Halliburton Energy Services, Inc.

Jing Shen

Christopher Michael Jones

Dingding Chen

David L Perkins

Michael Thomas Pelletier

Bin Dai

Robert S Atkinson

Anthony Herman Van Zuilekom

Li Gao

Wei Zhang

Ming Gu

Mark Anton Proett

Darren George Gascooke

William Joseph Soltmann

Hua Xia

Marian Morys

Megan Renee Pearl

Tian He

Katrina S Akita

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

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 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

Mutual-complementary modeling and testing methods are disclosed that can enable validated mapping from external oil and gas information sources to existing fluid optical databases through the use of forward and inverse neural networks. The forward neural networks use fluid compositional inputs to produce fluid principal spectroscopy components (PSC). The inverse neural networks apply PSC inputs to estimate fluid compositional outputs. The fluid compositional data from external sources can be tested through forward models first. The produced PSC outputs are then entered as inputs to inverse models to generate fluid compositional data. The degree of matching between reconstructed fluid compositions and the original testing data suggests which part of the new data can be integrated directly into the existing database as validated mapping. The applications of using PSC inputs to reconstruct infrared spectra and estimate oil-based-mud (OBM) contamination with endmember spectral fingerprints are also included.

The disclosed embodiments include systems and methods to perform in-situ analysis of reservoir fluids. In some embodiments, the system includes a first vial containing a first insulating cylinder having a first internal cavity for storing electrolytes, a capillary tube, and a first sealable end having a first seal that prevents the electrolytes that are stored in the first internal cavity from flowing through the first sealable end while the first seal remains intact. The system also includes a second vial containing a second insulating cylinder having a second internal cavity for receiving the electrolytes that are stored in the first insulating cylinder, and a second sealable end having a second seal. The system further includes a tube positioned between the first vial and the second vial, where the tube provides at least one fluid flow path between the first vial and the second vial.

In-situ reservoir fluid analysis system

A method may include transforming optical responses for a fluid sample to a parameter space of a downhole tool. The optical responses are obtained using a first operational sensor and a second operational sensor of the downhole tool. Fluid models are applied in the parameter space of the downhole tool to the transformed optical responses to obtain density predictions of the fluid sample. The density predictions of the first operational sensor are matched to the density predictions of the second operational sensor based on optical parameters of the fluid models to obtain matched density predictions. A difference between the matched density predictions and measurements obtained from a densitometer is calculated, and a contamination index is estimated based on the difference.

Dual-sensor tool optical data processing through master sensor standardization

A system includes an optical computing device having an optical multiplexer that receives a sample light generated by an optical interaction between a sample and an illumination light is provided. The system includes sensing elements that optically interact with the sample light to generate modified lights, and a detector that measures a property of the modified lights separately. Linear and nonlinear models for processing data collected with the above system to form high-resolution spectra are also provided. Methods for designing optimal optical multiplexers for optimal reconstruction of high-resolution spectra are also provided.

Obtaining high-resolution spectral data of formation fluids from optical computing device measurements

A downhole formation fluid identification sensing module for measuring averaged gas molecular weight of wellbore formation fluid acquires simultaneous temperature, pressure, and density measurements. The sensing module includes two venturi-type gas sensors that both contain vibrating tubes. During operation, formation fluid flows through the vibrating tubes whereby resonant frequency measurements are acquired simultaneously with temperature and pressure measurements. Each measurement is then utilized to determine the gas molecular weight of the dry, wet or saturated formation fluid.

Thermal modulated vibrating sensing module for gas molecular weight detection

A method may include collecting measurement data using a first operational sensor and a second operational sensor of a downhole tool, standardizing optical responses of each operational sensor to a master sensor in a tool parameter space to obtain a standardized master sensor response, transforming the standardized master sensor response to a synthetic parameter space response of the master sensor, applying a fluid model with the synthetic parameter space response of the master sensor to predict a fluid characteristic, comparing a first prediction obtained with the fluid model from the first operational sensor with a second prediction obtained with the fluid model from the second operational sensor, determining a fluid characteristic from the first prediction and the second prediction, and optimizing a well testing and sampling operation according to the fluid characteristic.

Disclosed are systems and methods that use discriminant analysis techniques and processing in order to reduce the time required to determine chemical and/or physical properties of a substance. One method includes optically interacting a plurality of optical elements with one or more known substances, each optical element being configured to detect a particular characteristic of the one or more known substances, generating an optical response from each optical element corresponding to each known substance, wherein each known substance corresponds to a known spectrum stored in an optical database, and training a neural network to provide a discriminant analysis classification model for an unknown substance, the neural network using each optical response as inputs and one or more fluid types as outputs, and the outputs corresponding to the one or more known substances.

Discrimination analysis used with optical computing devices

This disclosure includes methods for designing a simplified Integrated Computational Element (ICE) and for optimizing a selection of a combination of ICE designs. A method for fabricating a simplified ICE having one or more film layers includes predicting an optimal thickness of each of the one or more film layers of the simplified ICE using a neural network. A method for recalibrating the fabricated ICE elements for system implementation is also disclosed. The disclosure also includes the simplified ICE designed by and the ICE combination selected by the disclosed methods. The disclosure also includes an information handling system with machine-readable instructions to perform the methods disclosed herein.

Optical sensor optimization and system implementation with simplified layer structure

A photometric system design methodology employs genetic algorithms to optimize the selection of optical elements for inclusion in the photometric system in order to improve system performance with respect to environmental conditions (i.e., to 'ruggedize' the photometric system). The genetic algorithms utilize a multi-objective fitness function to evolve simulated optical element selection, which may be a combination of optical filters and integrated computational elements. The system may also output a size reduced database that serve as simulated candidate optical elements through global optimization, or may output a fixed number of simulated optical elements through conditional optimization for actual tool implementation and calibration analysis.

System, method and computer program product for photometric system design and environmental ruggedization

Methods and systems for obtaining high-resolution spectral data of formation fluids from optical computing device measurements

Apparatus, methods, and systems related to a thin-layer spectroelectrochemistry cell; electrically coupling a second end of a working electrical wire lead, a second end of a counter electrical wire lead, and a second end of a reference electrical wire lead to a potentiostat; introducing a conductive fluid into a cell body in the spectroelectrochemistry cell; introducing a detection species into the cell body; introducing a sample into the cell body; applying a voltage potential across the transparent sample window to drive an electrochemical reaction between the detection species and the sample in the transparent sample window fluid; transmitting electromagnetic radiation into an optical path through the transparent sample window, thereby optically interacting the electromagnetic radiation with the transparent sample window fluid to generate modified electromagnetic radiation; receiving the modified electromagnetic radiation with a detector; and generating an output signal corresponding to a characteristic of the sample.

Thin-layer spectroelectrochemistry cell and methods for use in subterranean formation operations

A method of calibration transfer for a testing instrument includes: collecting a first sample; generating a standard response of a first instrument based, at least in part, on the first sample; and performing instrument standardization of a second instrument based, at least in part, on the standard response of the first instrument. Data corresponding to a second sample is then obtained using the second instrument and a component of the second sample is identified based, at least in part, on a calibration model.

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Houston, TX, United States of America

Jing Shen