Schlumberger Technology Corporation

Services Petroliers Schlumberger

Schlumberger Holdings Limited

Mark Andersen

Denis Klemin

Alexander Nadeev

John Ratulowski

Sergey Sergeevich Safonov

Dmitry Anatolievich Koroteev

Murtaza Ziauddin

Oleg Dinariev

Omer M Gurpinar

Nikolay Vyacheslavovich Evseev

Evgeny Ivanov

Igor Andreevich Varfolomeev

Ivan Victorovich Yakimchuk

Alexander Nikolaevich Nadeev

Ivan Yakimchuk

Denis Vladimirovich Klemin

A method for performing a simulation of a field having a subterranean formation is described. The method includes obtaining phase behavior data of subterranean fluids of the field, generating an equation of state (EOS) model of the fluids based on the phase behavior data, generating a Helmholtz free energy model that reproduces predictions of the EOS model over a pre-determined pressure and temperature range, and performing the simulation of the field using the Helmholtz free energy model. The method may further include reducing the EOS model to a reduced EOS model having a reduced number of components to represent the EOS model over a pre-determined pressure and temperature range, generating the Helmholtz free energy model based on the reduced EOS model, and obtaining and using phase behavior data of injection fluids used. A computer system data.

Construction of digital representation of complex compositional fluids

A method and computer system for performing simulation of a field having a subterranean formation. The method includes obtaining measured core sample data of a core sample retrieved from the formation, the core sample data measured by injecting fluid into the core sample, obtaining a digital rock model of the core sample describing a physical pore structure in the core sample, and obtaining a fluid model describing a physical property of the fluid. A digital core analysis (DCA) of the core sample is performed to generate a DCA simulation result and the DCA is tuned using the measured core sample data to reduce a difference between the DCA simulation result and the measured core sample data. The tuning is performed by adjusting, in response to the difference exceeding a pre-determined limit, a parameter of the DCA to generate an adjusted parameter and further performing the DCA to reduce the difference.

Tuning digital core analysis to laboratory results

A method for performing simulation of a field having a subterranean formation, including: obtaining a three-dimensional (3D) porous solid image of a core sample, the core sample representing a portion of the field; generating a digital rock model from the solid image, the digital rock model describing a physical pore structure in the core sample; obtaining phase behavior data of fluids of the field; generating a digital fluid model of the fluids based on the phase behavior data, the digital fluid model describing a physical property of the fluid; performing, on a computer system and based on the digital rock model and the digital fluid model, simulations of the field by varying an input parameter for the simulations; and analyzing an output parameter generated by the simulations to determine an effect of varying the input parameter on the output parameter.

Digital core sensitivity analysis

Methods of well treatment include modeling approaches that account for the effect of pore structure during well stimulation treatments. In one aspect, methods may include preparing a computer model of a porous medium; simulating an injection of a fluid into the computer model of the porous medium; measuring a pore-scale heterogeneity of the computer model of the porous medium, and designing a stimulating fluid treatment for the porous medium. Other aspects may include the development of a wellbore stimulation methodology that allows stimulation fluid breakthrough curves of differing formation samples to be plotted as a single curve that accounts for the varied pore structure of the respective samples.

Well treatment with digital core analysis

A method and system for analysis of a digital core image obtained from a sample are disclosed. The method includes performing segmentations on the digital core image using multiple approaches to obtain multiple segmented images which are statistically analyzed to select the most suitable approach of the multiple approaches. Thereafter, a digital core model is generated using the segmented image corresponding to the most suitable approach. A simulation test may be performed on the digital core model to obtain a model test result and an oilfield operation may be performed based on the model test result. The system includes measurement and testing equipment to obtain the digital core image and a computing system including a data repository for storing a digital core image and a digital core model, and a digital core modeling tool. The digital core modeling tool performs the segmentations, statistical analysis, and generates the digital core model.

Digital core model construction

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Houston, TX, United States of America

Mark Andersen