Halliburton Energy Services, Inc.

Nl Industries

Chevron U.s.a. Inc.

Landmark Graphics Corporation

Other

Multi-chem Group, LLC

Dale E Jamison

Xiangnan Ye

Cato Russell McDaniel

William Walter Shumway

Robert J Murphy

Andrew David Vos

Sandeep D Kulkarni

Matthew Lynn Miller

Sharath Savari

Preston Andrew May

Jay Paul Deville

Robert Lynn Williams

Li Gao

Kushabhau Dagadu Teke

Donald Lee Whitfill

Katerina V Newman

Adam Dotson

Kenneth Heidt Matthews, Iii

Kevin Gregory Kleinguetl

Michael Thomas Pelletier

Daniel Joshua Stark

Ian D Robb

Charles R Landis

Jeff Kirsner

Brice Aaron Jackson

David L Carbajal

Don Siems

Kimberly Burrows-Lawson

Amir Bar

Jeffrey James Miller

Sorin Gabriel Teodorescu

Stephen W Almond

Timothy Neal Harvey

Arunesh Kumar

Vitor Lopes Pereira

Mateusz Michal Dyngosz

Sandeep Kulkarni

Brian Alan Evans

Shawn Lynn Broussard

Marek Kozikowski

Don L Whitfill

Claudia Lucia Bonin De Oliveira

Rui Shen

Denise Nicole Benoit

Lee Jeremy Hall

Kenneth William Pober

Chesnee Lae Davis

Dhanashree Gajanan Kulkarni

Donald Curtis Van Slyke

Jonathan Paul Walker

Drew A Fowler

Amir Shojaei

Kay A Morris

Aidan James Porter

Jason Glen Bell

Yerzhan Ayapbergenov

Anita Gantepla

Nicholas Charles Braley

Lalit N Mahajan

Jacob Michael Sieverling

Philip D Nguyen

B Raghava Reddy

Christopher Michael Jones

Krishna M Ravi

Shilin Chen

Loan K Vo

Anthony Herman Van Zuilekom

John L Maida

Etienne M Samson

Darren George Gascooke

Mathew Dennis Rowe

Michel Joseph LeBlanc

Jeffrey Marc Yarus

Andreas Ellmauthaler

Adan Hernandez Herrera

Siva Rama Krishna Jandhyala

Michael J Szymanski

Megan Renee Pearl

Ketan Chimanlal Bhaidasna

Kay Ann Galindo

Shadaab Syed Maghrabi

Jason Maxey

Yi Pan

Catherine Martin Santos

James Van Fisk, Jr

Adam Harold Martin

Waseem Abdulrazzaq

Lawrence Joseph Herskowitz

Jianxin Lu

Kingsley Ihueze Nzeadibe

Alexandra Clare Morrison

Alexander Simon Chretien

Marcus Ray Hudson

Arash Haghshenas

Brad Todd

Reem Mohammed Alburaikan

Jessica Paola Ramirez Angulo

Pramod Chamarthy

Feifei Zhang, Jr

Peter Gonzalez

Determining the likelihood of an event occurring during a drilling operation can utilize various input parameters collected from one or more sensors located downhole or at a surface location of the borehole undergoing the drilling operation. The collected input parameters can be weighted according to user parameters and then calculated over time to determine a trend in the input parameters. The trend can be analyzed against one or more probability models, such as using a machine learning process, to determine a potential for the event to occur and an estimated time interval over which the event may occur. The results of this analysis can be used by users or drilling systems to implement corrective actions to reduce or avoid the potential for the event occurring.

Event detection using hydraulic simulations

A method performed while drilling a wellbore in a subsurface formation with a drill bit. The method comprises obtaining sonic logs of the subsurface formation proximate the drill bit. The method comprises determining mechanical specific energy based on drilling parameters. The method comprises comparing respective trends of the sonic logs and the mechanical specific energy of the drill bit to identify a cause for a change in drilling performance of the drill bit. The method comprises performing a drilling operation based on the cause for the change in the drilling performance.

Identifying bit wear and justifying bit trip

A method of monitoring a drilling operation of a drilling rig includes receiving one or more parameters of a drilling operation of a drilling rig. The one or more parameters are detected by one or more sensors. The method further includes recognizing, using a trained machine-learning model, a signature in the one or more parameters; determining a corrective action based on the recognized signature; and outputting a recommendation for performing the corrective action or autonomously executing the corrective action.

Monitoring a drilling operation using artificial intelligence

Systems and methods of the disclosed embodiments include a rheometer having a housing with a fluid inlet and a fluid outlet, a cylinder with a cavity located to receive fluid that passes into the fluid inlet, a motor configured to rotate the cylinder, a torsion bob within the cavity, and a controller located remotely from the rheometer. The controller includes a pressure regulator configured to pressurize fluid to power the motor, a rotation sensor configured to receive an optical rotation signal indicating a rotation speed of the cylinder, and a torque sensor configured to receive an optical signal indicating a torque on the torsion bob. The controller may be configured to receive a rotation speed signal from the rotation sensor, a torque signal from the torque sensor, and to calculate a shear stress for the fluid based on the rotation speed signal and the torque signal.

Optical fluidic methods for a rheometer

Methods and systems for managing dielectric properties of a pulsed power drilling fluid are provided. In one embodiment, the methods include introducing a drilling fluid into a drilling pipe that extends into a portion of a wellbore; separating the drilling fluid into a solids rich portion and a solids lean portion in the drilling pipe at a location proximate to a pulsed power drill bit; allowing the solids lean portion of the drilling fluid to flow through the pulsed power drill bit; and drilling at least a portion of a wellbore. In some embodiments, the methods and systems include passing the drilling fluid through one or more hydrocyclones. In some embodiments, the methods and systems include passing the drilling fluid through one or more centrifuges.

Managing dielectric properties of pulsed power drilling fluids

A sensor assembly comprising one or more sensors configured to be radially positioned about a flowline, each of the respective sensors configured to obtain a respective measurement of a fluid flowing through the flowline at a respective position, wherein one or more phase properties of the fluid in the flowline are determined based on the measurements.

Sensor assembly for interpreting multiphase flow in a flowline

A sag detection apparatus comprises an oven containing a sample cell supported by a cell support structure, a thermal conductivity sensor including a sensor housing, and a roller with a first end supported by a first bearing and fixedly coupled to a first end of the cell support structure and a second end supported by a second bearing and fixedly coupled to a second end of the cell support structure. Temperature sensor wires electrically connect a temperature sensor and first fixed contact via stationary contacts configured to remain fixed during rotation of the roller and rotating contacts configured to rotate with rotation of the roller. Heat source wires electrically connect a heat source and a second fixed contact via stationary contacts configured to remain fixed during rotation of the roller and rotating contacts configured to rotate with rotation of the roller.

Sag detection using thermal conductivity

Systems and methods using: a membrane unit to treat fluids recovered from an oil and gas well are provided. The membrane unit may include a membrane having a porous substrate at. least partially coated with graphene oxide, making the membrane hydrophilic. The membrane separates water from other components within a fluid stream. The membrane unit may include an inlet to receive a fluid stream into the membrane unit. The fluid stream may be pretreated prior to reaching the membrane unit The membrane unit may also include a first outlet in fluid communication with one side of the membrane and a second outlet in fluid communication with the opposite side of the membrane.

Treating fluids recovered from well operations using a graphene oxide coated membrane

A method may include providing one or more inputs to a hybrid data generator, wherein one of the one or more inputs is based at least in part on a wellsite location, wherein the hybrid data generator comprises a large language model, and wherein the large language model is based at least in part on a machine learning algorithm. The method may further include utilizing an information handling system to generate a drilling program based at least in part on the one or more inputs and the hybrid data generator. The method may further include performing at least a portion of a drilling operation based at least in part on the drilling program and collecting at least one measurement from at least one sensor during the drilling operation.

Geomechanical data interpretation and recommendation system using large language models

Characterizing the decay of the microstructure of a drilling fluid gel using a model based on two exponential functions. Based on the model, identify at least two components of the decay model comprising a fast decay component and a slow decay component, wherein the fast decay component decays more quickly than the slow decay component. The decay of the microstructure of the gel over a time period can be determined using a rheometer or viscometer. Wellbore processes, including start up and tripping operations can be optimized based on the determination of the fast decay component and/or a slow decay component of the drilling fluid gel.

Fragile and normal viscoelastic components of drilling fluid gels

A self-cleaning temperature sensor comprises: a bellows coupled to a support structure; a pneumatic or hydraulic cylinder; a sensor configured to measure a temperature of a fluid and extending from the pneumatic or hydraulic cylinder, wherein the sensor comprises an exposure end configured to contact the fluid during sensing by the sensor; sensor wires connected with the sensor and extending to a control system; and a self-cleaning system comprising: a hood having walls extending radially outward a distance along a central axis wherein the hood defines a first volume; and a cleaning brush integrated with or adjacent the bottom of the support structure. In a retracted configuration, the exposure end of the sensor is positioned at a retracted position a distance from cleaning brush, and, in an extended configuration, the exposure end of the sensor extends a distance along the central axis away from the retracted position.

Self-cleaning thermometer for use in a mud pit

A self-cleaning thermal conductivity sensor comprises: a bellows coupled to a support structure; a pneumatic or hydraulic cylinder; a sensor configured to measure a thermal conductivity of a fluid and extending from the pneumatic or hydraulic cylinder, wherein the sensor comprises an exposure end configured to contact the fluid during sensing by the thermal conductivity sensor, a temperature sensor, and a heat source; sensor wires connected with the thermal conductivity sensor and extending to a control system; a self-cleaning system comprising: a hood having walls extending radially outward and defining a volume; and a cleaning brush integrated with or adjacent the bottom of the support structure. In a retracted configuration, the exposure end of the sensor is positioned at a retracted position a distance from cleaning brush, and, in an extended configuration, the exposure end of the sensor extends a distance along the central axis away from the retracted position.

Self-cleaning thermal conductivity sensor for use in a mud pit

A method for creating generated formatted data, that includes receiving, by a sequential data generator, raw data, where the raw data includes formation data at a drilling environment, processing the raw data to obtain generated recommendation data, where the generated recommendation data includes a proposed drilling location, and creating the generated formatted data, where the generated formatted data includes the generated recommendation data.

Service document generation using large language models

Gas bubble migration can be managed in liquids. In one example, a system can execute wellbore-simulation software to simulate changes in gas dissolution in a liquid over time. This may involve dividing the wellbore into segments spanning from the well surface to the downhole location, each segment spanning a respective depth increment between the well surface and the downhole location. Next, for each time, the system can determine a respective multiphase-flow regime associated with each segment of the plurality of segments based on a simulated pressure level, a simulated temperature, a simulated pipe eccentricity, and a simulated fluid velocity at the segment. The system can also determine how much of the gas is dissolved in the liquid at each segment based on the respective multiphase-flow regime at the segment. The system can display a graphical user interface representing the gas dissolution in the liquid over time.

Managing gas bubble migration in a downhole liquid

Methods for manufacturing lost circulation material particulates that may be for use in treating a subterranean formation are provided. In some embodiments, the methods include providing at a well-site a feed vessel that contains a liquid material, the liquid material including a monomer and an initiator; providing an injection device disposed underneath the feed vessel for directing at least a portion of the liquid material from the feed vessel to a reaction chamber, wherein the reaction chamber includes a fluid; discharging at least a portion of the liquid material from the feed vessel to the reaction chamber through an orifice disposed on the injection device; and forming LCM particulates at least in part by allowing at least a portion of the liquid material to polymerize in the fluid in the reaction chamber.

Methods of manufacturing lost circulation material at a well site

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Humble, TX, United States of America

Dale E Jamison