimizing a drilling tool assembly, the method comprising defining a desired drilling plan; determining current drilling conditions; determining current drilling tool parameters of at least two drilling tool assembly components; analyzing the current drilling conditions and the current drilling tool parameters to define a base drilling condition; comparing the base drilling condition to the desired drilling plan; determining a drilling tool parameter to adjust to achieve the desired drilling plan; and adjusting at least one drilling tool parameter of at least one of the two drilling tool assembly components based on the comparing the base drilling condition to the desired drilling plan.

Abstract: A method of optimizing a drilling tool assembly including inputting well data into an optimization system, the optimization system having an experience data set and an artificial neural network. The method further including comparing the well data to the experience data set and developing an initial drilling tool assembly based on the comparing the well data to the experience data, wherein the drilling tool assembly is developed using the artificial neural network. Additionally, the method including simulating the initial drilling tool assembly in the optimization system and creating result data in the optimization system based on the simulating.

Abstract: A method of optimizing a drilling operating parameter or a drilling system parameter for a drilling assembly employing at least first and second distinct cutting structures includes entering at least one design parameter for each of the cutting structures into a trained artificial neural network. At least one of the design parameters of the first cutting structure may be optionally combined with at least one of the design parameters of the second cutting structure. The combined design parameter may also be entered into the artificial neural network.

Abstract: A method for aggregating data that includes obtaining a log object including a log element, wherein the log element includes oilfield data obtained from a provider, obtaining an aggregation policy for the log element, and aggregating the log element into an aggregated object based on the aggregation policy is disclosed.

Abstract: A method of optimizing a drilling tool assembly including inputting well data into an optimization system, the optimization system having an experience data set and an artificial neural network. The method further including comparing the well data to the experience data set and developing an initial drilling tool assembly based on the comparing the well data to the experience data, wherein the drilling tool assembly is developed using the artificial neural network. Additionally, the method including simulating the initial drilling tool assembly in the optimization system and creating result data in the optimization system based on the simulating.

Abstract: A method of optimizing drilling including identifying design parameters for a drilling tool assembly, preserving the design parameters as experience data, and training at least one artificial neural network using the experience data. The method also includes collecting real-time data from the drilling operation, analyzing the real-time data with a real-time drilling optimization system, and determining optimal drilling parameters based on the analyzing the real-time date with the real-time drilling optimization system. Also, a method for optimizing drilling in real-time including collecting real-time data from a drilling operation and comparing the real-time data against predicted data in a real-time optimization system, wherein the real-time optimization includes at least one artificial neural network. The method further includes determining optimal drilling parameters based on the comparing the real-time data with the predicted data in the real-time drilling optimization system.

Abstract: A method for optimizing drilling parameters includes obtaining previously acquired data, querying a remote data store for current well data, determining optimized drilling parameters, and returning optimized parameters for a next segment to the remote data store. Determining optimized drilling parameters may include correlating the current well data to the previously acquired data, predicting drilling conditions for the next segment, and optimizing drilling parameters for the next segment.

Abstract: A drill bit, comprising a bit body, a sensor disposed in the bit body, a single journal removably mounted to the bit body, and a roller cone rotatably mounted to the single journal.

Abstract: A cutter element for a drill bit has a crest that defines a crest line. The crest can be non-rectilinear. The crest line of each cutter element defines an angle with respect to the axis of the rolling cone on which the cutter element is mounted. The cutter elements define different angles with respect to the cone axis and with respect to each other.

Abstract: A roller cone drill bit is disclosed which includes at least one roller cone rotatably mounted to a bit body. The bit body includes therein a lubricant reservoir adapted to supply lubricant to bearings on which the roller cone rotates about the bit body. The bit includes a sensor adapted to detect drilling fluid contamination of the lubricant. The bit includes a processor/transmitter operatively coupled to the sensor and adapted to communicate signals corresponding to detected contamination.

Abstract: A drill bit, comprising a bit body, a sensor disposed in the bit body, a single journal removably mounted to the bit body, and a roller cone rotatably mounted to the single journal.

Abstract: A cutter element for a drill bit has a non-rectilinear crest. The cutter element has a base portion and an extending portion and the extending portion has either a zero draft or a negative draft with respect to the base portion. The non-positive draft allows more of the borehole bottom to be scraped using fewer cutter elements. The cutter elements having non-positive draft can be either tungsten carbide inserts or steel teeth.

Abstract: A cutter element for a drill bit has a non-rectilinear crest. The cutter element has a base portion and an extending portion and the extending portion has either a zero draft or a negative draft with respect to the base portion. The non-positive draft allows more of the borehole bottom to be scraped using fewer cutter elements. The cutter elements having non-positive draft can be either tungsten carbide inserts or steel teeth.

Abstract: A roller cone drill bit is disclosed which includes at least one roller cone rotatably mounted to a bit body. The bit body includes therein a lubricant reservoir adapted to supply lubricant to bearings on which the roller cone rotates about the bit body. The bit includes a sensor adapted to detect drilling fluid contamination of the lubricant. The bit includes a processor/transmitter operatively coupled to the sensor and adapted to communicate signals corresponding to detected contamination.

Abstract: A method for selecting a design parameter for a drill bit is disclosed. The method includes entering a value of at least one property of an earth formation to be drilled into a trained neural network. The neural network is trained by selecting data from drilled wellbores. The data comprise values of the formation property for formations through which the drilled wellbores have penetrated. Corresponding to the values of formation property are values of at least one drilling operating parameter, the drill bit design parameter, and values of a rate of penetration and a rate of wear of a drill bit used on each of the formations. Data from the wellbores are entered into the neural network to train it, and the design parameter is then selected based on output of the trained neural network.