Halliburton Energy Services, Inc.

Pipesense, LLC

Quantic Energy Solutions Llo

Dingding Chen

Christopher Michael Jones

Bin Dai

Jing Shen

David L Perkins

Li Gao

Michael Thomas Pelletier

John Andrew Quirein

Anthony Herman Van Zuilekom

Syed Hamid

James Martin Price

Harry D Smith, Jr

Mark Anton Proett

Darren George Gascooke

Ming Gu

Roger Lynn Schultz

Peter Ojo Olapade

Tian He

Jeffery L Grable

Wei Zhang

Robert S Atkinson

Megan Renee Pearl

Larry Alan Jacobson

Paravastu Badrinarayanan

Bipin Kumar Pillai

Joni Polili Lie

Faisal Farooq Shah

Nestor Rodriguez

Abdolhamid Hadibeik

Murali Krishna Thottempudi

Michael C Dix

Martin T Hagan

Neal Gregory Skinner

Luis Emilio San Martin

Stanley Vernon Stephenson

Weijun Guo

William Joseph Soltmann

Bruce H Storm, Jr

Craig William Godfrey

Gulamabbas A Merchant

Haoshi Song

Philip Edmund Fox

Jerome Allen Truax

Ali Mese

Lucas Kurtis Fontenelle

Robert W Strickland

Sami Abbas Eyuboglu

Johanna Haggstrom

David Earl Ball

Michael David Nash

Sara Shayegi

Craig L Zitterich

Jacky Muri Wiener

Barry Fen Zhang

Chris Michael Jones

Allan Zhong

Edward Tian

Tuna Altay Sansal

Orlando De Jesus

Kamy Tehranchi

Mitchell Stuart

Scott Bauer

Deepak Gokaraju

John Howard

Orlando DeJesús

Meng Fun

Muhlis Unaldi

Sammi Abbas Eyuboglu

Improved systematic inversion methodology applied to formation testing data interpretation with spherical, radial and/or cylindrical flow models is disclosed. A method of determining a flow line parameter includes determining a diverse set of flow models and selecting at least one flow model from the diverse set of flow models representative, at least in part, of a formation tester tool, at least one formation, at least one fluid, and at least one flow of the at least one fluid. The method further includes lowering the formation testing tool into the at least one formation to intersect with the formation at least one formation and sealing a probe of the formation tester placed in fluid communication with the at least one formation. The method further includes initiating flow from the at least one formation and utilizing the at least one selected flow model to predict the flow line parameter.

Method and apparatus for flow line parameter interpretation with diverse flow models

A method includes receiving first material property data for a first material in one or more second materials, detecting material sensor data from at least one sensor, and applying an inverse model and a forward model to the first material property data to provide, at least in part, synthetic sensor measurement data for the one or more second materials.

Fluid optical database reconstruction methods and applications thereof

Provided herein are systems and methods to detect pipeline leaks. The systems and method can identify a pipeline pressure surge by applying a trained convolutional neural network (CNN) model for classifying pipeline pressure measurement images on each sensor site of a plurality of sensor sites, transfer pressure surge information obtained from at least a portion of the plurality of sensor sites to a cloud site, and determine whether the identified pressure surge is a pipeline leak at the cloud site using the pressure surge information. The plurality of sensor sites collect pipeline pressure measurement data. The pressure surge information corresponds to the identified pipeline pressure surge.

Systems and methods for improved pipeline leak detection

Improved systematic inversion methodology applied to formation testing data interpretation with spherical, radial and/or cylindrical flow models is disclosed. A method of determining a parameter of a formation of interest at a desired location comprises directing a formation tester to the desired location in the formation of interest and obtaining data from the desired location in the formation of interest. The obtained data relates to a first parameter at the desired location of the formation of interest. The obtained data is regressed to determine a second parameter at the desired location of the formation of interest. Regressing the obtained data comprises using a method selected from a group consisting of a deterministic approach, a probabilistic approach, and an evolutionary approach.

Method and apparatus for formation tester data interpretation with diverse flow models

A system to determine a contamination level of a formation fluid, the system including a formation tester tool to be positioned in a borehole, wherein the borehole has a mixture of the formation fluid and a drilling fluid and the formation tester tool includes a sensor to detect time series measurements from a plurality of sensor channels. The system includes a processor to dimensionally reduce the time series measurements to generate a set of reduced measurement scores in a multi-dimensional measurement space and determine an end member in the multi-dimensional measurement space based on the set of reduced measurement scores, wherein the end member comprises a position in the multi-dimensional measurement space that corresponds with a predetermined fluid concentration. The processor also determines the contamination level of the formation fluid at a time point based the set of reduced measurement scores and the end member.

Determination of mud-filtrate contamination and clean formation fluid properties

A method includes obtaining a plurality of master sensor responses with a master sensor in a set of training fluids and obtaining node sensor responses in the set of training fluids. A linear correlation between a compensated master data set and a node data set is then found for a set of training fluids and generating node sensor responses in a tool parameter space from the compensated master data set on a set of application fluids. A reverse transformation is obtained based on the node sensor responses in a complete set of calibration fluids. The reverse transformation converts each node sensor response from a tool parameter space to the synthetic parameter space and uses transformed data as inputs of various fluid predictive models to obtain fluid characteristics. The method includes modifying operation parameters of a drilling or a well testing and sampling system according to the fluid characteristics.

System and methods for mapping sensor data across parameter spaces

A method includes receiving fluid property data of a fluid and receiving material property data for materials in the fluid. The method includes detecting material sensor data from at least one sensor and applying an inverse model and a forward model to the fluid property data and the material property data to provide at least in part synthetic spectral channel data for the materials.

A method comprises determining an adaptive fluid predictive model calibrated with a plurality of types of sensor data, wherein the plurality of types of sensor responses comprise a first type of sensor response associated with a synthetic parameter space and a second type of sensor response associated with a tool parameter space. The method comprises applying the adaptive fluid predictive model to one or more fluid samples from field measurements obtained from a tool deployed in a wellbore formed in a subterranean formation and determining a value of a fluid answer product prediction with the applied adaptive fluid predictive model. The method comprises facilitating a wellbore operation with the tool based on the value of the fluid answer product prediction.

Optical sensor adaptive calibration

This disclosure presents a process for communications in a borehole containing a fluid or drilling mud, where a conventional mud pulser can be utilized to transmit data to a transducer. The transducer, or a communicatively coupled computing system, can perform pre-processing steps to correct the received data using an average of a moving time window of the received data, and then normalize the corrected data. The corrected data can then be utilized as inputs into a machine learning mud pulse recognition network where the data can be classified and an ideal or clean pulse waveform can be overlaid the corrected data. The overlay and the corrected data can be fed into a conventional decoder or decoded by the disclosed process. The decoded data can then be communicated to another system and used as inputs, such as to a well site controller to enable adjustments to well site operation parameters.

Machine learning mud pulse recognition networks

A method may comprise positioning a downhole fluid sampling tool into a wellbore; performing a pressure test operation within the wellbore; performing a pumpout operation within the wellbore; identifying one or more formation parameters at least in part from the at least one pressure test operation or the at least one pumpout operation; building a correlation model that relates a pumpout trend to the one or more formation parameters; determining a time when the downhole fluid sampling tool takes a clean fluid sample utilizing at least the correlation model; and acquiring the clean fluid sample with the downhole fluid sampling tool from the wellbore. Additionally, a system may comprise a downhole fluid sampling tool configured to: perform a pressure test operation within a wellbore; and perform a pumpout operation within the wellbore.

Contamination prediction of downhole pumpout and sampling

Aspects of the present disclosure relate to earth modeling using machine learning. A method includes receiving detected data at a first depth point along a wellbore, providing at least a first subset of the detected data as first input values to a machine learning model, and receiving first output values from the machine learning model based on the first input values. The method includes receiving additional detected data at a second depth point along the wellbore, providing at least a second subset of the additional detected data as second input values to the machine learning model, and receiving second output values from the machine learning model based on the second input values. The method includes combining the first output values at the first depth point and the second output values at the second depth point to generate an updated model of the wellbore, the updated model comprising an earth model.

Earth modeling methods using machine learning

A model can be trained for discriminant analysis for substance classification and/or measuring calibration. One method includes interacting at least one sensor with one or more known substances, each sensor element being configured to detect a characteristic of the one or more known substances, generating an sensor response from each sensor element corresponding to each known substance, wherein each known substance corresponds to a known response stored in a database, and training a neural network to provide a discriminant analysis classification model for an unknown substance, the neural network using each sensor response as inputs and one or more substance types as outputs, and the outputs corresponding to the one or more known substances.

Model based discriminant analysis

Disclosed herein are examples embodiments of a progressive modeling scheme to enhance optical sensor transformation networks using both in-field sensor measurements and simulation data. In one aspect, a method includes receiving optical sensor measurements generated by one or more downhole optical sensors in a wellbore; determining synthetic data for fluid characterization using an adaptive model and the optical sensor measurements; and applying the synthetic data to determine one or more physical properties of a fluid in the wellbore for which the optical sensor measurements are received.

Progressive modeling of optical sensor data transformation neural networks for downhole fluid analysis

A method may comprise positioning a downhole fluid sampling tool into a wellbore, performing a pressure test operation within the wellbore, performing a pumpout operation within the wellbore, identifying when a clean fluid sample may be taken by the downhole fluid sampling tool from at least the pressure test operation and the pumpout operation, and acquiring the clean fluid sample from the wellbore. A system may comprise a downhole fluid sampling tool and an information handling machine. The downhole fluid sampling tool may further comprise one or more probes attached to the downhole fluid sampling tool, one or more stabilizers attached to the downhole fluid sampling tool, and a sensor placed in the downhole fluid sampling tool configured to measure drilling fluid filtrate.

Growing community of inventors

Tomball, TX, United States of America

Dingding Chen