CN-121007003-B - Quantitative prediction method for fluid saturation of tight oil reservoir based on acoustic-electric combined model

Abstract

The invention discloses a quantitative prediction method for fluid saturation of a tight oil reservoir based on an acoustic-electric combined model, and particularly relates to the technical field of tight oil reservoir prediction, wherein a tight oil elasticity and electric petrophysical model with the same microstructure is respectively constructed by utilizing an H-S limit equation, an elasticity and electric differential effective medium theory and a Gurevich jet flow model. And finally, constructing a three-dimensional petrophysical model suitable for the tight oil reservoir by combining the rock elasticity and the electrical response, extracting the logging data correction combined model of the actual stratum, and applying the model to the tight oil reservoir.

Inventors

• PANG MENGQIANG
• BA JING
• ZHANG TING
• ZHANG LIN
• LUO CONG

Assignees

• 河海大学

Dates

Publication Date

20260512

Application Date

20250805

Claims (2)

1. 1. The quantitative prediction method for the fluid saturation of the tight oil reservoir based on the acoustic-electric combined model is characterized by analyzing the mineral distribution of the rock according to the analysis result of a rock core scanning electron microscope of the tight oil reservoir of the rock and calculating the matrix elastic modulus and the matrix conductivity of the mineral mixture after clay minerals are removed by utilizing an elastic HS boundary equation; Adopting a DEM model to respectively add pores and cracks as hard holes and soft holes into a rock matrix to obtain a rock skeleton model containing inclusion, and calculating the elastic modulus of the rock skeleton model containing the inclusion; Then, a DEM model is adopted to add clay minerals into the rock skeleton model containing the inclusion as clay ellipsoids, at the moment, the rock skeleton model containing pores, cracks and clay content is obtained, and the elasticity modulus of the rock skeleton model containing different clay contents is calculated; Simulating jet flow action under any saturation by using Gurvich model, calculating improved bulk modulus and shear modulus containing jet flow effect based on the obtained rock skeleton model, and obtaining compact oil rock elasticity model based on the obtained wave response characteristics of the partially saturated rock; After the compact oil rock elastic model is built, an electric rock physical model with the same pore structure and pore fluid is built, a mineral mixture is used as a matrix, the electric conductivity of the mineral mixture is given by utilizing an electric HS boundary equation, a rock skeleton model containing inclusion is obtained by utilizing an electric differential effective medium model, the electric conductivity of the rock skeleton model containing inclusion is calculated, clay minerals are added into the rock skeleton model containing inclusion as clay ellipsoids to obtain a compact oil electric rock physical model, and the electric conductivities of the compact oil electric rock physical models containing different clay contents are calculated; Constructing an acoustic-electric combined model by combining the elasticity and the electric response of the rock, calibrating and correcting the acoustic-electric combined model through logging data, and applying the acoustic-electric combined model to an actual tight oil reservoir so as to predict the fluid saturation of the actual tight oil reservoir; Wherein the bulk modulus contains an improvement in jet effect Shear modulus Is calculated as follows: ; ; In the formula, In order to be of an angular frequency, As a function of the viscosity of the fluid, 、 The content and aspect ratio of the microscopic micro-pores respectively, A bulk modulus of the skeleton that is only hard pores in the rock; And Bulk modulus and shear modulus of the rock skeleton model; longitudinal wave velocity of partially saturated rock And transverse wave velocity Calculation based on bulk modulus and shear modulus: ; = ; ; ; ; Wherein, the 、 And The bulk modulus, the shear modulus and the density of the partially saturated rock are respectively, And The water-containing skeleton bulk modulus and the oil-containing skeleton bulk modulus, To add the skeletal shear modulus of the pore, fissure and clay minerals, In order to achieve a degree of porosity, the porous material, For the content of the mud material, the mud material is prepared, For the density of the matrix material, In order to mix the densities of the fluids, And obtaining the compact oil rock elasticity model based on the obtained wave response characteristics of the partially saturated rock.
2. 2. The quantitative prediction method for the fluid saturation of the tight oil reservoir based on the acoustic-electric combined model, which is disclosed in claim 1, is characterized in that the tight oil electrical petrophysical model is designed as follows: ; Wherein, the To add the conductivity of phase 2, the initial conditions are (e=0)= ; Conductivity for phase 1; E is the content of phase 2; is composed of phase 2 depolarization factor (P=1, 2, 3); ; Wherein, the Is a depolarization factor associated with phase 2 shape, taking into account aspect ratio An ellipsoidal body of < 1; ; according to the Archie formula, the conductivity of pores and fissures is a function of the water saturation: ; Wherein, the For the conductivity of the brine to be high, Water saturation for rock; conductivity for pores or fissures; is a saturation index; Is the lithology coefficient.

Description

Quantitative prediction method for fluid saturation of tight oil reservoir based on acoustic-electric combined model Technical Field The invention relates to the technical field of tight oil reservoir prediction, in particular to a tight oil reservoir fluid saturation quantitative prediction method based on an acoustic-electric combined model. Background Compact oil refers to oil enriched in non-shale such as clastic rock or carbonate rock reservoirs, and the overpressure permeability of the compact oil is less than 0.1 multiplied by 10 -3μm2. The development of compact oil can obviously increase the supply quantity of petroleum resources, thereby effectively relieving the energy supply pressure. Compared with the traditional oil and gas resources, the compact oil reservoir has the characteristics of low porosity, poor permeability, complex mineral components, high clay content, poor oil-water separation and the like, and greatly influences the flow of reservoir fluid and the petrophysical characteristics. Because tight oil reservoirs have complex mineral compositions and fluid characteristics, conventional geophysical exploration methods have limitations in terms of fine reservoir characterization and fluid identification, which make it difficult to achieve efficient reservoir evaluation. Fluid saturation is considered to be one of the key parameters for tight oil reservoir evaluation. Fluid saturation is typically related to geophysical parameters by theoretical models or experimental empirical formulas so that fluid saturation may be estimated based on measured geophysical data. A common model is the Archie's formula, which establishes a mathematical relationship between rock resistivity and fluid saturation. By acquiring the elastic parameter sensitive to the fluid saturation, the quantitative relation between the elastic parameter and the fluid saturation is established, and the accuracy of fluid saturation prediction is improved by utilizing the fluid factor index. A quantitative prediction method for fluid saturation based on logging data and pre-stack seismic inversion parameters, namely a pore volume modulus method, establishes a relation between water saturation and pore volume modulus. The reservoir original stratum fluid model, the drilling fluid invasion model and the mathematical model of the well logging response provide a corresponding fluid saturation calculation method and a reservoir fluid-containing property discrimination method. With the introduction of Nuclear Magnetic Resonance (NMR) logging techniques, differential spectroscopy and shift spectroscopy have been widely used to identify reservoir fluid properties. The microcosmic existence state of the stratum length 7-section compact oil is quantitatively analyzed by utilizing nuclear magnetic resonance and micron-nanometer CT scanning technology, and the relation between the compact oil content and the initial water saturation of the reservoir, the clay mineral content and the pore structure is revealed. Clay mineral content can be a key controlling factor affecting the physical properties of tight reservoirs and has a significant impact on porosity, permeability, pore throat type and size distribution. An increase in clay mineral content results in reduced pore throat connectivity and, in turn, reduced movable fluid saturation. Because clay minerals have higher electrical conductivity, the overall electrical conductivity of the reservoir increases with increasing clay mineral content. Considering clay content and type helps to improve the accuracy of the inversion, thereby more accurately estimating fluid saturation and porosity. In recent years, with the continuous deep research of the elasticity and the electrical characteristics of the reservoir, the acoustic-electric coupling rock physical model not only can provide complementary information, but also can obviously improve the accuracy of the reservoir characterization result. The physical properties of rock such as elasticity and electrical property of reservoir rock are closely related to pore structure, fluid distribution, pressure and saturation of the reservoir. The reservoir elastic parameters are limited in sensitivity in oil-water distinction, and the fluid identification capacity has large uncertainty. Tight oil reservoirs have complex lithology characteristics and difficult oil-water differentiation, and oil-water differentiation is difficult only by virtue of elastic properties. Disclosure of Invention In contrast, the electrical parameters are more sensitive to the fluid type and can reflect the oil-water distribution characteristics more effectively. The consistency of the rock microstructure must be maintained when characterizing the elastic and electrical models in combination. And by adopting a three-dimensional petrophysical modeling method, parameters such as poisson ratio, longitudinal wave impedance, resistivity and the like of different rock-fluid combinatio