Schlumberger Technology Corporation

Alexander Nadeev

Dmitry Anatolievich Koroteev

Denis Klemin

Evgeny Mikhailovich Chuvilin

Mark Andersen

Igor Andreevich Varfolomeev

Dmitry Mikhailovich Mikhailov

Valery Vasilievich Shako

Dmitry Alexandrovich Korobkov

Nikita Ilyich Ryzhikov

Olga Vladimirovna Popova

Gary John Tustin

Diankui Fu

Bruno Lecerf

Murtaza Ziauddin

Oleg Dinariev

Omer M Gurpinar

John Ratulowski

Sergey Sergeevich Safonov

Valerie Lafitte

Andrey Vladimirovich Fedorov

Alexander Starostin

Shiyi Wang

Vadim Kamilievich Khlestkin

Ivan Victorovich Yakimchuk

Andrey Vladimirovich Kazak

Olesya Levanyuk

Tatiana Zolnikova

Ivan Viktorovich Yakimchuk

A method, apparatus, and program product perform microstructure analysis of a digital image of rock using a trained convolutional neural network model to generate a plurality of rock features. The rock features can represent a pore space in the microstructure of the rock including pores and throats. In many implementations, a statistical process can be applied to the rock features to generate characteristics of the pore space which can be used in classifying the rock.

Automated material classification by structural features

Evaluating structural changes in a sample resulting from a treatment of the sample. At least one sample of the material is scanned before and after the treatment and a first and a second image of the sample are obtained. The first and the second images are registered in a full spatial resolution using at least one region of the first image and at least one region of the second image, the regions corresponding to the same part of the sample. The registered images are analyzed and the changes in each sample caused by the performed treatment are evaluated.

Method for evaluating structural changes of a sample resulting from a treatment of the sample

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

In order to predict properties of a formation in a near-wellbore area exposed to a drilling mud rheological properties of the drilling mud, of a filtrate of the drilling mud and of a reservoir fluid are determined. Properties of an external mudcake, porosity and permeability of the core sample are determined. A mathematical model of the external mudcake is created. The drilling mud is injected through a core sample and dynamics of pressure drop across the sample and dynamics of a flow rate of a liquid leaving the sample are determined. Using an X-ray micro Computed Tomography a profile of concentration of particles of the drilling mud penetrated into the sample is determined. A mathematical model is developed for the internal mudcake to describe dynamics of changes in concentration of the particles of the drilling mud in a pore space of the core sample. A coupled mathematical model of the internal and the external mudcakes is created and parameters of the mathematical model of the internal mudcake are determined providing matching of simulation results to the experimental data on injection the drilling mud through the core sample and to the concentration profile of the particles of the drilling mud.

Method for predicting changes in properties of a formation in a near-wellbore area exposed to a drilling mud

A method for estimating porosity of a rock sample comprises the steps of defining a total mineral content of a sample, determining relative volume fractions for each mineral and determining X-ray attenuation coefficients for the defined minerals. Then, a first X-ray attenuation coefficient for a synthetic sample combined from the same minerals with the same volume fractions but with no pores is determined. X-ray micro/nanoCT scanning of the sample is performed and a second X-ray attenuation coefficient for the rock sample is determined. Porosity can be calculated as for a sample filled with a gas, water or light hydrocarbons, so for a sample which pores are filled with heavy hydrocarbons, or other liquid/gases with X-ray attenuation coefficient comparable with X-ray attenuation coefficient for the rock sample or for the synthetic sample.

Method for estimating porosity of a rock sample

A sample of frozen rocks is placed into contact with a frozen solution of an X-ray contrast agent at subzero temperature. Upon the end of saturation of the sample, a computed X-ray microtomography of the sample is conducted at a subzero temperature. The obtained microtomographic image is analyzed and spatial distribution and concentration of ice and/or gas hydrate inclusions, as well as open and closed porosity are determined.

Method for examining samples of frozen rocks

A sample of porous material is placed in a calorimeter cell and a pressure in the cell is increased starting from a pressure value of a first step by filling the cell with a wetting fluid. Measurements are taken of a heat flow to the cell and a fluid volume at each step. Then, the pressure in the cell is decreased to the pressure value of a first step with continued measurements of the heat flow to the cell. Increase and following decrease of the fluid pressure in the cell are repeated at least once. Then a temperature in the cell is decreased below a wetting fluid crystallization point. Once the fluid has been fully crystallized in sample pores, the temperature in the cell is increased above a wetting fluid melting point. Wetting limiting angle of the pores filled with fluid, and pore sizes are determined based on the results of heat flow measurements with due consideration of heat effect of fluid compression.

Method for determining the properties of porous materials

A method for building a 3D model of a rock sample comprises performing X-ray micro/nanoCT scanning of a rock sample and obtaining its initial three-dimensional microstructure image in a gray scale. Then, an analysis of the obtained three-dimensional image of the rock sample is performed and a binarization method is selected in dependence of the image quality and properties of the rock sample. The selected binarization method is at least once applied to the obtained initial three-dimensional image of the sample. Obtained 3D binarized image represents a 3D model of the rock sample.

Method for building a 3D model of a rock sample

A sample of an unconsolidated porous medium is frozen and at subzero temperature is placed into contact with a frozen solution of an X-ray contrast agent. Upon the end of saturation of the sample, X-ray computed microtomography of the sample is conducted at subzero temperatures and by means of analyzing the obtained computer tomograhic image, spatial distribution and concentration of ice and/or gas hydrate inclusions, open and closed porosity, pore size distribution, specific surface in the sample are determined.

Method for examining samples of unconsolidated porous media

A water-soluble salt of a metal with a high atomic weight is selected as an X-ray contrast substance providing a selective ion-exchange reaction with a clay. The salt has a general formula RM, where Ris selected from a group consisting of Ba; Sr; Tl; Rb. . . , and Mis selected from a group consisting of Cl; NO; OHn; CH3COO, SO; . . . . The X-ray contrast substance is injected into a core sample. Upon completion of the selective ion exchange reaction a non-contrast displacing agent is injected into the sample. The sample is scanned by computer X-ray microtomography. An area of interest and a reference cross-section are selected at the obtained computer tomography image. Grayscale histograms in cross-sections of the sample are obtained. Spatial distribution and concentration of the clay is estimated by means of histograms analysis starting from the reference cross-section histogram.

Method for determining spatial distribution and concentration of clay in a core sample

A method for treating a subterranean formation penetrated by a wellbore is provided. The method includes providing a treatment fluid containing a viscoelastic surfactant having at least one degradable linkage, a hydrolysable material, and a pH control material, and injecting the treatment fluid into a subterranean formation. The pH control material may have a pH equal or greater than about 9 and may include a strongly alkaline material and an oxidizing agent.

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Spring, TX, United States of America

Alexander Nadeev