Abstract: Drilling rig operations may be monitored using a variety of sensors and/or other data sources. Erroneous, faulty, and/or missing data may be cleansed prior to using the data for modeling and/or monitoring drilling operations. Erroneous, faulty, and/or missing data may be identified by comparing received data to anticipated values based on historical operations, other physically related sensor readings, and known operating ranges. Cleansing may comprise replacing erroneous, faulty, and/or missing data with a modeled value or omitting a reading entirely.

Abstract: A geothermal well includes a borehole formed in a geologic formation. The borehole includes a first portion extending from a first end to a second end and a second portion intersecting the first portion at a first depth. The second portion is exposed to the formation for heating a fluid flowing in the second portion. The well includes an outer string disposed in the first portion where a lower end of the outer string is disposed above the first depth. The well includes an inner string disposed though the outer string and an annulus formed between the inner string and the outer string. The well includes a choke fluidly coupled to at least one of the annulus or the inner string. The choke is configured to regulate return flow of the heated fluid to maintain surface backpressure above a boiling pressure of the heated fluid at surface.

Abstract: Systems and methods for monitoring slide drilling operations and providing advisory information includes a Bayesian network having a slide drilling dysfunction output node and six input nodes including: a downhole mechanical specific energy (MSE) trend node; a downhole bit aggressiveness trend node; a differential pressure trend node; a minimum buckling load node; a downhole weight on bit (DWOB) vs surface weight on bit (SWOB) ratio node, and a toolface efficiency index node. When one or more dysfunctions are detected based on the information provided to one or more nodes, the system sends out one or more alerts and provides one or more corrective actions to return to efficient drilling.

Abstract: A geothermal well includes a borehole formed in a geologic formation. The borehole includes a first portion extending from a first end to a second end and a second portion intersecting the first portion at a first depth. The second portion is exposed to the formation for heating a fluid flowing in the second portion. The well includes an outer string disposed in the first portion where a lower end of the outer string is disposed above the first depth. The well includes an inner string disposed though the outer string and an annulus formed between the inner string and the outer string. The well includes a choke fluidly coupled to at least one of the annulus or the inner string. The choke is configured to regulate return flow of the heated fluid to maintain surface backpressure above a boiling pressure of the heated fluid at surface.

Abstract: Drilling operations may be monitored to detect and quantify potential drilling dysfunctions. Using a Bayesian network, potential improvements to drilling operation may be made depending upon the type of dysfunction detected. Suggestions for improved drilling performance may comprise increasing, decreasing, or maintaining one or both of RPM and weight on bit. Suggestions may be presented to an operator as a cone having an apex at the current RPM and weight on bit drilling parameters, with suggestions for modifications to one or both of the RPM and weight on bit corresponding to a cone extending from that apex.

Abstract: A method for estimating the efficiency of hole cleaning of a well-bore during drilling operations includes (a) selecting at least one feature of the drilling operation for monitoring; (b) receiving, from a sensor, data regarding the at least one feature; (c) comparing the data to a predetermined condition for the at least one feature; (d) defining an event when the data meets the predetermined condition; (e) assigning a weight to the event based on a duration of the event; and (f) estimating an efficiency of the hole cleaning based on the event and the weight of the event. The at least one feature being selected from the list consisting of: hole angle, circulation, tight spot, static hole, and bit hydraulics.

Abstract: An x-ray mass flow rate sensor uses a low density polymer pipe, an x-ray source, and an x-ray detector. The polymer pipe has a low density (less than 2.8 SG) and a high pressure rating (greater than 5 ksi). By using a low density polymer pipe, the sensor is able to use an x-ray source that does not require a linear accelerator and is less than or equal to 450 kV. The x-ray source and the x-ray detector are mounted on opposite sides of the polymer pipe to form a detection area that passes through the polymer pipe. A real-time calibration of the sensor is performed by detecting gray level values in a calibration region of the detection area for two reference materials placed in the detection area. The sensor may additionally include a mechanical flow rate sensor with a plurality of pistons with springs of varying spring constants.

Abstract: A method for estimating the efficiency of hole cleaning of a well-bore during drilling operations includes (a) selecting at least one feature of the drilling operation for monitoring; (b) receiving, from a sensor, data regarding the at least one feature; (c) comparing the data to a predetermined condition for the at least one feature; (d) defining an event when the data meets the predetermined condition; (e) assigning a weight to the event based on a duration of the event; and (f) estimating an efficiency of the hole cleaning based on the event and the weight of the event. The at least one feature being selected from the list consisting of: hole angle, circulation, tight spot, static hole, and bit hydraulics.

Abstract: Drilling rig operations may be monitored using a variety of sensors and/or other data sources. Erroneous, faulty, and/or missing data may be cleansed prior to using the data for modeling and/or monitoring drilling operations. Erroneous, faulty, and/or missing data may be identified by comparing received data to anticipated values based on historical operations, other physically related sensor readings, and known operating ranges. Cleansing may comprise replacing erroneous, faulty, and/or missing data with a modeled value or omitting a reading entirely.

Abstract: Drilling operations may be monitored to detect and quantify potential drilling dysfunctions. Using a Bayesian network, potential improvements to drilling operation may be made depending upon the type of dysfunction detected. Suggestions for improved drilling performance may comprise increasing, decreasing, or maintaining one or both of RPM and weight on bit. Suggestions may be presented to an operator as a cone having an apex at the current RPM and weight on bit drilling parameters, with suggestions for modifications to one or both of the RPM and weight on bit corresponding to a cone extending from that apex.

Abstract: An x-ray mass flow rate sensor uses a low density polymer pipe, an x-ray source, and an x-ray detector. The polymer pipe has a low density (less than 2.8 SG) and a high pressure rating (greater than 5 ksi). By using a low density polymer pipe, the sensor is able to use an x-ray source that does not require a linear accelerator and is less than or equal to 450 kV. The x-ray source and the x-ray detector are mounted on opposite sides of the polymer pipe to form a detection area that passes through the polymer pipe. A real-time calibration of the sensor is performed by detecting gray level values in a calibration region of the detection area for two reference materials placed in the detection area. The sensor may additionally include a mechanical flow rate sensor with a plurality of pistons with springs of varying spring constants.

Abstract: A method, system and computer program product for utilizing look-up tables representing all models in an automation control architecture to independently handle uncertainties in sensed data. Data is stored in a form of conditional probability tables (CPTs) or conditional probability distributions (CPDs), where the data comes from an operator, a service provider, a drilling contractor and an equipment manufacturer. Models of the drilling process domains, such as wellbore hydraulics, drill bit/rock interactions, torque and drag modeling, vibration modeling and drilling machinery operation, are received. Data is extracted from these models into the CPTs or CPDs. The CPTs or CPDs are converted to look-up tables. Data in the look-up tables are then visually displayed in graphical form. As a result, real-time troubleshooting of drilling operations occurs in an efficient manner.

Abstract: A method, system and computer program product for utilizing look-up tables representing all models in an automation control architecture to independently handle uncertainties in sensed data. Data is stored in a form of conditional probability tables (CPTs) or conditional probability distributions (CPDs), where the data comes from an operator, a service provider, a drilling contractor and an equipment manufacturer. Models of the drilling process domains, such as wellbore hydraulics, drill bit/rock interactions, torque and drag modeling, vibration modeling and drilling machinery operation, are received. Data is extracted from these models into the CPTs or CPDs. The CPTs or CPDs are converted to look-up tables. Data in the look-up tables are then visually displayed in graphical form. As a result, real-time troubleshooting of drilling operations occurs in an efficient manner.

Abstract: The disclosure relates to an adaptive system for diagnosing vibrations during drilling including a drilling assembly at least partially located in a wellbore, a sensor located in the wellbore, and a data processing unit. The drilling assembly may drill the wellbore. The sensor may detect high frequency data reflecting vibrations in the drilling assembly. The data processing unit may execute a classification model based on machine learning techniques which uses features extracted from the high frequency data to diagnose the type or intensity of a vibration or both in the drilling assembly. The disclosure further relates to an adaptive method of diagnosing vibrations during drilling by collecting high frequency data reflecting vibrations in a drilling assembly, extracting at least one feature from the high frequency data, and diagnosing the type of vibration using the at least one extracted feature and a classification model based on machine learning techniques.

Abstract: A method, computer program product and system for presenting attributes of interest. A decision surface is created using process maps. The process maps are representative of system operational data from a plurality of sensors. A current operating point is identified including a location and a movement characteristic of the operating point. The location and the movement characteristic of the operating point are used to identify an attribute with a final probabilistic value assigned to the attribute. If the final probabilistic value for the attribute crosses a previously-defined threshold, an alarm is generated. The decision surfaces, the process maps, the current operating point, the predicted movement of the operating point, the attributes, and the alarms are visually represented in a data handling system to assist the operator in the real time monitoring and operation of the physical system.

Abstract: A method, system and computer program product for distinguishing between a sensor fault and a process fault in a physical system and use the results obtained to update the model. A Bayesian network is designed to probabilistically relate sensor data in the physical system which includes multiple sensors. The sensor data from the sensors in the physical system is collected. A conditional probability table is derived based on the collected sensor data and the design of the Bayesian network. Upon identifying anomalous behavior in the physical system, it is determined whether a sensor fault or a process fault caused the anomalous behavior using belief values for the sensors and processes in the physical system, where the belief values indicate a level of trust regarding the status of its associated sensors and processes not being faulty.