Halliburton Energy Services, Inc.

Sensortran, Inc.

Petroleum Geo-Services, Inc.

Mikko K Jaaskelainen

Brian Vandellyn Park

Jason Edward Therrien

Ian Bradford Mitchell

Seldon David Benjamin

John L Maida

Julian Drew

Ronald Glen Dusterhoft

Joshua Lane Camp

Maximo Gustavo Michaelis

John Paul Bir Singh

Eric James Davis

Krishna Babu Yerubandi

Norman Raymond Warpinski

Kenneth Glenn Dixson

John Laureto Maida, Jr

Michel Joseph LeBlanc

Ira Jeffrey Bush

Henry Clifford Bland

Glenn Robert McColpin

Barry Fish

Benjamin Simon Schaeffer

Michel LeBLANC

Stanley Vernon Stephenson

David Andrew Barfoot

Priyesh Ranjan

Kwang Il Suh

Yijie Shen

Glenn Andrew Wilson

Etienne M Samson

Harold Grayson Walters

Tirumani N Swaminathan

Tasneem Mandviwala

Kenneth James Smith

Shahab Jamali Ghare Tape

Norman R Warpinski

Nitika Kalia

Hongyan Duan

Yunmiao Wang

Cole Aaron Grandjean

Kashyap Navinchand Choksey

Michel Joseph Leblanc

Glenn Mccolpin

Philip D Nguyen

Stephen Michael Greci

Ahmed Elsayed Fouda

Timothy Holiman Hunter

Michael Wayne Sanders

Dinesh Ananda Shetty

Ken A Smith

Vladimir Nikolayevich Martysevich

Andreas Ellmauthaler

Ubong Akpan Inyang

Steven James Maas

Kent Kalar

Aaron Gene Russell

Baidurja Ray

Antonio Recio

Han-Sun Choi

Michael Edwin Pollard

Crispin Doyle

Neha Sahdev

Grayson Dane Byrd

Richard Gibson

James S Bunn

Celso Max Trujillo

Faraaz Adil

Henry Timmons Moeller, Ii

Joshua Brandt Stokes

Karn Agarwal

Ron Dusterhof

Nicholas Clive Spencer Wheeler

Michael J Mayerhofer

Angus Duthie

Steve Tichenor

Henry Killen

J Brett Bunn

Brian Vandelyn Park

Seldon Benjamin

Henry T Moeller

William Neal Gibler

John Laureto Maida

Ken James Smith

Norman Warpinski

Glenn Robert Mccolpin

Described herein are systems and techniques related to a propulsion device that moves equipment along a wellbore. While wellbore equipment may be deployed in a wellbore using gravity or with the flow of a fluid like drilling mud, in certain instances, such techniques are not well suited to this task. Systems and techniques of the present disclosure may be applied to deploy tools in a wellbore by controlling motion of a self-propelled device along the wellbore. This may include using wheels, tracks, propellers, impellers, or other devices to propel tools into a wellbore even when the wellbore has perforations that may disrupt conventional deployment techniques. Techniques of the present disclosure may include transferring power to a wellbore apparatus via one or more elements of a fiber optic cable.

Fiber deployment and monitoring on demand with fracturing spread control

Aspects of the subject technology relate to systems and methods for optimizing production flow monitoring by utilizing data driven in-situ injection. Systems and methods are provided for receiving sensor data from at least one of a distributed fiber optic sensing line positioned along a wellbore and a plurality of subsurface and surface sensors, generating flow models based on the sensor data received from the at least one of the distributed fiber optic sensing line and the plurality of subsurface and surface sensors to optimize production flow, and generating flow profiles based on the flow models and the sensor data received from the at least one of the distributed fiber optic sensing line and the plurality of subsurface and surface sensors to adjust zonal inflow device.

Data driven in-situ injection and production flow monitoring

A system can determine multi-phase measurements with respect to a wellbore. The system can include a set of acoustic devices, a measurement device, and a computing device. The set of acoustic devices can be positioned at the surface of a wellbore to generate acoustic signals proportional to flow of fluid with respect to the wellbore. The measurement device can be positioned with respect to the set of acoustic devices to sense the acoustic signals. The computing device can be communicatively coupled to the measurement device to interpret the acoustic signals for determining a type of the fluid and a ratio of one or more phases of the fluid.

Acoustic devices for measuring multi-phase wellbore fluid

A method for configuring a learning machine to predict a flow rate of at least one phase of a fluid. The method comprises determining a feature set for the learning machine, the feature set including information derived from a signal generated by a flow of the fluid interacting with a fluidic oscillator in a wellbore. The method comprises configuring the learning machine with the feature set including information derived from the signal.

Machine learning-based wellbore fluid flow rate prediction

A method for determining flow information of a well producing fluid from a subsurface formation comprising. The method comprises obtaining at least one first production measurement from the well. The method comprises obtaining, with a sensor, a first measurement generated by the fluid flowing through a device. The method comprises inputting the first measurement and the at least one first production measurement into a virtual flow meter. The method comprises determining, via the virtual flow meter, a multi-phase flow rate of the fluid based on the first measurement and the at least one first production measurement.

Virtual flow metering using acoustics for a well

A system may include a sensing fiber that can receive interrogation data via a coil of reference fiber, the coil of reference fiber configurable to be of a same type of fiber as the sensing fiber, and the sensing fiber configurable to be coupled in series with the coil of reference fiber. A known temperature and a known strain can be received from the coil of reference fiber. The known temperature, the known strain, and the interrogation data can be outputted for calibrating a measurement of the interrogation data.

Coil of reference fiber for downhole fiber sensing measurement

Aspects of the subject technology relate to systems and methods for determining positions of fluids during a cementing process in real-time. Systems and methods are provided for receiving one or more sensing parameters from a distributed acoustic sensing fiber optic line positioned in a wellbore during a cementing process, determining types of fluid proximate to the wellbore based on the one or more sensing parameters received from the distributed acoustic sensing fiber optic line, determining pressure gradients of the types of fluid based on the one or more sensing parameters received from the distributed acoustic sensing fiber optic line, and compiling flow profiles for the types of fluid proximate to the wellbore based on at least one of the determining of the types of fluid and the determining of the pressure gradients of the types of fluid.

Downhole pressure sensing for fluid identification

A method of controlling a pumping sequence of a fracturing fleet at a wellsite. A managing application executing on a computer in the control van can retrieve the pumping sequence from a local or remote storage computer. The managing application can establish an electronic communication link to receive sensor data from a plurality of fracturing units. The managing application can control the plurality of fracturing units with a stage script with multiple sequential instructions for a pumping stage of a pumping sequence while receiving one or more periodic data sets from the plurality of fracturing units wherein the data sets are indicative of the current state of the pumping stage of the pumping sequence.

Method for equipment control

A distributed acoustic system (DAS) may include at least one laser that transmits a continuous wave (CW) light, a pulser optically connected to the at least one laser to receive the CW light from the at least one laser and form a light pulse, and a splitter optically connected to the pulser to optically split the light pulse into a plurality of light pulses. The DAS may further comprise a circulator optically connected to an output of the splitter, a sensor fiber attached to the circulator, and a reference coil disposed on the sensor fiber between the circulator and a first section. A method for using the DAS may include transmitting one or more light pulses from a laser into the DAS, generating an interrogator specific noise profile for the reference wellbore, and generating an application specific noise profile for a first sensor fiber disposed in the reference wellbore.

Sensor deployment for distributed acoustic sensing with noise cancellation using a reference coil

A system for making multi-phase measurements of a fluid includes a flow meter device and a computing device. The flow meter device can include one or more acoustic devices that can generate acoustic signals in a wellbore. The computing device can receive acoustic signals from the flow meter device and determine an arrangement of the one or more acoustic devices with respect to the wellbore. The computing device can interpret the acoustic signals using the determined arrangement of the one or more acoustic devices to make a multi-phase measurement of fluid with respect to the wellbore.

Determining orientation of a subsurface flow meter device

A coupling agent for deployment around an optical fiber in a subsurface wellbore, the coupling agent including a curable or non-curable compound that can form into a reversible polymerized gel state located around at least a portion of the optical fiber and there is a gel-optical fiber interface strain gradient that is greater than a wellbore fluid-optical fiber strain gradient. A method of deploying an optical fiber in a fluid path of a subsurface wellbore and introducing a coupling agent in the fluid path.

Coupling agent to enhanced coupling of optical fibers deployed in subsurface wells

A system for evaluating the flow of pressurized fluid flowing through perforation clusters in a wellbore casing of a hydraulic fracturing wellbore. The system can temporarily increase an intensity of acoustic emissions produced by the pressurized fluid flowing through the perforation clusters and can employ an optical fiber-based acoustic sensing system disposed on or in a monitoring well residing in the formation but remotely from the hydraulic fracturing well to measure the acoustic emissions while the intensity of the acoustic emissions is temporarily increased. The measured acoustic emissions can be converted into a total flow rate of the pressurized fluid flowing through the perforation clusters, which can in turn be used to calculate a cluster level uniformity index for the hydraulic fracturing well.

Determining cluster level uniformity index in hydraulic fracturing wells

Introduced herein are system and method for precisely determining the actual location of a standing wave created within a wellbore as well as other key properties about the created wave. The introduced system and method utilize a fiber optic sensing system, such as fiber optic Distributed Acoustic Sensing (DAS) system, that actively interrogates and monitors fiber optic sensors along the length of a wellbore. The introduced system and method generate one or more pressure pulses that combine with one another to create a standing wave within a wellbore and process the acoustic response of the standing wave using the fiber optic DAS system over a wide range of frequencies. Based on the measurements, the introduced system and method determine the actual location of the created standing wave and move it to a desired location within the wellbore by adjusting one or more properties of the pressure pulses.

Using fiber optic sensing to establish location, amplitude and shape of a standing wave created within a wellbore

A method, includes: detecting one or more properties of a waveguide having a downhole end and an uphole end; and responsive to the detected one or more properties, positioning into a passage of a wellbore the waveguide to minimize tension thereof.

Method and system for detecting one or more properties, positioning, and minimizing tension of a waveguide

Aspects of the subject technology relate to systems, methods, and computer-readable media for machine learning analysis of low-frequency signal data in fracturing operations. The present technology can receive strain data associated with a monitoring well that is proximate to a treatment well. The strain data can comprise information representing a fracturing operation associated with the treatment well. Further, the present technology can convert the strain data into image data where a color scale corresponds to a degree of strain observed by a fiber optic cable deployed in the monitoring well. As follows, the present technology can provide the image data to a machine-learning model, which is configured to identify one or more features in the image data.

Machine learning analysis of low-frequency signal data in fracturing operations

Growing community of inventors

Houston, TX, United States of America

Mikko K Jaaskelainen