1. A method for converting resistivity index and relative permeability of a argillaceous sandstone reservoir is characterized by comprising the following steps:

s1, establishing an improved W-S model of the argillaceous sandstone reservoir according to the rock-electricity experimental data, CEC data and an Archie formula;

s2, establishing a resistivity increase coefficient I for eliminating the influence of additional conductivity of the mud according to the improved W-S model and the Archie formula^*；

S3, introducing a correction term of relative permeability of a wetting phase based on an original conversion model of resistivity index and relative permeability, and establishing a conversion model of the resistivity index and the relative permeability of the argillaceous sandstone reservoir;

and S4, giving an application flow in the well, and completing logging processing and productivity prediction work by utilizing the established argillaceous sandstone reservoir resistivity index and relative permeability conversion model.

2. The method for converting resistivity index and relative permeability of the argillaceous sandstone reservoir according to claim 1, wherein the method comprises the following steps: in the S1, an improved argillaceous sandstone reservoir W-S model is established based on the rock-electricity experimental data and CEC data, and the improved argillaceous sandstone reservoir W-S model formula is as follows:

wherein, F^*Is a formation factor of pure sandstone with porosity equal to the total porosity of the dispersed argillaceous sandstone, S_wtIs the stratum water saturation, and n is the water saturation index; b is cation exchange capacity conductivity; q_vIs the cation exchange capacity per unit pore volume and represents the number of moles of exchangeable cations contained in the rock sample per unit total pore volume in mol/L, C_wIs the formation water conductivity.

3. The method for converting resistivity index and relative permeability of the argillaceous sandstone reservoir according to claim 1, wherein the method comprises the following steps: in S2, the resistivity increase coefficient I for eliminating the influence of additional conductivity of mud^*Comprises the following steps:

wherein, C₀Conductivity of pure rock at 100% saturation, C_tIs the conductivity of the hydrocarbon-bearing formation, C_0sdConductivity of pure sandstone fractions, C_0shIs the electrical conductivity of the argillaceous part, C_tsdIs the conductivity of sandstone parts of hydrocarbon-bearing formations, C_tshIs the conductivity of the argillaceous portion of the hydrocarbon-bearing formation.

4. The method for converting resistivity index and relative permeability of the argillaceous sandstone reservoir according to claim 3, wherein the method comprises the following steps: in the step S3, the influence of mud on the conductivity law of the rock is considered in the original conversion model of resistivity index and relative permeability; based on the method, the resistivity increasing coefficient of pure rock is changed into the resistivity increasing coefficient of the argillaceous sandstone, the water saturation index is changed, and the empirical coefficient S is added^f _wComprises the following steps:

finally determining f to be 1 through formula fitting and calculation of average absolute error, and introducing the resistivity increase coefficient I obtained in the step S2 for eliminating the influence of additional conductivity of the argillaceous substance^*Establishing a conversion model of the resistivity index and the relative permeability of the argillaceous sandstone reservoir:

wherein, K_rwIs the relative permeability of the aqueous phase, S^* _WIs the water saturation, S_wIs the water saturation of the formation, I^*The resistivity increase coefficient for eliminating the influence of additional conductivity of the argillaceous substance.

5. The method for converting resistivity index and relative permeability of the argillaceous sandstone reservoir according to claim 1, wherein the method comprises the following steps: in S4, the formula for the production performance prediction is as follows;

the crude oil yield is calculated as:

wherein, P_eIs the formation static pressure (MPa); p_wIs the wellbore pressure; mu.s_oViscosity of the oil (mPas), h is effective thickness, K_oEffective permeability of the oil phase;

ko needs to be derived from the relative permeability of the oil phase and the absolute permeability of the oil phase:

K_o＝K_ro·K

wherein, K_roThe relative permeability of the oil phase, K is the absolute permeability of the oil phase, and general well logging data can be given;

the formula for calculating the water yield is as follows:

wherein, P_eIs the formation static pressure (MPa); p_wIs the wellbore pressure; mu.s_wViscosity of water (mPas), h is effective thickness, K_wEffective permeability of the aqueous phase;

K_wthe relative permeability of the oil phase is required to be determinedThe absolute permeability of the oil phase can be obtained:

K_w＝K_nv·K

wherein, K_rwIs the relative permeability of the aqueous phase; k is the absolute permeability of the water phase, and general well logging data can be given;

the model establishment, correction and equation calculation in S1, S2, S3 and S4 are implemented using a computer system.

Background

Relative permeability is one of the important parameters for reservoir interpretation and production prediction. The methods for obtaining relative permeability in the laboratory are mainly steady-state method and unsteady-state method, but the preparation cost is relatively expensive and the preparation time is long. Many researchers have established various transformation models to make the calculated relative permeability of fluids closer to the true measurement, and most typically the method of calculating the relative permeability of the wetting and non-wetting phases based on the resistivity increase factor.

Workers introduce wettability phase tortuosity based on Poiseuille law and Darcy law, and establish a new relative permeability model, compared with the original model, the experimental measurement result of the model is closer to the experimental measurement value, however, the model does not consider the pore structure of the rock core. The influence of the pore structure on the tortuosity of the wetting phase is considered by workers, the influence of the pore structure is determined through capillary pressure curve analysis, and the relation between the tortuosity of the wetting phase and the resistivity index is researched, so that the problem that the tortuosity is difficult to obtain in an I-Kr model is solved.

The invention patent with publication number CN 105021506A discloses a three-phase relative permeability calculation method based on a pore network model, which is based on the construction of the pore network model, reproduces the underground rock pore structure characteristics to simulate the flow of fluid, and utilizes an orthogonal design method, and utilizes the orthogonal design method to select the best design parameters in the process so as to predict the relative permeability of three-phase fluid.

However, the two methods have good application effects in pure sandstone reservoirs, and the influence of the additional conductivity of clay on the experimental results is not considered in the argillaceous sandstone reservoirs, so that a new calculation method for logging relative permeability of the argillaceous sandstone reservoirs needs to be established, and accordingly, productivity prediction and reservoir interpretation evaluation are performed.

Disclosure of Invention

Technical problem to be solved

The invention aims to solve the problems, and provides a conversion method of resistivity index and relative permeability of a argillaceous sandstone reservoir, so as to solve the problems that in the prior art, the application effect is better in a pure sandstone reservoir, and the influence of additional conductivity of clay on an experimental result is not considered in the argillaceous sandstone reservoir.

(II) technical scheme

In order to achieve the purpose, the invention is realized by the following technical scheme: a method for converting resistivity index and relative permeability of a argillaceous sandstone reservoir comprises the following steps:

s1, establishing an improved W-S model of the argillaceous sandstone reservoir according to the rock-electricity experimental data, CEC data and an Archie formula;

s2, establishing a resistivity increase coefficient I for eliminating the influence of additional conductivity of the mud according to the improved W-S model and the Archie formula^*；

S3, introducing a correction term of relative permeability of a wetting phase based on an original conversion model of resistivity index and relative permeability, and establishing a conversion model of the resistivity index and the relative permeability of the argillaceous sandstone reservoir;

and S4, giving an application flow in the well, and completing logging processing and productivity prediction work by utilizing the established argillaceous sandstone reservoir resistivity index and relative permeability conversion model.

Preferably, in S1, based on the petroelectricity experimental data and the CEC data, an improved W-S model of the argillaceous sandstone reservoir is established, and the formula of the improved W-S model of the argillaceous sandstone reservoir is as follows:

wherein, F^*Is a formation factor of pure sandstone with porosity equal to the total porosity of the dispersed argillaceous sandstone, S_wtIs the stratum water saturation, and n is the water saturation index; b is cation exchange capacity conductivity; q_vIs the cation exchange capacity per unit pore volume and represents the number of moles of exchangeable cations contained in the rock sample per unit total pore volume in mol/L, C_wIs the formation water conductivity.

Preferably, in S2, the resistivity increase coefficient I for eliminating the influence of the additional conductivity of the mud is^*Comprises the following steps:

wherein, C₀Conductivity of pure rock at 100% saturation, C_tIs the conductivity of the hydrocarbon-bearing formation, C_0sdConductivity of pure sandstone fractions, C_0shIs the electrical conductivity of the argillaceous part, C_tsdIs the conductivity of sandstone parts of hydrocarbon-bearing formations, C_tshIs the conductivity of the argillaceous portion of the hydrocarbon-bearing formation.

Preferably, in S3, in the original conversion model of resistivity index and relative permeability, the influence of mud on the rock conduction law is considered; based on the method, the resistivity increasing coefficient of pure rock is changed into the resistivity increasing coefficient of the argillaceous sandstone, the water saturation index is changed, and the empirical coefficient S is added^f _wComprises the following steps:

finally determining f to be 1 through formula fitting and calculation of average absolute error, and introducing the resistivity increase coefficient I obtained in the step S2 for eliminating the influence of additional conductivity of the argillaceous sand reservoir to establish a conversion model of resistivity index and relative permeability of the argillaceous sand reservoir:

wherein, K_rwRelative permeability of the aqueous phase, S_WIs the water saturation, S_wAnd I is an increasing resistivity coefficient for eliminating the influence of additional conductivity of the argillaceous material.

Preferably, in S4, the formula for generating the prediction is as follows;

the crude oil yield is calculated as:

wherein, P_eIs the formation static pressure (MPa); p_wIs the wellbore pressure; mu.s_oViscosity of the oil (mPas), h is effective thickness, K_oEffective permeability of the oil phase;

K_othe relative permeability of the oil phase and the absolute permeability of the oil phase are required to obtain:

K_o＝K_ro·K

wherein, K_roThe relative permeability of the oil phase, K is the absolute permeability of the oil phase, and general well logging data can be given;

the formula for calculating the water yield is as follows:

wherein, P_eIs the formation static pressure (MPa); p_wIs the wellbore pressure; mu.s_wViscosity of water (mPas), h is effective thickness, K_wEffective permeability of the aqueous phase;

K_wthe relative permeability of the oil phase and the absolute permeability of the oil phase are required to obtain:

K_w＝K_nv·K

wherein, K_rwIs the relative permeability of the aqueous phase; k is the absolute permeability of the water phase, and general well logging data can be given;

the model establishment, correction and equation calculation in S1, S2, S3 and S4 are implemented using a computer system.

The invention provides a method for converting resistivity index and relative permeability of a argillaceous sandstone reservoir, which has the following beneficial effects:

1. according to the conversion method of the resistivity index and the relative permeability of the argillaceous sandstone reservoir, the influence of additional conductivity of argillaceous sandstone in the argillaceous sandstone reservoir is considered, the correction term of the relative permeability of the wetting phase is introduced, and a new conversion model of the resistivity increase rate and the relative permeability of the argillaceous sandstone reservoir is successfully established, so that the calculated relative permeability of the fluid is closer to a real measured value, the application effect of the conversion method in the argillaceous sandstone reservoir is better, and the defect that the research on the relative permeability of the argillaceous sandstone reservoir is rarely performed in the previous research is overcome.

2. The conversion method of the resistivity index and the relative permeability of the argillaceous sandstone reservoir establishes a working process applied in a well, can well perform evaluation and capacity prediction of the argillaceous sandstone reservoir, provides reference for capacity prediction of oil fields in other areas, can be practically applied, and solves the problems that at present, many theoretical researches on interpretation and evaluation of oil and gas reservoirs are needed, but a well logging interpretation process is lacked.

3. The conversion method of the resistivity index and the relative permeability of the argillaceous sandstone reservoir applies the new model in the well, and obtains more reliable relative permeability and well logging interpretation evaluation results of the argillaceous sandstone reservoir. The model experiment has good effect, and is more suitable for researching the permeability and the conductivity of the argillaceous sandstone.

Drawings

FIG. 1 shows laboratory measurements C_tImproved W-S model calculation C_tComparison of values.

FIG. 2 is a schematic diagram of a phase permeation measurement curve;

FIG. 3 is a graph comparing laboratory measurements with various model calculations of relative water permeability results;

FIG. 4 is a graph comparing results of laboratory measurements and various model calculations of relative oil permeability;

FIG. 5 is a flow chart of an in-well application;

FIG. 6 is a diagram of the comprehensive well logging interpretation effect;

FIG. 7 is a block flow diagram of the method of the present invention.

Detailed Description

The objects, technical solutions and advantageous effects of the present invention will be described in further detail below.

It is to be noted that the following detailed description is exemplary and is intended to provide further explanation of the claimed invention, and unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

The embodiment of the invention provides a method for converting resistivity index and relative permeability of a argillaceous sandstone reservoir.

Referring to fig. 1, fig. 2, fig. 3, fig. 4, fig. 5, fig. 6 and fig. 7, the method includes the following steps: s1, establishing an improved argillaceous sandstone reservoir W-S model according to the rock-electricity experimental data, CEC data and an Archie formula, and establishing an improved argillaceous sandstone reservoir W-S model based on the rock-electricity experimental data and the CEC data in S1, wherein the improved argillaceous sandstone reservoir W-S model formula is as follows:

wherein, F^*Is a formation factor of pure sandstone with porosity equal to the total porosity of the dispersed argillaceous sandstone, S_wtIs the stratum water saturation, and n is the water saturation index; b is cation exchange capacity conductivity; q_vIs the cation exchange capacity per unit pore volume and represents the number of moles of exchangeable cations contained in the rock sample per unit total pore volume in mol/L, C_wIs the formation water conductivity;

s2, establishing a resistivity increase coefficient I for eliminating the influence of additional conductivity of the mud according to the improved W-S model and the Archie formula^*And (S2) a resistivity increase coefficient I for eliminating the influence of additional conductivity due to sludge^*Comprises the following steps:

wherein, C₀Conductivity of pure rock at 100% saturation, C_tIs the conductivity of the hydrocarbon-bearing formation, C_0sdConductivity of pure sandstone fractions, C_0shIs the electrical conductivity of the argillaceous part, C_tsdIs the conductivity of sandstone parts of hydrocarbon-bearing formations, C_tshConductivity of the muddy part of the hydrocarbon-bearing formation;

s3, introducing a correction term of relative permeability of a wetting phase based on the original conversion model of the resistivity index and the relative permeability, and establishing the argillaceous sandstone reservoirIn the conversion model of the resistivity index and the relative permeability, in S3, the influence of mud on the conductivity law of the rock is considered in the original conversion model of the resistivity index and the relative permeability; based on the method, the resistivity increasing coefficient of pure rock is changed into the resistivity increasing coefficient of the argillaceous sandstone, the water saturation index is changed, and the empirical coefficient S is added^f _wComprises the following steps:

finally determining f to be 1 through formula fitting and calculation of average absolute error, and introducing the resistivity increase coefficient I obtained in S2 for eliminating the influence of additional conductivity of the argillaceous substance^*Establishing a conversion model of the resistivity index and the relative permeability of the argillaceous sandstone reservoir:

wherein, K_rwIs the relative permeability of the aqueous phase, S^* _WIs the water saturation, S_wIs the water saturation of the formation, I^*The resistivity increase coefficient for eliminating the influence of additional conductivity of the argillaceous substance;

s4, giving an application flow in a well, and completing logging processing and capacity prediction work by utilizing the established resistivity index and relative permeability conversion model of the argillaceous sandstone reservoir, wherein in S4, a formula for capacity prediction is as follows;

the crude oil yield is calculated as:

wherein, P_eIs the formation static pressure (MPa); p_wIs the wellbore pressure; mu.s_oViscosity of the oil (mPas), h is effective thickness, K_oEffective permeability of the oil phase;

ko needs to be derived from the relative permeability of the oil phase and the absolute permeability of the oil phase:

K_o＝K_ro·K

wherein, K_roThe relative permeability of the oil phase, K is the absolute permeability of the oil phase, and general well logging data can be given;

the formula for calculating the water yield is as follows:

wherein, P_eIs the formation static pressure (MPa); p_wIs the wellbore pressure; mu.s_wViscosity of water (mPas), h is effective thickness, K_wEffective permeability of the aqueous phase;

K_wthe relative permeability of the oil phase and the absolute permeability of the oil phase are required to obtain:

K_w＝K_nv·K

wherein, K_rwIs the relative permeability of the aqueous phase; k is the absolute permeability of the water phase, and general well logging data can be given;

the model establishment, correction and equation calculation in S1, S2, S3 and S4 are implemented using a computer system.

The specific implementation mode is as follows:

firstly, an improved W-S model of the argillaceous sandstone reservoir is established based on rock-electricity experimental data, CEC data and an Archie formula.

1.1, introducing an original W-S model.

Waxman and Smits (1968) based on the experimental results, a argillaceous sandstone conductivity model based on the conductivity of the clay mineral surface charged layer is given:

wherein F is a formation factor of pure sandstone with porosity equal to the total porosity of the dispersed argillaceous sandstone; b is cation exchange capacity conductivity; q_vIs the cation exchange capacity per unit pore volume and represents the number of moles of exchangeable cations contained in the rock sample per unit total pore volumeThe unit is mol/L, C_wIs the formation water conductivity. C0 is the conductivity of pure rock at 100% saturation.

CEC is typically applied to experimental measurements, while Qv is used for well log interpretation. The relationship between the two is:

in the formula: rho_gRock average particle density (g/cm 3); phi is a_tIs the total porosity of the argillaceous sandstone. Cation Exchange Capacity (CEC) is primarily the capacity of a substance to exchange cations with solutions containing other cations and represents the amount of exchangeable cations contained per unit mass of dry rock sample, usually expressed in the laboratory as millimoles of exchangeable cations per 100g of dry rock sample. CEC size depends on clay type, distribution and volume. CEC directly affects formation conductivity, and therefore, the effects of clay factors need to be considered when calculating formation conductivity.

B＝3.83(0.83-0.6e^-0.5Cw) (25℃)

Wherein C is_wIs the formation water conductivity.

The predecessors were correcting for the effect of clay in calculating the hydrocarbon saturation of the formation, not based on the clay volume content, but rather on the cation exchange capacity of the clay. Mudstone is more conductive than adjacent sandstone because of the additional conductivity of clay. For hydrocarbon-containing argillaceous sandstone, oil gas enters into the pore space to replace a part of free water, the exchange cations related to clay are more concentrated in the rest water, and the concentration of the exchange cations in the pore water is increased, so that the effective capacity of hydrocarbon-containing argillaceous sandstone cation exchange is increased, and therefore, a W-S model is established:

wherein, F^*Is a formation factor of pure sandstone with porosity equal to the total porosity of the dispersed argillaceous sandstone, S_wtIs the water saturation of the formation and n is the water saturation index. B is cation exchange capacity conductivity; q_vIs the cation exchange capacity per unit pore volume and represents the number of moles of exchangeable cations contained in the rock sample per unit total pore volume in mol/L, C_wIs the formation water conductivity. Q_vCation exchange capacity per unit pore volume.

1.2, improved W-S model.

The experiment will be measured herein as C_tCalculation with W-S model C_tThe comparison shows that the conductivity of the hydrocarbon-bearing formation obtained by the W-S model is greater than the measured value (FIG. 1). Therefore, the W-S model of the oil-gas-containing stratum is improved.

In the original W-S model, the effective capacity of cation exchange of hydrocarbon-containing dispersed argillaceous sandstone is assumed to be equal to the effective capacity Q of cation exchange when the rock is completely water-containing_vAnd total water saturation S_wtIn connection with, namely:

wherein Q_vCation exchange capacity per unit pore volume, S_wtThe formation water saturation.

For a argillaceous sandstone reservoir with strong additional conductivity of clay mineral cations, the conductivity of the argillaceous sandstone is higher than that of adjacent sandstone due to the strong additional conductivity of the clay. For the oil-gas-containing argillaceous sandstone, oil gas enters a pore space to replace a part of free water, the exchange cations related to clay are more concentrated in the rest water, and the concentration of the exchange cations in the pore water is increased, so that the effective capacity of cation exchange of the oil-gas-containing argillaceous sandstone is increased, and the conductivity of the oil-gas-containing stratum calculated by a W-S model is obviously larger than the measured value.

While in the actual formation interpretation, the effective capacity Q of the shale sandstone cation exchange_vAnd total water saturation S_wtThe relation between the effective capacities of the hydrocarbon-containing argillaceous sandstone cations calculated by the W-S model is further consideredLarge, and the ratio definition of the formula I does not conform to the above formula I when comparing the formula (1) with the formula (4). I in the alder formula is defined as:

based on the Archie's formula, in combination with the theory of actual formation interpretation, in a argillaceous sandstone reservoir, the present document gives an improvement in the conductivity of a hydrocarbon-bearing formation:

FIG. 1 shows 6 laboratory measurements C_tCalculation of value and W-S model before and after improvement C_tThe values compare the plots. Table 1 lists the relevant parameters for each rock sample. The figure shows that the fitting curve of the rock sample has better consistency with the calculation result of the improved W-S model, which shows that the improved W-S resistivity model can better describe the conduction law of the hydrocarbon-bearing argillaceous sandstone stratum

TABLE 1 petrographic parameters and CEC data of rock samples

Secondly, establishing a resistivity increasing coefficient I for eliminating the influence of additional conductivity of the argillaceous mass according to an improved W-S model and an Archie formula^*。

For the argillaceous sandstone stratum, the stratum conductivity and the stratum water conductivity no longer meet the simple Archie rule due to the influence of argillaceous quality, and the additional conductivity C caused by argillaceous quality at low mineralization degree_exWith C_wIncreases with C, resulting in formation conductivity with C_wIs increased sharply; additional conductivity C due to argillaceous substances when the degree of mineralization is high_exNot following C_wChange of formation conductivity with C_wIs increased linearly. Therefore, for a argillaceous formation, the influence of mud on the electrical conduction law of the rock must be considered. W-S modelThe conductivity equation is divided into two parts, one part is the conductivity of the clay component, and the other part is the conductivity of the electrolyte:

wherein, C_wAs the conductivity of the electrolyte, C_eFor the conductivity of the "clay-exchanged cation", i.e. the additional conductivity of the clay, F^*Formation factor of argillaceous sandstone, C_e＝BQ_v. Based on the improved W-S model, it is assumed herein,then there are:

C₀＝C_0sd+C_0sh

wherein C is₀Conductivity of pure rock at 100% saturation, C_0sdConductivity of pure sandstone fractions, C_0shIs the electrical conductivity of the argillaceous portion.

Similarly, the conductivity of a hydrocarbon-bearing formation may be expressed as:

C_t＝C_tsd+C_tsh

wherein C is_tIs the conductivity of the hydrocarbon-bearing formation, C_tsdIs the conductivity of sandstone parts of hydrocarbon-bearing formations, C_tshIs the conductivity of the argillaceous portion of the hydrocarbon-bearing formation.

The above formula can be obtained according to the formula:

C_0sd＝C₀-C_0sh

C_tsd＝C_t-C_tsh

the resistivity increase factor can be expressed as:

and correcting the resistivity increase coefficient by using rock electricity experimental data and CEC measurement data and combining the established resistivity increase coefficient expression (13). Additional conductivity in the shale would make the calculated conductivity of the rock too large, resulting in lower experimentally measured values. Therefore, the improved W-S model eliminates the influence of additional conductivity of the argillaceous sand, and establishes an increasing coefficient equation of resistivity of the argillaceous sand.

And thirdly, introducing a correction term of the relative permeability of the wetting phase based on the original conversion model of the resistivity index and the relative permeability. And establishing a conversion model of the shale sandstone reservoir rate index and the relative permeability.

3.1, measuring a relative permeability curve;

and after the rock sample is vacuumized for 48 hours, the rock core is saturated to simulate the mode of pressurizing formation water. The core was saturated. The outlet plug of the core holder is installed firstly, the core is installed, the core holder is connected into the displacement experiment process and is immersed in the formation water, and then the inlet plug is connected. Under the condition of reservoir temperature and pressure, oil is used for displacing a core of saturated water, a high-pressure high-precision displacement pump is used for injecting crude oil into the core, the core is displaced until no water is produced, and the total amount of the displaced water is measured. After displacement with the test oil to 10 pore volumes under water-bound conditions, i.e., when the displacement reached equilibrium. Oil phase permeability at irreducible water saturation was measured. The oil is displaced by mixing according to a certain oil-water ratio, and the pressure in the displacement process, the amount of the flowing formation water and oil and the time can be measured by using a high-pressure high-precision displacement pump until oil-free production. And recording the pressure of the rock sample inlet and outlet and the oil and water flow when the flow is stable, and measuring the oil and water quantity change in the oil-water separation. And simultaneously, measuring the resistance of two ends of the rock core. The computer automatically collects the resistance value and the volume of the expelled liquid. And (4) converting the oil-water ratio for displacement until no oil is produced, namely measuring the formation water permeability under the residual oil saturation when the residual oil saturation state is reached. And finally, accurately measuring the outlet oil and water quantity by using a 25ml oil-water separator and a balance with the graduation of 0.02-0.1ml at the outlet of the rock core, as shown in figure 2.

3.2, introducing a conversion model of the existing resistivity index and the relative permeability;

for a pure rock with complete water content, assuming that formation water is movable water and a rock skeleton is not conductive, the rock is conductive from water in rock pores, current of the rock can be conducted along a flow path of the water flow, based on the flow similarity of the water flow and the current, the flux similarity of Darcy's law and ohm's law is utilized, and normalized water saturation is introduced, so that a wetting phase I-Kr model is established (Li, 2005; Li, 2007);

can also be written as:

the Mandon introduces a bent capillary tube model, considers that the water phase seepage channels in the water-bearing rock and the oil-bearing gas rock are both kept in the shape of the bent capillary tube, the flow rate of the water flow is similar to that of the current flow, and establishes I-K considering tortuosity_rModel:

pairoys considers the influence of non-wetting term fluids, introducing the concept of residual phase saturation:

wherein S_rnwResidual saturation of the non-wetting phase. I-K established herein_rModel the non-wetting phase relative permeability was calculated using the Pairoys model:

3.3, introducing and determining a correction term of the relative permeability of the wetting phase;

the Maldon model and the Li model are pure rocks I-K_rModel, K calculated by model taking into account only conductivity of pure sandstone portion_rwBoth are greater than the laboratory measurements, as in figure 6, and both models are not applicable to argillaceous sandstone reservoirs. For a argillaceous sandstone reservoir, due to the influence of additional conductivity of argillaceous, a nonlinear relation is formed between a resistivity increase coefficient and water saturation under a log-log coordinate, and particularly, the influence effect is more obvious at high saturation, so that the influence of the argillaceous on the conductivity rule of the rock must be considered. Based on the method, the resistivity increasing coefficient of the pure rock is changed into the resistivity increasing coefficient of the argillaceous sandstone, the water saturation index is changed, the empirical coefficient is added, and the empirical coefficient correction is carried out on the calculated water phase relative permeability.

By fitting a formula and calculating the average absolute error, and finally determining that f is 1, the water phase relative permeability calculated by the saturation after the isotonic point is equal to the actually measured water phase relative permeability, and the overall effect is the best, as shown in table 2.

When f is 1, the calculation formula of the water phase relative permeability of the argillaceous sandstone reservoir is as follows:

the oil phase relative permeability continues to use the oil phase relative permeability calculation method of the Li model and the Madong model. Of note are formulasThe relative permeability of the wetting phase is used in calculating the relative permeability of the non-wetting phase, so we propose the argillaceous sandstone I-K_rwThe model also improves the calculation of the relative permeability of the non-wetting phase. Table 2 lists the mean absolute error for different correction term f coefficients.

TABLE 2 mean absolute error for different correction term f-coefficients

3.4, comparing the fluid phase permeability and the measured value obtained by the calculation of the models, and analyzing;

FIG. 3 and FIG. 4 show Li model and Maldon model, argillaceous sandstone reservoir I-K, respectively_r0The model calculates the water phase to oil phase relative permeability values for 6 rock samples versus laboratory measurements. From FIG. 4, it can be seen that K is calculated by Li model_rwK calculated by the Mandon model and greatly different from the actual measured value_rwThe fit is better at low water saturation, while at high water saturation the fit is generally effective with an average absolute error of 2.3%. K calculated by the model herein_rwThe fitting is better when the water saturation is high, the fitting effect is general when the water saturation is low, the average absolute error of the whole model is 0.8%, and the whole effect is superior to that of the Mandon model. As can be seen from FIG. 4, the sandstone samples are Li model, Maldon model and argillaceous sandstone I-K_rThe model fits well to the experimental measurements. Thus, under conditions of high water saturation in a argillaceous sandstone reservoir, the relative permeability of the water phase and the oil phase can be calculated using the model herein.

And fourthly, giving an application flow in the well, and completing logging processing and productivity prediction work by utilizing the established argillaceous sandstone reservoir resistivity index and relative permeability conversion model.

4.1, giving an application process in the well, and drawing a flow chart, such as fig. 5;

(1) and acquiring CEC data of a rock sample under a laboratory condition, and the combined measurement data of resistivity and phase permeability in the process of core oil flooding water under a reservoir condition to obtain the rock electrical parameter data. For improving W-S model and establishing I-K of argillaceous sandstone reservoir_rwThe model provides data support.

(2) An improved W-S model is established by utilizing the petroelectricity parameter data and the CEC data and combining an Archie formula and the actual stratum condition, and the calculation result of the improved W-S model has better consistency with the laboratory measurement result.

(3) Based on the improved W-S model, the influence of the additional conductivity of the cations on the surface of the clay is consideredResistivity correction is carried out to give an increasing coefficient I of the resistivity of the argillaceous sandstone^*。

(4) On the basis of the original model, the resistivity increase coefficient is changed into the resistivity increase coefficient I of the argillaceous sandstone given in the previous step^*An empirical coefficient is added. And selecting the optimal empirical coefficient through formula fitting and average absolute error calculation.

(5) And establishing reservoir parameter calculation relational expressions such as shale content, porosity, irreducible water saturation, permeability, residual oil saturation and the like by using a core scale logging method, and giving oil production, water production and water content formulas.

(6) And storing the two wells in the area, performing reservoir parameter interpretation and capacity prediction on the two wells in the area, comparing with an actual oil testing result, and evaluating the application effect of the established interpretation method.

4.2, automatically processing the actual logging data, and displaying the calculation result in a graph;

and according to the logging information, logging parameters including shale content, porosity, water saturation, permeability, residual oil saturation and irreducible water saturation are included. The method comprises the steps of respectively calculating the shale content by using a natural gamma method, determining the effective porosity of a stratum by using neutrons, calculating the water saturation by using an improved resistivity increasing coefficient of the shale sandstone reservoir and substituting the resistivity increasing coefficient into an Archie equation, obtaining parameters such as relative permeability of an oil phase and a water phase by using a model established in the method, and finally realizing the capacity prediction. And obtaining the permeability through a regression method of the porosity and permeability of the rock sample in the region, and obtaining the saturation of the irreducible water by utilizing the empirical relationship between the saturation of the irreducible water and the porosity of the rock sample. And (3) obtaining the residual oil saturation by performing multiple linear regression on the residual oil saturation, the irreducible water saturation, the permeability and the porosity, inputting the residual oil saturation into a computer system, and obtaining a logging comprehensive interpretation effect graph as shown in figure 6.

The flowchart of this embodiment can be referred to as shown in fig. 7.

The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.