Schlumberger Technology Corporation

Saudi Arabian Oil Company

University of Strathclyde

Services Petroliers Schlumberger

Wael Abdallah

Shouxiang Mark Ma

Ping Zhang

Mohammed Badri

Mikhail Stukan

Chengbing Liu

Reza Taherian

Bastian Sauerer

Salah Mohammed Al-Ofi

Andrea Valori

Patrice Ligneul

Dominic Joseph Brady

Sharath Chandra Mahavadi

Dmitry Eskin

Mohammed Fadhel Al-Hamad

Oliver Clinton Mullins

Dean M Homan

Youxiang Zuo

Andrew Emil Pomerantz

Cosan Ayan

Andrew Clarke

Mohan Kanaka Raju Panga

Richard E Lewis

Tianhua Zhang

Paul Ryan Craddock

Gong Li Wang

Shawn David Taylor

Ahmed Gmira

Francois Xavier Dubost

Simon Ivar Andersen

Simone Di Santo

Anas Mohammedali Almarzooq

Geza Horvath Szabo

Dingan Zhang

Johannes J M Buiting

Junyi Yuan

Farhan Ali

Paul Swenson

Robin Singh

Leilei Zhang

Marie Van Steene

Maxim Fedorov

Mohamed Ahmed Abdel Reheem Hamdy

Bastian Christoph Eddie Sauerer

Tariq Alshaikh Mubarak

Mohamed Saif

Ramy Ahmed Mohamed

Sachit Saumya

Aida Abdullah Abri

Ali Jasim A Al Solial

Abdullah Habelreeh

A workflow is provided that extracts and isolates an oil/water interface of a formation fluid sample and employs a ToF-SIMS instrument to characterize properties of the oil-water interface of the formation fluid sample. Additionally or alternatively, the workflow can use a ToF-SIMS instrument to analyze a formation rock sample and characterize properties of the formation rock sample. The workflow can also involve combining the at least one property related to the oil-water interface of the formation fluid sample and the least one property related to the formation rock sample for output or display to a user.

Application of ToF-SIMS for analysis of formation fluid samples and formation rock samples

Methods and systems are provided for predicting thermal properties of a subsurface rock formation. A training dataset is derived from petrophysical properties of a plurality of formation rock samples and thermal properties of the plurality of formation rock samples. The training dataset is used to train a machine learning model that predicts label data representing the predefined set of thermal properties given input data representing the predefined set of petrophysical properties of an arbitrary formation rock sample. The machine learning model can be validated and deployed for use in predicting thermal properties of subsurface rock formations.

Methods and systems for predicting formation thermal properties

Method and systems are provided that characterize a geological formation, which involve collecting or obtaining electrical signal data that results from interaction of electrical signals with the formation. The electrical signal data is processed to determine data characterizing relative permittivity and conductivity of the formation. The data characterizing relative permittivity and conductivity of the formation are used as inputs to models to determine data characterizing water saturation of the formation, particularly useful in formation evaluation and reservoir saturation dynamic monitoring in fresh water and mixed salinity environments.

Quantification of formation water saturation and salinity using relative permittivity and conductivity measurements

A method can include receiving Stoneley wave data acquired by a sonic tool disposed in a borehole in a formation; performing an inversion using the Stoneley wave data to generate formation and borehole information; and identifying an interval along the borehole for performance of a downhole acquisition operation by a downhole tool using a machine learning model and the formation and borehole information as input to the machine learning model.

Borehole acquisition operation interval via Stoneley wave

A method for characterizing organic matter in a geological rock formation includes performing a Raman spectroscopy measurement on a rock sample to acquire a Raman spectrum of the rock sample. The method includes analyzing the Raman spectrum of the rock sample to determine data characterizing at least one Raman spectral feature corresponding to the rock sample, inputting the data characterizing the at least one Raman spectral feature corresponding to the rock sample into a computation model that determines a value of a property of organic matter in the rock sample, and storing or outputting or displaying the value of the property of organic matter in the rock sample. In embodiments, the property of organic material in the rock sample can be a property of kerogen in the rock sample, or a property of asphaltenes or bitumen in the rock sample.

Methods and systems for estimating properties of organic matter in geological rock formations

Methods and systems are provided that use resistivity log data to estimate water saturation of formation rock and/or other useful formation parameters (such as CEC) in a manner that accounts for one or more electrically conductive mineral components contained in the formation rock.

Water saturation estimation of pyrite-rich formation rock

Methods and systems are provided for clay detection, clay typing, and clay volume quantification using downhole electromagnetic measurements conducted by a downhole logging tool on a formation at a low frequency less than 5000 Hz. The downhole electromagnetic measurements are used to determine permittivity data that characterizes permittivity of the formation at the low frequency less than 5000 Hz. The downhole low frequency electromagnetic measurements are nondestructive, and the results indicate it is with high sensitivity to the existence of clays.

Clay detection and quantification using downhole low frequency electromagnetic measurements

Methods and systems are provided that determine or estimate total clay volume fraction in a formation for well log data of the formation using machine learning are described. Methods and systems are also provided that employ computation models to determine amounts or concentrations of at least one clay mineral in the formation from total clay volume fraction of the formation.

Methods and systems for characterizing clay content of a geological formation

Methods and systems are provided for characterizing formation water salinity of subsurface formation using multifrequency permittivity data over a range of frequencies below 1 MHz. The multifrequency permittivity data is processed to determine salinity of formation water contained in the subsurface formation. Other useful formation properties (such as formation water saturation) can be determined based on the formation water salinity.

Determination of formation water salinity using downhole low frequency electromagnetic measurements and permittivity dispersions based thereon

Methods may include emplacing a downhole tool within a wellbore, sampling a fluid downhole with the downhole tool; analyzing the fluid, and calculating an interfacial tension (IFT), wherein calculating the acid-base IFT contribution comprises measuring a concentration of a surface-active species directly. Apparatuses for measuring an interfacial tension (IFT) in a fluid downhole may be part of a downhole tool and may include a sampling head to sample the fluid; and a downhole fluid analysis module that includes a spectrometer capable of measuring a concentration of a surface-active species in the fluid, and a processor configured to determine the IFT of the fluid downhole based on the measured concentration of the surface-active species.

Method to predict downhole reservoir fluids interfacial tension

Methods and systems are provided that employ a combination of dielectric dispersion measurement(s) and Nuclear Magnetic Resonance (NMR) measurement(s) to determine data that characterizes tortuosity of rock and data that characterizes tortuosity of fluid phases in the rock independently from one another.

Methods and systems to determine tortuosity of rock and fluids in porous media

Methods and systems are provided for characterizing a subterranean formation that involve the generation of four 3D geological model of the formation that are updated before and after an enhanced hydrocarbon production process.

Methods and systems for characterizing a hydrocarbon-bearing rock formation using electromagnetic measurements

Methods and systems are provided for characterizing interfacial tension (IFT) of reservoir fluids, which involves obtaining fluid property data that represents fluid properties of a reservoir fluid sample measured downhole at reservoir conditions, and inputting the fluid property data to a computational model that determines a value of oil-water IFT of the reservoir fluid sample based on the fluid property data. In embodiments, the fluid property data represents single-phase fluid properties of the reservoir fluid sample, such as fluid density and viscosity of an oil phase of the reservoir fluid sample and fluid density of a water phase of the reservoir fluid sample. In embodiments, the computation model can be based on machine learning or analytics combined with a thermodynamics-based physics model.

Methods and systems for predicting interfacial tension of reservoir fluids using downhole fluid measurements

Embodiments of the present disclosure are directed towards systems and method for characterizing wettability. Embodiments may include acquiring, using at least one processor, multi-physics data and transforming the multi-physics data into transformed data that is more sensitive to wettability than the multi-physics data. Embodiments may further include processing, using a data processing engine, the acquired and transformed data using at least one of a data correlation technique and an inversion technique.

System and method for characterizing reservoir wettability from an imaging technique combined with multiphysics logs and data analytics

Methods and systems are provided for clay detection, clay typing, and clay volume quantification using electromagnetic measurements on a porous media sample at a low frequency less than 5000 Hz. The electromagnetic measurements are used to determine and store permittivity data that characterizes permittivity of the porous media sample at the low frequency less than 5000 Hz. The low frequency electromagnetic measurements can be performed in a laboratory, at a wellsite or other surface location. The low frequency electromagnetic measurements are nondestructive, and the results indicate that the methods and systems are highly sensitive to the existence of clays.

Clay detection and quantification using low frequency electromagnetic measurements

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Al-Khobar, Saudi Arabia

Wael Abdallah