Halliburton Energy Services, Inc.

Ming Gu

Dingding Chen

Christopher Michael Jones

Michael Thomas Pelletier

Darren George Gascooke

John Andrew Quirein

Mehdi Alipour Kallehbasti

Tian He

Di Du

Bin Dai

Jing Shen

Deepak Gokaraju

Mehdi Eftekharifar

Mehdi Alipour Kallenhbasti

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

Disclosed are methods and systems for determination of fluid contamination of a fluid sample from a downhole fluid sampling tool. A method may comprise obtaining a fluid sample, wherein the fluid sample comprises a reservoir fluid contaminated with a well fluid; obtaining input parameters, wherein the input parameters comprise fluid properties obtained from measurement of the fluid sample and mud filtrate composition; obtaining initial values of iterative parameters, wherein the iterative parameters comprise fluid contamination of the fluid sample; determining calculated fluid properties of the reservoir fluid using equation of state flash calculating; and repeating steps of determining component mole fractions and determining calculated fluid properties for use in the mole fraction distribution function until a comparison of one or more of the calculated fluid properties with one or more of the input parameters is within a tolerance error.

Drilling fluid contamination determination for downhole fluid sampling tool

Near real-time methodologies for maximizing return-on-fracturing-investment for shale fracturing. An example system can calculate, based on sonic data and density data, mechanical properties and closure stress of a portion of shale rocks for fracture modeling. The system can generate one or more rock mechanical models based on the mechanical properties and closure stress of the portion of shale rocks, and perform one or more fracture modeling simulations based on one or more treatment parameter values. Based on the one or more fracture modeling simulations, the system can generate a neural network model which predicts a fracture productivity indicator of an effective propped area (EPA) and/or an effective propped length (EPL), and calculate a return-on-fracturing-investment (ROFI) based on the EPA or EPL predicted by the neural network model.

Near real-time return-on-fracturing-investment optimization for fracturing shale and tight reservoirs

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 methods and systems for determination of fluid contamination of a fluid sample from a downhole fluid sampling tool. A method may comprise obtaining a fluid sample, obtaining input parameters, wherein the input parameters comprise fluid properties obtained from measurement of the fluid sample and mud filtrate composition, obtaining initial values of iterative parameters, determining component mole fractions of the reservoir fluid using the initial values of the iterative parameters in a mole fraction distribution function, determining calculated fluid properties of the reservoir fluid using equation of state flash calculating, and repeating steps of determining component mole fractions and determining calculated fluid properties and further obtaining updated values of the iterative parameters for use in the mole fraction distribution function until a comparison of one or more of the calculated fluid properties with one or more of the input parameters is within a tolerance error.

Disclosed herein are methods and systems for fluid characterization of fluid samples from a downhole fluid sampling tool. A fluid characterization method may include obtaining a fluid sample of a reservoir fluid; analyzing the fluid sample to derive input parameters, wherein the input parameters comprise fluid properties obtained from measurement of the fluid sample; determining component mole fractions of the fluid sample using a mole fraction distribution function; and determining calculated fluid properties using equation of state flash calculating.

Fluid characterization and phase envelope prediction from downhole fluid sampling tool

A method for shale fracturing includes determining dynamic-elastic properties of a shale deposit in a geological formation. A training database is generated by three-dimensional fracture modeling. A neural network is generated in response to output results of the training database. The shale fracturing may then be performed based on the neural network.

Shale fracturing characterization and optimization using three-dimensional fracture modeling and neural network

A combination model combining a velocity regression method (V-reg) with the modified ANNIE model may be used to predict the stiffness coefficients, C, in a transversely isotropic medium without using the Stoneley wave velocity. The stiffness coefficients may be used to characterize the surrounding formation, which may be used in hydraulic fracture modeling, stage/perforation design, and well completion operations (e.g., perforating and fracturing operations).

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Humble, TX, United States of America

Ming Gu