RU2778354C1 - Method and system for estimating the hydrocarbon reserves in an inhomogeneous layer - Google Patents

Abstract

FIELD: physics.

SUBSTANCE: invention relates to the field of geophysics and can be used to estimate the hydrocarbon reserves in a non-homogeneous layer. Proposed is a method for estimating fluids in a dense hydrocarbon reservoir inside a non-homogeneous geological layer or a section thereof, wherein the method includes: a) obtaining the physical parameters of fluids and the layer; b) constructing at least one three-dimensional (3D) model of the dense hydrocarbon reservoir applying the physical parameters, wherein such a three-dimensional model comprises an imitation of the pore structure and the mineralogical composition; c) calculating the amount of hydrocarbon for each said three-dimensional model in stage b); d) calculating the total amount of hydrocarbon reserves; and (e) creating a development plan based on the estimated total hydrocarbon reserves.

EFFECT: application of three-dimensional modelling of the non-homogeneous and porous structure of a nanoscale layer contributes to a more accurate estimation of the hydrocarbon reserves and fluid behaviour.

20 cl, 10 dwg

Description

FIELD OF TECHNOLOGY TO WHICH THE INVENTION RELATES

[0001] The present invention generally relates to methods and systems for quantifying and optimizing development schemes using enhanced oil recovery (EOR - English: enhanced oil recovery) and improved development methods (IOR - English: improved oil recovery) in the case of dense heterogeneous reservoir that combine digital rock representation with density functional modeling for porosity-scale processes.

BACKGROUND OF THE INVENTION

[0002] One of the important parts of planning the development of a hydrocarbon field is the evaluation and optimization of various schemes for applying enhanced oil recovery methods or improved development methods. This problem is currently being addressed by two complementary methods: (a) using detailed geologic and 3D reservoir modeling using commercial simulators to quantify reservoir processes, and (b) using physical coreflood tests in the laboratory. After a sufficient number of different development scenarios have been obtained, best by a combination of simulations and physical testing, the best option is determined as the optimal development solution.

SUMMARY OF THE INVENTION

[0003] The present invention includes any of the following embodiments, in any combination(s) of one or more of them:

[0004] According to an aspect of the present invention, one or more embodiments relate to a method for evaluating fluids in a tight hydrocarbon reservoir within a heterogeneous subterranean formation or section thereof, such method comprising the steps of : obtaining physical parameters of the fluids and the formation; building at least one three-dimensional (3D) model of a dense hydrocarbon reservoir using physical parameters, and such a three-dimensional model contains simulations of the structure of porosity and mineralogical composition; calculating the amount of hydrocarbons for each specified three-dimensional model; calculation of the total amount of hydrocarbon reserves; and creating a development plan based on estimated total hydrocarbon reserves.

[0005] Another embodiment provides a method for evaluating and optimizing enhanced oil recovery technologies or enhanced development techniques for tight hydrocarbon reservoirs, comprising the steps of: building at least one 3D rock model for a porous rock using physical properties and 3D images of a porous rock mass of representative core samples. samples from a tight hydrocarbon reservoir, wherein a set of 3D rock models is defined by a set of representative core samples, each 3D rock model containing a pore distribution and mineralogical composition; construction of a common fluid component in a three-dimensional rock model; calculation of three-dimensional distribution of fluid components in a three-dimensional rock model. The volume of the hydrocarbon reservoir under consideration is then filled with 3D rock models, which are determined using a set of representative core samples. This makes it possible to calculate the rate of exchange of fluid components between 3D rock models and dynamic indicators of the total amount of fluid components in a dense hydrocarbon reservoir. The next step is to develop a reservoir development plan based on the calculated flow dynamics.

[0006] These embodiments, along with other aspects, features, and advantages of the present invention, along with various features of novelty that characterize the present invention, are detailed in the claims appended to and forming part of this specification. The aspects and advantages mentioned above are, individually or collectively, neither exhaustive nor critical to the spirit or practice of the present invention. Other aspects, features and advantages of the present invention will become more apparent to those skilled in the art from the following detailed description given in conjunction with the accompanying drawings. Accordingly, the graphics and description are to be considered illustrative and not restrictive.

[0007] In this summary of the invention presents a selection of concepts, which are further described below in the detailed description. This summary is not intended to indicate key or essential features of the claimed subject matter, nor should it be construed as limiting the scope of the claimed subject matter.

BRIEF DESCRIPTION OF GRAPHICS

[0008] FIG. 1 shows the spatial distribution of rock grains, pores and solid organics in a two-dimensional image obtained using a focused ion beam in a scanning electron microscope (FIB-SEM - English: Focused Ion Beam - Scanning Electron Microscopy). Here rock grains are shown in white, pores are shown in black, and solid organic matter (with solid hydrocarbon) is shown in gray.

[0009] FIG. 2 is a reduced resolution and larger scale image showing features of heterogeneity such as hard grain dominated clusters, pore dominated clusters, organic solid dominated clusters, and microcracks.

[0010] FIG. Figure 3 shows a high resolution digital 3D model of the distribution of: pores and solid organic matter (left); only pores (in the middle); and only solid organics (on the right). In all three images mentioned above, the rock grains are made transparent.

[0011] FIG. 4 shows the 3D distribution of hydrocarbon components (HC) in liquid, HC in gas and HC in solid organics using a high-resolution 3D digital model.

[0012] FIG. 5, using a 3D digital model on a larger scale, shows the distribution of features of heterogeneity, such as clusters dominated by hard grains, clusters dominated by pores filled with liquid, clusters dominated by pores filled with gas, clusters dominated by solid organics, and microcracks.

[0013] FIG. 6 shows an example of a 3D distribution of hydrocarbon components (HC) in the initial equilibrium state using a high-resolution 3D digital model; with different shades indicating HC in liquid (1), HC in solid organics (2), and other shades indicating changes in composition.

[0014] FIG. 7, using a digital 3D model on a larger scale, an example of the distribution of signs of inhomogeneity in the initial equilibrium state is shown. Signs of heterogeneity are clusters with a predominance of hard grains, clusters with a predominance of pores filled with liquid, clusters with a predominance of solid organic matter, and microcracks.

[0015] FIG. 8A-B, using a high-resolution 3D digital model, shows an example of a 3D distribution of hydrocarbon components (HC) after gas-driven formation (Fig. 8A) and after additional production with a typical agent (surfactant) used to increase oil recovery (EOR) (FIG. 8B); different shades indicate HC in liquid (1), HC in gas (3), HC in solid organic (3), surfactant for EOR (4), and other shades indicate changes in composition.

[0016] FIG. 9A-B shows an example of the distribution of heterogeneities after primary extraction (a) and after an EOR process using surfactant treatment (b) using a larger scale digital 3D model. Signs of heterogeneity are clusters dominated by hard grains, clusters dominated by pores filled with liquid, clusters dominated by pores filled with gas, clusters dominated by solid organics, clusters dominated by the EOR agent, and microcracks.

[0017] FIG. 10 shows an example comparison of primary recovery and two scenarios using alternative EOR methods (two different surfactants).

DETAILED DESCRIPTION OF THE INVENTION

[0018] In the following description, numerous details are set forth to provide an understanding of certain embodiments of the present invention. It should be understood that in the following disclosure, many different embodiments, or examples, are presented for implementing various features of the various embodiments. Specific examples of components and their locations are described below to simplify the present description. Of course, these are just examples and are not limiting. In addition, in the present description, numeric and/or alphabetic reference positions may be repeated in various examples. This repetition is for the sake of simplicity and clarity and does not imply a relationship between the various embodiments and/or configurations discussed. However, those skilled in the art will appreciate that the system and/or methodology may be practiced without these details and that numerous variations or modifications to the described embodiments are possible. This description should not be considered as limiting, but only as given for the purpose of describing the general principles of the embodiments. The scope of the described embodiments is to be established with reference to the following claims.

[0019] In the context of this document, the terms "connect", "connection", "connected", "in connection with" and "connecting" mean "in direct connection with" or "in connection through one or more elements"; and the term "kit" means "one item" or "more than one item". In addition, the terms "attach", "attachment", "engaged", "engaged", and "engaged with" mean "directly attached to each other" or "attached to each other through one or more elements". In the context of this document, the terms "up" and "down"; "upper" and "lower"; "up and down"; as well as other similar terms denoting positions relative to a given point or element, are used to more clearly describe some elements. Generally, these terms refer to a reference point on the surface from which drilling is started, where this is the high point and the total depth is the low point, where the borehole (e.g., wellbore, borehole) is vertical, horizontal, or inclined relative to the surface. .

[0020] As used herein, the term "tight hydrocarbon reservoir" means a reservoir containing hydrocarbons (eg, oil and/or natural gas) and formed by relatively impermeable reservoir rock from which hydrocarbons are difficult to recover. The relative impermeability of the reservoir rock may be due to smaller grains or the presence of a mineral skeleton between larger grains, or may be due to the predominance of grains sized to match silty material or clay (as in the case of tight shale reservoirs). Tight hydrocarbon reservoirs may contain oil and/or natural gas in relatively impermeable reservoir rock, as well as water-based formation fluid such as brine.

[0021] As used herein, the term "petrophysical properties" means the physical and chemical properties of a reservoir rock and its hydrocarbon and non-hydrocarbon contents, such as water-based formation fluids. In the context of this document, the term "formation" means a set of rock units that have comparable lithological characteristics, facies, or other similar properties. In the context of this document, the term "physical parameters" refers to the physical properties of rocks or fluids that can be measured and quantified. In the context of this document, the term "Helmholtz energy" refers to the thermodynamic potential of a mixture, which characterizes the potential energy of this mixture at a certain temperature, volume and numbers of molecules of various types.

[0022] In the context of this document, the term “completion plan” refers to the events and equipment necessary to bring the wellbore into production after completion of drilling operations, including, but not limited to, the assembly of well pipes and equipment necessary to ensure safe and efficient production from oil or gas well.

[0023] As used herein, the term "development plan" refers to the events and equipment necessary to produce hydrocarbons from an oil and gas reservoir, including, but not limited to, drilling wells with predetermined trajectories, performing hydraulic fracturing operations, and other operations such as pumping agents for EOR/IOR. The development plan contains completion plans for individual wells.

[0024] Economical production from tight shale reservoirs (organic shale) is determined by the ability to place horizontal wells in a quality formation that can be effectively stimulated by hydraulic fracturing using logging and core data obtained from vertical wells. The main factors that determine successful production from these wells are petrophysical properties such as porosity, permeability, wettability, hydrocarbon saturation and pore pressure. Other factors include geomechanical attributes such as fracture surface area plus fracture conductivity. In addition to accurately measuring all physical and petrophysical properties of reservoirs, it is equally important to have accurate simulations based on known tight reservoir rock samples. With accurate and realistic simulations, a development plan can be developed to improve recovery efficiency.

[0025] Multiphase flow analysis improves the accuracy of simulations by accounting for most, if not all, of the influencing factors that are measurable in a tight hydrocarbon reservoir, including intrinsic properties, background properties, combined properties, and artificially introduced properties. For example, intrinsic physical and petrophysical properties of reservoirs, taking into account background properties such as temperature or pressure distribution. Other related factors include wettability distribution and absolute permeability distribution, as well as several artificially introduced factors such as secondary flooding or surfactant agents used in enhanced oil recovery (EOR) operations.

[0026] In addition to the multiphase flow analysis for a tight hydrocarbon reservoir, digital rock modeling is performed to estimate the total amount of fluid in the reservoir. Digital rock modeling involves a combination of technology for digital representation of rocks and the description of the density functional of multiphase multicomponent mixtures. In modeling, nanoscale digital rock models are used to calculate the actual distribution of hydrocarbons in the rock, which is then used to calculate the total amount of hydrocarbon reserves, taking into account the total volume of the reservoir, the estimated quantities of hydrocarbons (oil or gas) for 3D models, and also to calculate the relative frequency of occurrence in the collector.

EXAMPLE 1: DIGITAL ROCK MODELING

[0027] Estimating hydrocarbon fluid reserves for specific geological formations is a necessary step in field development planning and production forecasting. For conventional hydrocarbon fluid reservoirs, the estimation procedure includes the following steps: 1) estimating reservoir fluid properties (eg, density, composition) using, among other things, downhole fluid probes or recombined surface fluid probes; 2) estimating pore volume in the reservoir using appropriate well logging data or any other applicable data, methods and/or tools; 3) estimating the volume of portions of pores saturated with various fluid phases (eg, gas, oil, water, if a multiphase mixture is present) using appropriate well logging data or any other applicable data, methods and/or tools; and 4) calculating the total amount of fluid in the reservoir (gas, oil, water and certain hydrocarbon components) using the information obtained in the previous steps.

[0028] This conventional procedure is based on the assumption that fluid properties are the same in all parts of the reservoir that are in fluid communication. This assumption corresponds to the concept of the initial thermodynamic equilibrium of the fluid in the reservoir.

[0029] It is well known that in tight, heterogeneous formations (e.g., shale) with nanosized pores, there are various phenomena that can affect the state and composition of the fluid: adsorption/absorption, osmosis, capillary condensation, disjoining pressure, and dispersion forces. Because of this, the usual procedure for directly estimating reserves is not applicable.

[0030] Embodiments of the present invention provide a novel resource estimation method that combines digital rock representation technology and density functional description of multiphase multicomponent mixtures. In the method of the present invention, nanoscale digital rock models are used to calculate the actual distribution of hydrocarbons in the rock, and then the calculation of the total amount of reserves is performed using a summation procedure, in which one of the implementations uses data on the total volume of the reservoir (or part of it under consideration), the calculated quantities for 3D models and the relative frequency of their occurrence in the reservoir or in its part under consideration.

и нефти

для обычных коллекторов основана на интегральных уравнениях[0031] Typically, the procedure for estimating the amount of gas available

and oil

for conventional collectors based on integral equations

[0032]

[0033]

- это распределение пористости,

- плотности газа и нефти в условиях коллектора, и

- это распределения насыщенности газом и нефтью. Количественные показатели

получают от зондов скважинного флюида или зондов рекомбинированного поверхностного флюида, в то время как 3D-поля величин

получают в процессе геологического 3D-моделирования (по существу из соответствующих данных каротажа скважины). Когда требуется количество конкретных углеводородных компонентов, уравнения (1), (2) изменяют следующим образом[0034] where the integration is performed for the entire reservoir or a specific reservoir,

is the porosity distribution,

are the densities of gas and oil at reservoir conditions, and

are the distributions of gas and oil saturation. Quantitative indicators

are obtained from downhole fluid probes or recombined surface fluid probes, while 3D value fields

obtained in the process of geological 3D modeling (essentially from the corresponding well logging data). When the amount of specific hydrocarbon components is required, equations (1), (2) are modified as follows

[0035]

[0036]

представляют собой массовые концентрации газа и нефти в компоненте с номером i. Эти концентрации также оценивают с помощью зондов скважинного флюида или зондов рекомбинированного поверхностного флюида.[0037] where

are the mass concentrations of gas and oil in the i -th component. These concentrations are also measured using downhole fluid probes or recombined surface fluid probes.

[0038] Equations (1) - (4) provide estimates of the total mass of oil and gas in the reservoir, as well as the mass of individual hydrocarbon components. These parameters are used to calculate the corresponding volume and mass of gas and oil under surface conditions.

[0039] Equations (1) - (4) fail for tight heterogeneous reservoirs where fluid properties may change in narrow pores and a significant amount of hydrocarbons may be in adsorbed/absorbed form. In these cases, the reserve estimation procedure should be adjusted to take into account the relevant physical and chemical phenomena on the nanoscale.

[0040] Therefore, a new procedure for estimating hydrocarbon reserves is proposed, based on the following steps:

[0041] First , construct a thermodynamic description of the fluid in the reservoir by applying appropriate analytical equations of state, analyzing data from downhole fluid probes and/or recombined surface fluid probes, or any other fluid data, including, but not limited to, fluid inclusion analysis for the fluid. trapped in closed pores, as well as any other related experimental or analytical methods applied or analyzed when estimating the initial state of the reservoir (prior to production), or derived from a non-initial state by approximation, for example, the Peng-Robinson equation of state. In it, the initial state of the fluid is characterized by initial fluid state probes and interpreted as the state of the fluid in the reservoir within large pores or fractures, as the most mobile part of the reservoir fluid. In particular, an analytical expression is derived for the Helmholtz energy of the fluid per unit volume

[0042]

- это абсолютная температура коллектора и

- это молярные плотности химических компонентов (количества молекул определенных типов, приходящиеся на единицу объема) в смеси коллектора (индекс i означает порядковый номер конкретного компонента). Применив уравнение (5) для энергии Гельмгольца, приходящейся на единицу объема, можно вывести аналитические выражения для химических потенциалов[0043] where

is the absolute collector temperature and

are the molar densities of the chemical components (the number of molecules of certain types per unit volume) in the reservoir mixture (the index i means the serial number of a particular component). Applying equation (5) for the Helmholtz energy per unit volume, we can derive analytical expressions for chemical potentials

[0044]

.[0045] and numerical values of chemical potentials in the initial state of the fluid

.

[0046] Second, the geologic heterogeneity of the reservoir, or portion thereof, is studied using well log data and core samples to identify representative core samples. Such representative core samples are used to obtain 3D images of rocks using, among others, X-ray microtomography, 3D NMR imaging, 3D reconstruction based on petrographic analysis of thin sections and confocal microscopy, 3D reconstruction based on the analysis of 2D element maps, obtained by scanning electron microscopy (SEM), focused ion beam 3D scanning electron microscopy (FIB-SEM) with the possible addition of an energy dispersive X-ray spectroscopy (EDX) function, etc.; digital processing and morphological analysis of three-dimensional core images by sequentially applying image filtering, segmentation and recognition of a number of properties. Thus, a 3D digital model is created using the simulation scheme described above. As a result of this step, various digital 3D models of porous rock samples with detailed pore distribution and mineralogical composition are obtained.

[0047] Third, using the total Helmholtz energy of the fluid (5), digital 3D rock models and the interaction potentials between the constituent minerals and the components of the fluid mixture, analytically build the functional of the total Helmholtz energy of the fluid in the rock

[0048]

представляет собой абсолютную температуру, а

- молярные плотности химических компонентов флюида. В частности, это явное выражение применяют для получения аналитического выражения химических потенциалов составляющих компонентов

.[0049] In this expression

is the absolute temperature, and

- molar densities of the chemical components of the fluid. In particular, this explicit expression is used to obtain an analytical expression for the chemical potentials of the constituent components

.

[0050] Fourth , the 3D distribution of chemical components in the rock is numerically determined as the absolute conditional minimum of the density functional (7) with restrictions

[0051]

which is a necessary and sufficient condition for the distribution of the chemical components of the fluid in the rock to be in stable thermodynamic equilibrium and correspond to the initial state of the fluid as a whole.

[0052] This distribution corresponds to a stable thermodynamic equilibrium of the fluid within the rock sample, which is consistent with the data from the fluid probes discussed in the first step, which represent a mobile fluid. It also makes it possible to estimate average molar densities for digital rock models:

[0053]

с общим объемом

. Если в 3D-распределении флюида воксели (элементарные ячейки) могут быть конкретно отнесены к газу или нефти, это позволяет усреднить плотности для этих фаз[0054] where integration is performed over a 3D model

with total volume

. If voxels (unit cells) can be specifically assigned to gas or oil in a 3D fluid distribution, this allows averaging the densities for these phases

[0055]

[0056]

[0057] Fifth , the total amount of component i (in moles) in the reservoir (or part of the reservoir) is determined as the following sums by phase (if applicable)

[0058]

[0059]

[0060]

представляет собой объем коллектора (или части коллектора),

- это частота или вероятность встречаемости конкретной 3D модели

в коллекторе.[0061] where

is the volume of the reservoir (or part of the reservoir),

is the frequency or probability of occurrence of a particular 3D model

in the collector.

[0062] This indicator can be converted to mass units by multiplying it by the molar mass of the component

[0063]

[0064]

[0065]

[0066] These last results are adjusted hydrocarbon reserves instead of quantities (1), (2).

[0067] To demonstrate the application of the present invention, an in-place estimate was performed in a tight rock formation using the technique described by Eq. (5)-(17). Several representative core samples were studied to evaluate the geological heterogeneity of the reservoir. To simulate the thermodynamic equilibrium distribution of fluids on the pore scale, which corresponds to Eq. (5)-(8) used a set of representative rock blocks from these core samples. Next, the reserves were calculated for all blocks of rocks using Eq. (9)-(10). Finally, in-place reserves were evaluated using Eq. (11)-(17) and based on information about the actual predominance of each of the studied rock blocks in core samples.

[0068] Modeling within individual rock blocks was performed using pore-scale 3D numerical simulations that take into account phenomena in multi-phase composition, physical phenomena at the nanoscale level, and non-classical thermodynamic effects (thermodynamics of small systems). Namely, the numerical simulation was performed using density functional hydrodynamics (DFH - English: Density Functional Hydrodynamics), which leads to the achievement of the necessary minimum of the functional in Eq. (7) along with the fulfillment of the conditions in Eq. (eight).

[0069] To obtain the spatial distribution of grains, pores and solid organics within a representative block of rock from a representative core sample of a dense rock formation, scanning technologies that provide sufficiently high resolution (for example, scanning with FIB-SEM) were used, as shown in the example image on Fig. 1. At the same time, by applying a lower resolution scanning technology (eg, X-ray microtomography), information was obtained on the signs of heterogeneity at a larger scale (i.e., core scale), as shown in the example image in FIG. 2. The robustness of this analysis is achieved by collecting and examining a sufficient number of representative rock blocks (similar to those shown in Fig. 1) and a sufficient number of representative cores (samples similar to those shown in Fig. 2).

[0070] A high resolution 3D digital model (similar to the example shown in FIG. 3) was built using a set of high resolution 2D images (similar to those shown in FIG. 1). Using this high-resolution 3D digital model, its capacitance was estimated by determining the conditional minimum of the Helmholtz energy functional according to Eq. (7) together with the fulfillment of the conditions of Eq. (eight). To do this, the complete system of hydrodynamic equations DFH was solved numerically until an equilibrium solution was reached. The simulations took into account relevant phenomena such as flow over a wide range of Knudsen numbers, diffusional and convective transport, disjoining pressure, direct and reverse osmosis effects, capillary condensation, thin film precursors and anomalous rheology, contact angle hysteresis due to roughness and the number of capillaries, adsorption, accumulation and desorption of solid organic components. An example of the thus obtained thermodynamic equilibrium distribution of the components is shown in FIG. four.

[0071] The obtained information about the distribution of hydrocarbon components on a representative set of high resolution digital models, together with information obtained from a lower resolution 2D scan, as shown in FIG. 2 was used to build a set of larger-scale 3D models of representative core samples containing a distribution of heterogeneity features such as clusters dominated by hard grains, clusters dominated by liquid-filled pores, clusters dominated by gas-filled pores, clusters dominated by solid organics ( Fig. 5).

EXAMPLE 3: EOR/IOR DEVELOPMENT SCHEME

[0072] One of the important parts of planning the development of a hydrocarbon field is the evaluation and optimization of various schemes for applying enhanced oil recovery methods or improved development methods. This problem is currently being addressed by two complementary methods: (a) using detailed geologic and 3D reservoir modeling using commercial simulators to quantify reservoir processes, and (b) using physical coreflood tests in the laboratory. After a sufficient number of different development scenarios have been obtained, best by a combination of simulations and physical testing, the best option is determined as the optimal development solution.

[0073] However, this approach has several disadvantages.

[0074] First, there are many enhanced oil recovery (EOR) and enhanced oil recovery (IOR) technologies that involve complex physical and chemical processes occurring at the pore level. In the range of commercial simulators offered, reservoir modeling is based on the concept of macroscopic Darcy flow, which is not suitable for complex micro- and nanoscale phenomena. To overcome this difficulty, increasingly complex transport models for porous rocks are being developed; each such new model relies on additional phenomenological parameters that must be established on the basis of experimental data. However, for EOR and IOR technologies, experimental support for simulation models is limited by the fact that each experimental run permanently changes the experimental rock sample. This makes the validation of macroscopic simulations problematic.

[0075] Secondly, the practical effectiveness of EOR and IOR technologies depends on the fine-scale geological structure of the reservoir. In the case of a high degree of heterogeneity at the pore scale, reservoir models based on large-scale averaging of geological parameters are no longer applicable.

[0076] The present invention is a computational approach for quantifying and optimizing EOR/IOR development schemes in the case of tight heterogeneous reservoirs. This computational approach combines a digital rock representation approach with functional density modeling for porosity-scale processes.

[0077] It is well known that in tight, heterogeneous formations (e.g., shale) with nanosized pores, various phenomena can occur that affect the state and composition of the fluid (e.g., adsorption/absorption, osmosis, capillary condensation, disjoining pressure, and dispersion forces). All these phenomena are considered using the hydrodynamic characteristics of the multiphase composition using the density functional method. This approach provides a direct quantitative description of complex physical and chemical processes occurring on a pore scale, without the use of macroscopic phenomenological parameters.

[0078] The present invention describes EOR/IOR processes that use a combination of micro/nanoscale density functional modeling for representative rock samples and fluid transport modeling at the macro level in a statistical population of these rock samples.

[0079] Currently, reservoir simulators for oil and gas are based on a model of fluid transport in rocks called Darcy's law:

[0080]

представляет собой скорость переноса флюида,

- это вязкость при сдвиге,

- это симметричный тензор проницаемости,

- это плотность массы флюида,

- это гравитационный потенциал, и

- это частная производная относительно декартовых координат.[0081] where

is the fluid transfer rate,

is the shear viscosity,

is the symmetric permeability tensor,

is the mass density of the fluid,

is the gravitational potential, and

is the partial derivative with respect to Cartesian coordinates.

[0082] Expression (18) is applied to single-phase flow, while in multi-phase transfer, it is believed that there are separate phase transfer rates, each of which is described by a separate law with the same functional structure in equation (18).

[0083] When considering the transport of individual chemical components (such as water or methane) of the reservoir fluid, convective transport with the speed of the carrier (18) is combined with diffusion or dispersive transport. The resulting concentration stream is presented as follows [E.J. Peters, Petrophysics. University of Texas, 2007, pp. 5-23, 5-24]

[0084]

представляет собой поток концентрации для i-ого химического компонента,

- это концентрация этого компонента, и

- это матрица диффузии или дисперсии.where

represents the concentration flow for the i -th chemical component,

is the concentration of this component, and

is the diffusion or dispersion matrix.

[0085] The transport laws (18), (19) do not cover all transport phenomena that are observed in real tight geological formations and real EOR/IOR processes. Indeed, in tight rocks, osmotic effects can appear, such as an increase in the pressure gradient (i.e., direct osmosis, which contradicts (18)) or an increase in the concentration gradient (i.e., reverse osmosis, which contradicts (19)). Additional phenomena not taken into account in the transfer laws are electrokinetic transfer, absorption and diffusion transfer of hydrocarbons in the organic phase of the rock matrix.

[0086] Currently, many pore-scale EOR/IOR phenomena are clearly identified, but these phenomena are studied and modeled separately within specific models. This leads to the emergence of many different models that are quite adequate in themselves, but contradict each other when considered together [Othman, M. B., Jalan, S., Masoudi, R., & Mohd Shaharudin, M. S. B. (2013, July 2) . Chemical EOR: Challenges for Full Field Simulation. Society of Petroleum Engineers. doi: 10.2118/165247-MS].

[0087] This situation has conceptual difficulties in the case of highly heterogeneous reservoirs, when it is necessary to describe the EOR / IOR in some large reservoir block, while different physical and chemical effects appear in sharply different parts of this block.

[0088] Embodiments of the present invention overcome this conceptual difficulty by describing the EOR/IOR processes for representative rock samples directly using density functional modeling, while the subsequent fluid exchange between different rock samples is described using a set of transfer matrices.

[0089] Accordingly, a new procedure for quantifying and optimizing EOR/IOR technologies for tight heterogeneous reservoirs is proposed, which is based on the following steps.

относительно рассматриваемого коллектора или части такого коллектора. Если V _A представляет собой объем конкретной модели, а V _res - объем коллектора, то взвешенные значения нормализованы следующим образом[0090] First, the geological heterogeneity of the reservoir is studied using well log data and core samples with the identification of representative core samples. Such representative core samples are used to obtain 3D images of the porous body using, but not limited to, X-ray microtomography, 3D NMR imaging, 3D reconstruction based on petrographic analysis of thin sections and confocal microscopy, 3D reconstruction based on the analysis of 2D maps - elements obtained by scanning electron microscopy (SEM), focused ion beam 3D scanning electron microscopy (FIB-SEM) with the possible addition of an energy dispersive X-ray spectroscopy (EDX) function, etc .; digital processing and morphological analysis of three-dimensional core images by sequentially applying image filtering, segmentation and recognition of a number of properties. As a result of this step, a set of digital 3D models of porous rock samples is formed, with each model having a detailed pore distribution and mineralogical composition. In one embodiment, this set of digital rock models is interpreted as a statistical population, where each model is associated with its individual weight.

relative to the considered reservoir or part of such a reservoir. If V _A is the specific model volume and V _res is the reservoir volume, then the weighted values are normalized as follows

[0091]

[0092] Second, using the thermodynamic properties of the reservoir fluid and injected agents, digital 3D rock models and interaction potentials between constituent minerals and components of the fluid mixture, the total Helmholtz energy functional of the fluid in the rock is constructed in analytical form

[0093]

- молярные плотности химических компонентов флюида. В частности, явное выражение (21) применяют для получения аналитического выражения химических потенциалов составляющих компонентов

.[0094] In this expression, T is the absolute temperature, and

- molar densities of the chemical components of the fluid. In particular, the explicit expression (21) is used to obtain an analytical expression for the chemical potentials of the constituent components

.

[0095] Third , the 3D distribution of the chemical components of the fluid in the rock is numerically defined as the absolute conditional minimum of the density functional with restrictions regarding the total amount of each component in the 3D model

[0096]

whereN _i is the total amountith chemical component of the fluid, calculated in moles.

[0097] The proposed approximation of the quasi-equilibrium fluid distribution is valid when changes in the total number of components (22) occur relatively slowly. This is generally true for reservoir EOR/IOR processes according to Lake, L.W., Enhanced Oil Recovery, Prentice Hall, 1989. After performing the above minimization, chemical potential values can be calculated that are constant throughout the 3D model.

[0098]

и вызвана разницей в химических потенциалах[0099] Fourth , the rate of fluid component exchange between typical rock pieces is characterized by the component transfer matrix

and is caused by the difference in chemical potentials

[0100]

, может быть рассчитана численно с применением моделирования функционала плотности. То же уравнение (24) применяют для описания скорости обмена между образцом породы A и соседней средой B за пределами рассматриваемого коллектора или его части (например, нагнетательная скважина, добывающая скважина, водоносный горизонт и т. д.). К тому же, в этом последнем случае скорость обмена флюида вызвана разницей химических потенциалов между образцом породы A и соседней средой B, и матрица переноса

может быть рассчитана численно с применением моделирования функционала плотности.[0101] Transfer matrix at the boundary between two pieces of rock A and B,

, can be calculated numerically using density functional modeling. The same equation (24) is used to describe the rate of exchange between rock sample A and adjacent media B outside the considered reservoir or part of it (eg, injection well, production well, aquifer, etc.). Also, in this latter case, the fluid exchange rate is caused by the chemical potential difference between the rock sample A and the neighboring environment B, and the transfer matrix

can be calculated numerically using density functional modeling.

[0102] Fifth , for different EOR/IOR scenarios, the resulting dynamics of the total number of chemical components can be estimated using the exchange rate (24):

[0103]

[0104] In particular, this provides a quantitative assessment of hydrocarbon production and allows you to choose the best scenario.

[0105] In one embodiment, steps four and five can be replaced by a final step that evaluates primary recovery and recovery under various enhanced oil recovery scenarios by calculating dynamic total chemical components recovered for each 3D rock model considered, as well as selecting the optimal script to execute.

[0106] To demonstrate the application of the present invention, an EOR scenario was selected for a tight rock formation using the methodology contained in Eq. (20)-(25). Several representative core samples were studied to evaluate the geological heterogeneity of the reservoir. From these core samples, a set of representative rock blocks was extracted and interpreted as a statistical population, especially in accordance with the multiphase flow analysis mentioned above. Both representative rock blocks and representative core samples were used to simulate fluid distribution corresponding to various EOR application scenarios. Simulations were performed using density functional hydrodynamics (DFH). The total incremental production of chemical constituents related to the EOR scenarios was estimated using the initial quantities of constituents in accordance with Eq. (8), and the optimal EOR scenario was chosen, which provides the maximum additional production for useful components.

[0107] Using a set of high resolution 2D images like those shown in FIG. 1, built a statistical set of high-resolution 3D digital models; an example of one model from such a collection is shown in Fig. 3. Initial equilibrium distributions of chemical components described by Eq. (22) and (23) were simulated in pore models using DFH (Fig. 4). The description of the properties of the chemical components used in the DFH simulations was based on the Helmholtz energy functional according to Eq. (21). In addition, with a set of lower resolution images similar to those shown in FIG. 2 and information about the distribution of chemical components (i.e., similar to that shown in Fig. 4) obtained in the statistical set of high-resolution models, a statistical set of digital 3D models of a larger scale is built, containing features of heterogeneity; An example of such a model is shown in Fig. 5.

[0108] In the next step, primary production was numerically modeled taking into account the gas lift mechanism using both a statistical set of high resolution models and a statistical set of models of a larger scale. Modeling within separate high-resolution 3D models was carried out using pore-scale 3D numerical simulations, which took into account phenomena occurring in a multiphase composition, physical phenomena at the nanoscale level, and non-classical thermodynamic effects (non-equilibrium thermodynamics of small systems). Namely, numerical simulations were performed using DFH methods that take into account relevant phenomena such as flow over a wide range of Knudsen numbers, diffusion and convection transport, disjoining pressure, direct and reverse osmosis effects, capillary condensation, thin film precursors and anomalous rheology, contact hysteresis angle, due to the roughness and the number of capillaries, adsorption, accumulation and desorption of solid organic components. The results of numerical simulations obtained in this way ensured the minimization of the Helmholtz energy functional in Eq. (21) and the fulfillment of the necessary conditions according to Eq. (22) and (23) (Fig. 8A).

. Эти матрицы применяли в численных имитациях первичного извлечения на статистической совокупности моделей более крупного масштаба. Численные имитации на моделях более крупного масштаба осуществляли путем численного решения Ур. (24) (Фиг. 9A).[0109] From the results of numerical simulations on a statistical population of high-resolution 3D digital models, transfer matrices were extracted

. These matrices were used in numerical simulations of primary extraction on a larger scale statistical population of models. Numerical simulations on larger scale models were performed by numerically solving Eq. (24) (Fig. 9A).

[0110] After simulating the primary extraction process, a number of EOR scenarios were simulated (FIGS. 8B and 9B) using the same methodology that was used to simulate the primary extraction. The differences between the individual EOR scenarios were in the type, amount and method of application of the enhanced oil recovery agent (EOR), as well as in the physical mechanism of interaction of this agent with fluids and rock. The description of the EOR agent was based on the Helmholtz energy functional according to Eq. (21).

[0111] In the final step, primary recovery scenarios and individual EOR scenarios were evaluated by calculating the dynamic performance of all recovered chemical components using Eq. (25) (Fig. 10). Based on this information, the optimal EOR scenario was selected. Then, based on the chosen scenario, optimal operations for primary extraction and application of EOR methods can be performed.

[0112] The foregoing description contains illustrations and explanations, but is not intended to be exhaustive or to limit the inventive ideas to the exact form set forth. In light of the foregoing ideas, modifications and variations are possible, which can also be obtained as a result of the practical application of the techniques described in the present description.

[0113] Even though specific combinations of features are set forth in the claims and/or the description discloses, these combinations are not intended to limit the present invention. In fact, many of these features may be combined in ways that are not specifically stated in the claims and/or disclosed in the specification. Although each dependent claim below may directly depend on only one other claim, the description includes each dependent claim in combination with every other claim.

[0114] No element, act, or instruction used in this application should be considered critical or essential to the invention unless they are expressly described as such outside of the preferred embodiment. In addition, the phrase "based on" is intended to mean "based at least in part on" unless explicitly stated otherwise.

Claims (59)

1. A method for evaluating fluids in a tight hydrocarbon reservoir within a heterogeneous geological formation or section thereof, the method comprising: a) obtaining physical parameters of fluids and formation; b) building at least one three-dimensional (3D) model of a dense hydrocarbon reservoir using physical parameters, and such a three-dimensional model contains a simulation of the pore structure and mineralogical composition; c) calculating the amount of hydrocarbon for each specified 3D model in step b); d) calculation of total hydrocarbon reserves; and e) creation of a development plan based on estimated total hydrocarbon reserves. 2. The method of claim 1, wherein the physical parameters of the fluids are obtained from fluid probes located within the hydrocarbon reservoir. 3. The method according to claim 1, characterized in that the physical parameters of the formation are obtained from formation samples. 4. The method according to claim 1, characterized in that in step c) the amount of hydrocarbon is obtained by calculating the conditional minimization of the Helmholtz energy functional. 5. The method according to claim 4, characterized in that 3D models are created on the basis of an analytical expression for the density of the Helmholtz energy functional. 6. The method according to p. 4, characterized in that the Helmholtz energy function in a dense hydrocarbon reservoir has the form:

, where T is the absolute temperature of the dense hydrocarbon reservoir; n _i are the molar densities of the chemical components; and the subscript is the serial number of a particular chemical component, and, accordingly, the chemical potentials κ _i are calculated using the following analytical expression:

. 7. The method according to p. 6, characterized in that the functional of the total Helmholtz energy in a dense hydrocarbon reservoir has the form

, where T is the absolute temperature of the dense hydrocarbon reservoir; n _i are the molar densities of the chemical components, and the chemical potentials κ _i are calculated using the following analytical expression:

. 8. The method according to p. 1, characterized in that the average molar densities

first calculated in step d) using the formula:

, where the integration is carried out over a 3D model M(k) with a total volume V(k) , with subsequent assignment of the 3D fluid distribution to gas and oil to obtain average densities using the formulas:

. 9. The method according to p. 8, characterized in that the total amount of hydrocarbon reserves in molesB _i chemical component with serial numberi in a dense hydrocarbon reservoir is calculated as follows:

, where V is the volume of a tight hydrocarbon reservoir,

is the frequency or probability of a particular 3D model M(k) in a tight hydrocarbon reservoir. 10. The method according to claim 9, characterized in that the total amount of hydrocarbon reserves B _i is converted into mass units by multiplying the molar mass of the component m _i :

. 11. The method of claim 1, wherein in step d) the total amount of hydrocarbon reserves is calculated using the total volume of the reservoir, the calculated quantities for the 3D models M(k) and their relative frequency of occurrence in the reservoir or part of the reservoir. 12. The method of claim 1 wherein these steps are performed on a plurality of 3D models that collectively form part of a tight hydrocarbon reservoir. 13. The method according to claim. 1, characterized in that these steps are performed for a plurality of deposits in order to estimate the total reserves by adding the total hydrocarbon reserves of each of the deposits. 14. A method for evaluating and optimizing enhanced oil recovery technologies or improved development methods for tight hydrocarbon reservoirs, including: a) building 3D rock models for porous rock using physical properties and 3D images of the porous body for representative core samples from tight hydrocarbon reservoirs, the 3D rock model containing pore distribution and mineralogical composition; b) building a common fluid component in 3D rock models in step a); c) calculation of 3D distribution of fluid components in 3D rock models; d) calculating the rate of exchange of fluid components between 3D rock models; e) calculation of dynamic indicators of the total number of fluid components in a tight hydrocarbon reservoir; and f) creation of a development plan based on calculated dynamic performance. 15. The method according to claim 14, characterized in that in step a) each of at least one three-dimensional rock model is associated with a weight index

for the collector, and the weights

normalized according to:

, where V _A is the volume of one particular 3D rock model and V _res is the volume of the reservoir. 16. The method according to claim 14, characterized in that in step b) the total fluid mixture is described using the total Helmholtz energy functional described by the formula:

, where T is the absolute temperature of the dense hydrocarbon reservoir and n _i are the molar densities of the fluid components. 17. The method according to claim 16, characterized in that the chemical potentials κ _i of the fluid components are calculated using the following expression:

. 18. The method according to claim 14, characterized in that the total amount of fluid components in the three-dimensional rock model is calculated using the following expression:

, where N _i is the total amount of one fluid component in a tight hydrocarbon reservoir. 19. The method according to p. 18, characterized in that the exchange rate

calculated using the following expression:

, where

is the fluid component transfer matrix, and

is the difference in chemical potentials. 20. The method according to claim 19, characterized in that the total amount of fluid components is calculated using the following expression:

, where N _{i tot} is the total amount of one fluid component in a tight hydrocarbon reservoir.

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