Intellicess Inc.

University of Texas System

Pradeepkumar Ashok

Eric Van Oort

John James D'Angelo

Adrian Marius Ambrus

Dongmei Chen

Parham Pournazari

Roman Shor

Adrian Ambrus

Ian Rostagno

Vivek Singhal

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.

Cleansing of drilling sensor readings

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.

Geothermal well designs and control thereof for extraction of subsurface geothermal power

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.

Systems and methods for monitoring slide drilling operations

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.

Optimization of drilling operations using drilling cones

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.

Method for long-horizon event-based detection of poor hole cleaning conditions for use in streaming analytics at the drilling rig

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.

X-ray mass flow rate sensors for high pressure processes

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.

Utilizing look-up tables representing all models in an automation control architecture to independently handle uncertainties in sensed data in oil and gas well construction

Growing community of inventors

Austin, TX, United States of America

Pradeepkumar Ashok