CN110412660A - Reservoir Classification Method and Device - Google Patents

Abstract

The present invention provides a kind of Reservoir Classification method and apparatus, this method comprises: obtaining the lithology sensitive parameter of each rock stratum in target well according to the well-log information of the geologic information of target well and target well；According to the lithology sensitive parameter and pre-stored reservoir conditions of rock stratum each in target well, the reservoir in target well is determined；According to the lithology sensitive parameter of rock stratum each in reservoir and pre-stored quality factor model, the quality factor of each rock stratum in reservoir is obtained, quality factor is used to indicate the Reservoir type of each rock stratum in reservoir.Reservoir Classification method provided by the invention realizes the vertical continuity classification of reservoir, provides the foundation data for the prediction in effective reservoir space.

Description

Reservoir Classification Method and Device

technical field

The invention relates to the technical field of oil and gas development, in particular to a reservoir classification method and device.

Background technique

Reservoir classification refers to the classification and evaluation of reservoirs that are close to the real geological conditions, which is of great significance to oil and gas exploration and development.

Complex lithology (such as conglomerate, etc.), low porosity and low permeability reservoirs have complex pore structures. This kind of reservoirs shows that different rocks have significant differences in permeability under the same porosity conditions, that is, in macroscopically heterogeneous In the qualitative background, there is also complex microscopic anisotropy, and it is necessary to classify such reservoirs in detail when classifying reservoirs. In the prior art, different reservoirs are commonly distinguished by porosity and permeability. Although this classification method is simple, the classification results are not practical, and the classification results for reservoirs with complex pore structures are not detailed and the accuracy is low.

Contents of the invention

The invention provides a reservoir classification method and device, which realizes the vertical continuity classification of the reservoir, has high accuracy, and provides basic data for the prediction of the effective reservoir space.

The first aspect of the present invention provides a reservoir classification method, comprising: obtaining the lithology sensitive parameters of each rock formation in the target well according to the geological data of the target well and the logging data of the target well;

Determining the reservoir in the target well according to the lithology sensitive parameters of each rock formation in the target well and the pre-stored reservoir conditions;

Acquire the quality factor of each rock layer in the reservoir according to the lithology sensitive parameters of each rock layer in the reservoir and the pre-stored quality factor model, and the quality factor is used to indicate the reservoir of each rock layer in the reservoir type.

Optionally, the well logging data include: lithology indicating parameters, density parameters and lithoelectric parameters of each rock formation in the target well;

According to the geological data of the target well and the logging data of the target well, the lithology sensitive parameters of each rock formation in the target well are obtained, including:

Obtaining multiple rock formations in the target well according to the geological data of the target well;

According to the lithology indicating parameters of each rock formation in the target well, the shale content of each rock formation in the target well is obtained; the lithology indicating parameter is natural gamma ray, resistivity, porosity or spontaneous potential;

According to the shale content and density parameters of each rock formation in the target well, the effective porosity of each rock formation in the target well is obtained;

Obtaining the oil saturation of each rock formation in the target well according to the effective porosity and lithoelectric parameters of each rock formation in the target well;

According to the effective porosity, lithoelectric parameters and oil saturation of each rock layer in the target well, the lithology sensitive parameters of each rock layer in the target well are obtained.

Optionally, the density parameters include: skeleton density, pore fluid density, shale density and logging density of each rock formation in the target well;

According to the shale content and density parameters of each rock formation in the target well, the effective porosity of each rock formation in the target well is obtained, including:

Obtain the effective porosity of each rock formation in the target well according to the following formula one:

Wherein, ψ _D is the effective porosity of each rock formation in the described target well, ρ _ma is the skeleton density of each rock formation in the described target well, ρ _b is the logging density of each rock formation in the described target well, and ρ _f is the density of each rock formation in the described target well. The pore fluid density of each rock formation, ρSH is the shale density of each rock layer in the target well, and _SH is the shale content of each rock layer in the target well.

Optionally, the lithoelectric parameters include: the formation water resistivity of the target well and the logging resistivity of each rock formation in the target well;

According to the effective porosity and lithoelectric parameters of each rock formation in the target well, the oil saturation of each rock formation in the target well is obtained, including:

Obtain the oil saturation of each rock formation in the target well according to the following formula two:

Wherein, S ₀ is the oil saturation of each rock formation in the described target well, a is the first lithology coefficient, b is the second lithology coefficient, R is the formation water _resistivity of each rock formation in the described target well, and R _t is The logging resistivity of each rock formation in the target well, m is the cementation index, and n is the saturation index.

Optionally, the pre-stored reservoir conditions include: the minimum effective porosity value, the minimum logging resistivity value and the minimum oil saturation value of each rock formation in the target well;

The determining the reservoir in the target well according to the lithology sensitive parameters of each rock formation in the target well and the pre-stored reservoir conditions includes:

Determining the strata corresponding to the effective porosity greater than the minimum effective porosity value, logging resistivity greater than the minimum logging resistivity value and oil saturation greater than the minimum oil saturation value as the reservoir in the target well .

Optionally, the lithoelectric parameters include: the impedance of each rock formation in the target well;

According to the lithology sensitive parameters of the reservoir and the pre-stored quality factor model, obtaining the quality factor of each rock layer in the reservoir includes:

Acquire the quality factor of each rock formation in the reservoir according to the following formula three:

P _Z ＝x·(R _t -y·AI)·ψ _D Formula 3

Wherein, P _Z is the quality factor of each rock formation in the reservoir, x is the quality coefficient of the target well, y is the impedance coefficient of the target well, and AI is the impedance of each rock formation in the reservoir.

Optionally, after obtaining the quality factors of each rock formation in the reservoir according to the pre-stored quality factor model, the method further includes:

Acquiring the quality factor of each rock formation in the reservoirs of multiple adjacent wells of the target well;

The quality factor of each rock formation in the reservoir of the target area is acquired according to the quality factors of each rock formation in the reservoir of the target well and the plurality of adjacent wells.

A second aspect of the present invention provides a reservoir classification device, comprising:

A lithology sensitive parameter acquisition module, configured to acquire the lithology sensitive parameters of each rock formation in the target well according to the geological data of the target well and the logging data of the target well;

A reservoir determination module, configured to determine the reservoir in the target well according to the lithology sensitive parameters of each rock formation in the target well and the pre-stored reservoir conditions;

A quality factor acquisition model, used to acquire the quality factor of each rock layer in the reservoir according to the lithology sensitive parameters of each rock layer in the reservoir and the pre-stored quality factor model, and the quality factor is used to indicate the Reservoir type for each rock formation in the layer.

A third aspect of the present invention provides a reservoir classification device, comprising: at least one processor and a memory;

the memory stores computer-executable instructions;

The at least one processor executes the computer-executable instructions stored in the memory, so that the reservoir classification device performs the above-mentioned reservoir classification method.

A fourth aspect of the present invention provides a computer-readable storage medium, where computer-executable instructions are stored on the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, the above reservoir classification method is realized.

The present invention provides a reservoir classification method and device, the method comprising: according to the geological data of the target well and the logging data of the target well, obtaining the lithology sensitivity parameters of each rock formation in the target well; parameters and pre-stored reservoir conditions to determine the reservoir in the target well; according to the lithology sensitive parameters of each rock layer in the reservoir and the pre-stored quality factor model, the quality factor of each rock layer in the reservoir is obtained, and the quality factor is used to indicate The reservoir type for each rock formation in the reservoir. The reservoir classification method provided by the invention realizes the longitudinal continuity classification of the reservoir by using the quality factor model, has high accuracy, and provides basic data for the prediction of the effective reservoir space.

Description of drawings

Fig. 1 is a schematic flow chart one of the reservoir classification method provided by the present invention;

Fig. 2 is the histogram of lithology identification of each rock formation in Well Ma X;

Fig. 3 is the lithology identification plate of each rock formation in Well Ma X;

Fig. 4 is the second schematic flow diagram of the reservoir classification method provided by the present invention;

Fig. 5 is a schematic diagram of the reservoir determination results of each rock formation in Ma X well;

Fig. 6 is a schematic diagram of the reservoir classification results of Well Ma X;

Figure 7 is a schematic diagram of the reservoir classification results of multiple wells in the area where Well Ma X is located;

Fig. 8 is a structural schematic diagram 1 of the reservoir classification device provided by the present invention;

Fig. 9 is a second structural schematic diagram of the reservoir classification device provided by the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are part of the implementation of the present invention. example, not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

Fig. 1 is a first schematic flow diagram of the reservoir classification method provided by the present invention. The execution body of the method shown in Fig. 1 may be a reservoir classification device, and the reservoir classification device may be realized by any software and/or hardware. As shown in Figure 1, the reservoir classification method provided in this embodiment may include:

S101. According to the geological data of the target well and the logging data of the target well, the lithological sensitivity parameters of each rock formation in the target well are obtained.

Before exploiting oil and gas in the target wells of exploration, it is necessary to predict the oil and gas reservoirs of the target wells in order to improve the extraction efficiency in the exploitation stage. The geological data includes the core description data of the target well. According to the core description data, multiple rock layers in the target well can be obtained, and according to the core description data of each rock layer, the corresponding lithology of each rock layer can be obtained. Figure 2 is the histogram of the lithology identification of each rock formation in Well Ma X. As shown in Figure 2, according to the geological data of Well Ma X, it can be obtained that Well Ma X includes four rock formations at a depth of 3040-3120m, respectively containing Cobblestone, gray sandstone, mudstone and brown sandstone.

The logging data includes the lithological parameters of each rock formation in the target well, as shown in Figure 2, the lithological parameters may include: 1-meter bottom gradient resistivity R _i , 4-meter bottom gradient resistivity R _t , acoustic time difference AC of rock formations, Compensation density DEN, compensation neutron CNL, etc. Moreover, the lithological parameters in the logging data are vertically continuous, and are often represented by continuous logging curves, such as: acoustic wave time difference logging curves, etc.; where the FMI image in Figure 2 is the micro-resistivity scanning imaging image of the rock formation , the resistivity of the rock formation in the following examples is the gradient resistivity R _t at the bottom of 4 meters.

Specifically, this example takes Well Ma X as an example to illustrate the lithology parameters in the logging data. The area of Well Ma X mainly includes gray pebble-bearing coarse sandstone, gray glutenite, gray calcareous cemented glutenite, gray calcareous The logging data of cemented conglomerate, brown glutenite and mudstone are as follows:

Gray pebble-bearing coarse sandstone: characterized by medium electrical resistance, low density, and high porosity, resistivity 25-40Ω.m, density 2.35-2.43g/cm ³ , NMR total pores and effective pores are both greater than 10%, FMI image shows yellow Irregular bright white blocks are embedded on the base.

Gray sandy conglomerate: characterized by medium-high resistance, medium density, and medium porosity, resistivity 30-100Ω.m, density 2.43-2.53g/cm ³ , NMR total pores 7-14%, FMI image shows bright yellow background and is relatively regular The bright spot pattern, the bedding is not developed, and the bright spot gravels are arranged in disorder.

Gray calcareous cemented glutenite: characterized by ultra-high resistance, medium density, and medium porosity, resistivity greater than 200Ω.m, density 2.45-2.55g/cm ³ , NMR total pores 7-9%, FMI images can be seen with a bright yellow background The lower black patches reflect the development of secondary pores.

Gray calcareous cemented conglomerate: characterized by ultra-high resistance, low acoustic wave, and low porosity, resistivity greater than 200Ω.m, density 2.55-2.65g/cm ³ , total nuclear magnetic pores less than 5%, FMI images show that fractures are well developed, partial.

Brown sandy conglomerate: characterized by low electrical resistance, high density, and low porosity, with resistivity less than 25Ω.m, density 2.55-2.6g/cm ³ , and total NMR pores less than 5%.

Mudstone: ultra-low electrical resistance, disordered density, ultra-low porosity, resistivity less than 10Ω.m, density 2.25-2.65g/cm ³ , NMR total pores less than 5%, FMI image presents dark yellow features, and horizontal bedding can be seen.

According to the geological data and logging data of the target well, the lithological sensitivity parameters of each rock formation are obtained. Due to the characteristics of low porosity and low permeability in the glutenite area, and the complex pore structure, different rocks have significant differences in permeability under the same porosity conditions. The lithological sensitivity parameters in this area can be the resistance of the rock formation One or more of porosity, porosity, and oil saturation. Since the lithology parameters in the well logging data represent the vertical continuity of each rock formation, the lithology sensitive parameters obtained in this embodiment are also the representation of the vertical continuity of each rock formation. Specifically, Figure 3 is the lithology identification plate of each rock formation in Ma X well, where the abscissa is the compensation density of the rock formation, and the ordinate is the resistivity of the rock formation. Reservoirs are divided.

S102. Determine the reservoir in the target well according to the lithology sensitive parameters of each rock formation in the target well and the pre-stored reservoir conditions.

When classifying the reservoir of the target well, the reservoir condition can be set in advance, and the reservoir condition corresponds to the lithology sensitive parameter of the target well. The reservoir condition can be the lower limit value corresponding to the lithology sensitive parameter or the upper and lower limit value corresponding to the lithology sensitive parameter.

Correspondingly, the specific method of determining the reservoir in the target well can be: determining the rock formation whose lithology sensitive parameter corresponding to each rock formation in the target well is greater than the lower limit value in the reservoir condition as a reservoir; The rock formations whose lithology sensitive parameters are between the upper and lower limits in the reservoir conditions are determined as reservoirs. This embodiment does not limit the pre-stored reservoir conditions and the manner of determining the reservoir.

S103. Obtain the quality factor of each rock layer in the reservoir according to the lithology sensitive parameters of each rock layer in the reservoir and the pre-stored quality factor model, and the quality factor is used to indicate the reservoir type of each rock layer in the reservoir.

Bring the lithology sensitive parameters obtained in S101 into the pre-stored quality factor model to obtain the quality factor of each rock formation in the reservoir. In the existing technology, the reservoirs are often divided by the size of lithological parameters. In this way, the experimental data can only be used to achieve qualitative classification, but the quantitative classification of the entire well interval reservoir cannot be realized. Reservoirs are classified according to their production, which includes not only energy oil, but also a large amount of water. Therefore, the reservoir classification methods in the prior art cannot accurately characterize the amount of oil in the reservoir and the quality of the oil.

In this embodiment, after obtaining the reservoir in the target well according to the reservoir conditions, the quality of the reservoir is also divided, and the quality factor provided in this embodiment is a characterization of the oil quality in the reservoir. At the same time of classification, the oil quality of different reservoirs is also obtained.

The reservoir classification method provided in this embodiment includes: according to the geological data of the target well and the logging data of the target well, obtaining the lithology sensitive parameters of each rock formation in the target well; Reservoir conditions, determine the reservoir in the target well; obtain the quality factor of each rock layer in the reservoir according to the lithology sensitive parameters of each rock layer in the reservoir and the pre-stored quality factor model, and the quality factor is used to indicate each rock layer in the reservoir type of reservoir. The reservoir classification method provided by the present invention uses the quality factor model to realize the vertical continuity classification of the reservoir, and at the same time realizes the classification of the reservoir, it also obtains the oil quality of different reservoirs, with high accuracy, which is an effective reservoir space The predictions provided accurate reference materials.

Below in conjunction with Fig. 4, the reservoir classification method provided by the present invention will be described in detail. Fig. 4 is a flow diagram two of the reservoir classification method provided by the present invention. As shown in Fig. 4, the reservoir classification method provided by the present invention may include:

S201. Obtain multiple rock formations in the target well according to the geological data of the target well.

Specifically, in this embodiment, different lithologies in the target well can be obtained according to the core description data in the geological data, and the rock formations in the target well can be divided according to the lithology to obtain multiple rock formations.

S202. Obtain the shale content of each rock layer in the target well according to the lithology indicating parameter of each rock layer in the target well; the lithology indicating parameter is natural gamma ray, resistivity, porosity or spontaneous potential.

In this embodiment, the logging data includes: lithology indicating parameters, density parameters and lithoelectric parameters of each rock formation in the target well; the lithology indicating parameters of the rock formations are lithology parameters that can distinguish lithology, such as: A rock formation and B rock formation They are rock formations of different lithologies. The resistivity of rock formation A is generally 25-40Ω m, while the resistivity of rock formation B is generally less than 10Ω m. From the resistivity parameters, rock formation A and rock formation B can be distinguished, so the resistivity can be The lithology indicating parameters of formation A and formation B.

Specifically, in this embodiment, Well Ma X is taken as an example for illustration. The stratum indicating parameter of Well Ma X is resistivity R _t , and R _t can be used as the adopted value for obtaining the shale content of each stratum. Wherein, the shale content of each rock formation can be obtained according to the following formulas, formula 4 and formula 5:

Among them, SH' is the transition parameter of shale content, R _t is the resistivity of each rock formation in Well Ma X, specifically, it is the logging resistivity of each rock formation, R _tMIN is the resistivity of mudstone layer, R _tMAX is the resistivity of sandy conglomerate layer Resistivity, SH is the shale content of each rock formation in Well Ma X, and GCUR is the regional empirical coefficient. Generally, the regional empirical coefficient of new formations is 3.7, and the regional empirical coefficient of old formations is 2.

Specifically, the lithology indicating parameters in this embodiment are natural gamma ray, resistivity, porosity or natural potential. When obtaining the shale content of each rock layer, the resistivity in the above formula 4 and formula 5 is replaced by other such as Natural gamma ray, porosity or spontaneous potential will do.

Depending on the region, the lithology indicating parameters are different. The specific way to select the lithology indicating parameters can be to combine the natural gamma ray logging curve, resistivity logging curve, porosity logging curve and spontaneous potential logging curve of each rock formation in the target well. The well curves are compared, and the lithological parameters corresponding to the logging curves with the largest difference in each rock formation are determined as the indicator parameters of each rock formation in the target well.

S203. Obtain the effective porosity of each rock layer in the target well according to the shale content and density parameters of each rock layer in the target well.

The density parameters in this embodiment can include: the skeleton density, pore fluid density, shale density and logging density of each rock formation in the target well; wherein, the logging density of each rock formation is the density collected in the target well for each rock formation; The skeletal density of each rock layer is the core density after the cores corresponding to each rock layer are taken out from the target well, the fluid and mud in the core are washed and dried; the pore fluid density and shale density of each rock layer are measured before the skeleton density Perform measurement acquisition.

Specifically, the effective porosity of each rock formation in the target well can be obtained according to the following formula one:

Among them, ψ _D is the effective porosity of each rock formation in the target well, ρ _ma is the skeleton density of each rock formation in the target well, ρ _b is the logging density of each rock formation in the target well, ρ _f is the pore fluid density of each rock formation in the target well, and ρ _SH is the shale density of each rock formation in the target well, and SH is the shale content of each rock formation in the target well.

S204. According to the effective porosity and lithoelectric parameters of each rock formation in the target well, the oil saturation of each rock formation in the target well is obtained.

In this embodiment, the lithoelectric parameters include: the water resistivity of each rock formation in the target well and the resistivity of each rock formation in the target well.

Specifically, the oil saturation of each rock formation in the target well can be obtained according to the following formula two:

Among them, S ₀ is the oil saturation of each rock formation in the target well, a is the first lithology coefficient, b is the second lithology coefficient, R _w is the formation water resistivity of each rock formation in the target well, and R _t is the formation water resistivity of each rock formation in the target well m is the cementation index, which is related to the rock cementation and pore structure, and n is the saturation index, among which, it is related to the distribution of oil, gas and water in the pores. Specifically, a, b, m and n in Formula 2 can be obtained in a laboratory by sampling cores of each rock formation according to methods in the prior art.

S205. According to the effective porosity, lithoelectric parameters and oil saturation of each rock layer in the target well, the lithology sensitive parameters of each rock layer in the target well are obtained.

As in the above embodiments, different regions have different lithological sensitive parameters. In this embodiment, the glutenite region is taken as an example, and the lithological sensitive parameters are effective porosity, resistivity and oil saturation.

Those skilled in the art can imagine that different lithological sensitivity parameters can be selected according to different regions.

S206. Determine the reservoir in the target well according to the lithology sensitive parameters of each rock formation in the target well and the pre-stored reservoir conditions.

Wherein, the pre-stored reservoir conditions include: the minimum effective porosity value, the minimum resistivity value and the minimum oil saturation value of the target well.

After obtaining the lithological sensitivity parameters of each rock formation in the target well, combined with the reservoir conditions, determine the rock formations corresponding to the effective porosity greater than the minimum effective porosity value, the resistivity greater than the minimum resistivity value, and the oil saturation greater than the minimum oil saturation value is the reservoir in the target well. For example, the reservoir of Well Ma X is: the porosity is greater than 7%, the resistivity is greater than 30Ω.m, and the oil saturation is greater than 42%. Determine the reservoir in each rock formation in Fig. 3 according to the reservoir conditions to obtain the results in Fig. 5. Fig. 5 is a schematic diagram of the determination results of the reservoirs in each rock formation in Ma X well. As shown in Fig. 5, the reservoir in Ma X well Layers are the corresponding rock formations in the boxes.

S207, according to the lithology sensitive parameters of the reservoir and the pre-stored quality factor model, acquire the quality factor of each rock layer in the reservoir.

The lithoelectric parameters in this embodiment also include: the impedance of each formation in the target well. Specifically, the quality factor of each rock layer in the reservoir can be obtained according to the following formula three:

P _Z ＝x·(R _t -y·AI)·ψ _D Formula 3

Among them, P _Z is the quality factor of each rock layer in the reservoir, x is the quality coefficient of the target well, y is the impedance coefficient of the target well, and AI is the impedance of each rock layer in the reservoir. Specifically, it is an empirical coefficient, and different regions have different quality coefficients x, and the impedance coefficient y is set in order to make the impedance and resistivity close in magnitude, usually a constant of 0.01. Optionally, the effective porosity in this embodiment may be the effective porosity calculated in the above formula 1, or the nuclear magnetic effective porosity obtained by nuclear magnetic logging.

Specifically, in this embodiment, a rock formation with a quality factor greater than 2 is determined as a type I reservoir, a rock formation with a quality factor of 1-2 is determined as a type II reservoir, and a rock formation with a quality factor less than 1 is determined as a type III reservoir.

The reservoir classification method provided by the present invention is applied below in conjunction with FIG. 6 . FIG. 6 is a schematic diagram of the reservoir classification results of Well Ma X, and Table 1 is a table of classification results of 3295.5-3303.0m reservoirs in Well Ma X corresponding to FIG. 6 . Table 1 is as follows:

Table I

layer number top depth Bottom depth layer thickness RT AI quality factor Reservoir classification 1 3296.0 3297.1 1.1 28.3 11780.4 0.60 Type III reservoir 2 3297.1 3298.5 1.4 35.6 11614.8 1.22 Type II reservoir 3 3298.5 3299.0 0.6 43.0 11533.0 2.01 Class I reservoir 4 3299.0 3299.6 0.6 40.7 11673.8 1.47 Type II reservoir 5 3299.6 3300.8 1.2 33.5 11582.0 0.70 Type III reservoir 6 3300.8 3302.3 1.5 34.6 11729.5 1.27 Type II reservoir

In Fig. 6, the reservoir classification method provided in this example is used to realize the vertical continuous reservoir classification of Well Ma X. From Fig. 6 and Table 1, it can be seen that: the target well at 3295.5-3303.0m is mainly divided into II and III Class reservoirs. However, in the oil test of this well section, three stages of fracturing combined test were used, and a 3.0mm choke was used to obtain a daily output of 2.35 tons of oil and 1680m ³ of gas. The reservoir classification results are in good agreement.

Further, the reservoir classification method provided in this embodiment can be applied to different target wells in the same area. Figure 7 is a schematic diagram of the reservoir classification results of multiple wells in the area where Well Ma X is located. As shown in Figure 7, after obtaining the target well After the quality factor of each rock formation in the reservoir, the multiple adjacent wells of the target well, such as Well A, Well B, Well C, Well D, Well E, Well F, Well G and Well H, can also obtain the respective quality factors in the reservoir. The quality factor of the rock formation.

According to the geological data of the target area where the target well is located, the lateral change of the reservoir in the target area can be obtained, and according to the quality factors of each rock layer in the reservoir of the target well and multiple adjacent wells, the rock layers in the reservoir in the target area can be obtained The quality factor of each stratum in the reservoir corresponding to the target area can be used to obtain the lateral change law of the reservoir in the target area, and further provide basic information for the prediction of the effective reservoir space in the target area.

The reservoir classification method provided in this embodiment obtains the shale content of each rock layer according to the lithology indicating parameters of each rock layer in the target well, and obtains the shale content of each rock layer according to the shale content, skeleton density, pore fluid density, shale density and logging Density, to obtain the effective porosity of each rock layer, according to the effective porosity of each rock layer and the water resistivity and resistivity of each rock layer, to obtain the oil saturation of each rock layer, according to the effective porosity, resistivity and oil saturation of each rock layer and Pre-stored reservoir conditions determine the reservoir in the target well, and obtain the quality factor of each rock layer in the reservoir according to the effective porosity, resistivity and the pre-stored quality factor model. The reservoir classification method provided in this embodiment determines the reservoir according to the close parameters of the reservoir, effective porosity, resistivity and oil saturation, and further classifies the reservoir on the basis of the effective reservoir, with high accuracy, as Prediction of effective reservoir space provides basic information.

Fig. 8 is a first structural diagram of the reservoir classification device provided by the present invention. As shown in Fig. 8, the reservoir classification device 300 includes: a lithology sensitive parameter acquisition module 301, a reservoir determination module 302, and a quality factor acquisition module 303.

The lithology sensitive parameter acquisition module 301 is used to acquire the lithology sensitive parameters of each rock formation in the target well according to the geological data of the target well and the logging data of the target well;

A reservoir determination module 302, configured to determine the reservoir in the target well according to the lithology sensitive parameters of each rock formation in the target well and the pre-stored reservoir conditions;

The quality factor acquisition module 303 is used to obtain the quality factor of each rock layer in the reservoir according to the lithology sensitive parameters of each rock layer in the reservoir and the pre-stored quality factor model, and the quality factor is used to indicate the reservoir quality of each rock layer in the reservoir type.

The principle and technical effect of the reservoir classification device provided in this embodiment are similar to those of the above reservoir classification method, and will not be repeated here.

Optionally, the logging data include: lithology indicator parameters, density parameters and lithoelectric parameters of each rock formation in the target well;

The lithology sensitive parameter acquisition module 301 is specifically used to acquire multiple rock formations in the target well according to the geological data of the target well;

Obtain the shale content of each rock layer in the target well according to the lithology indicating parameters of each rock layer in the target well; the lithology indicating parameters are natural gamma ray, resistivity, porosity or spontaneous potential;

Obtain the effective porosity of each rock layer in the target well according to the shale content and density parameters of each rock layer in the target well;

Obtain the oil saturation of each rock formation in the target well according to the effective porosity and lithoelectric parameters of each rock formation in the target well;

According to the effective porosity, lithoelectric parameters and oil saturation of each rock formation in the target well, the lithology sensitive parameters of each rock formation in the target well are obtained.

Optionally, the density parameters include: skeleton density, pore fluid density, shale density and logging density of each rock formation in the target well;

The lithology sensitive parameter acquisition module 301 is specifically used to acquire the effective porosity of each rock formation in the target well according to the following formula 1:

Among them, ψ _D is the effective porosity of each rock formation in the target well, ρ _ma is the skeleton density of each rock formation in the target well, ρ _b is the logging density of each rock formation in the target well, ρ _f is the pore fluid density of each rock formation in the target well, and ρ _SH is the shale density of each rock formation in the target well, and SH is the lithology indicator parameter of the target well.

Optionally, the lithoelectric parameters include: the formation water resistivity of the target well and the resistivity of each rock formation in the target well;

The lithological sensitive parameter acquisition module 301 is specifically used to obtain the oil saturation of each rock formation in the target well according to the following formula 2:

Among them, S ₀ is the oil saturation of each rock formation in the target well, a is the first lithology coefficient, b is the second lithology coefficient, R _w is the formation water resistivity of each rock formation in the target well, and R _t is the formation water resistivity of each rock formation in the target well resistivity, m is the cementation index, and n is the saturation index.

Optionally, the pre-stored reservoir conditions include: the minimum effective porosity value, the minimum resistivity value and the minimum oil saturation value of each rock formation in the target well;

The reservoir determination module 302 is specifically used to determine the strata corresponding to the effective porosity greater than the minimum effective porosity value, the resistivity greater than the minimum resistivity value, and the oil saturation greater than the minimum oil saturation value as the reservoir in the target well.

Optionally, the lithoelectric parameters include: the impedance of each rock formation in the target well;

The quality factor acquisition module 303 is specifically used to acquire the quality factor of each rock formation in the reservoir according to the following formula three:

P _Z ＝x·(R _t -y·AI)·ψ _D Formula 3

Among them, P _Z is the quality factor of each in the reservoir, x is the quality coefficient of the target well, y is the impedance coefficient of the target well, and AI is the impedance of each rock layer in the reservoir.

Optionally, the quality factor acquisition module 303 is also used to acquire the quality factor of each rock formation in the reservoir of multiple adjacent wells of the target well;

According to the quality factors of each rock formation in the reservoir of the target well and multiple adjacent wells, the quality factor of each rock formation in the reservoir of the target area is obtained.

FIG. 9 is a second structural schematic diagram of the reservoir classification device provided by the present invention. The reservoir classification device may be, for example, a terminal device, such as a smart phone, a tablet computer, or a computer. As shown in FIG. 9 , the reservoir classification device 400 includes: a memory 401 and at least one processor 402 .

The memory 401 is used for storing program instructions.

The processor 402 is configured to implement the method for classifying reservoirs in this embodiment when the program instructions are executed. For specific implementation principles, please refer to the above-mentioned embodiments, and details will not be repeated here in this embodiment.

The reservoir classification device 400 may also include an input/output interface 403 .

The input/output interface 403 may include an independent output interface and an input interface, or may be an integrated interface integrating input and output. Wherein, the output interface is used to output data, and the input interface is used to obtain input data, the above-mentioned output data is a general term for output in the above method embodiments, and the input data is a general term for input in the above method embodiments.

The present invention also provides a readable storage medium, wherein execution instructions are stored in the readable storage medium, and when at least one processor of the reservoir classification device executes the execution instructions, when the computer execution instructions are executed by the processor, the above implementation is realized. The reservoir classification method in the example.

The present invention also provides a program product, which includes execution instructions, and the execution instructions are stored in a readable storage medium. At least one processor of the reservoir classification device can read the execution instructions from the readable storage medium, and the at least one processor executes the execution instructions so that the reservoir classification device implements the reservoir classification methods provided in the above-mentioned various embodiments.

In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or May be integrated into another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware, or in the form of hardware plus software functional units.

The above-mentioned integrated units implemented in the form of software functional units may be stored in a computer-readable storage medium. The above-mentioned software functional units are stored in a storage medium, and include several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (English: processor) to execute the program described in each embodiment of the present invention. part of the method. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (English: Read-Only Memory, abbreviated: ROM), random access memory (English: Random Access Memory, abbreviated: RAM), magnetic disk or optical disc, etc. Various media that can store program code.

In the embodiments of the above-mentioned network device or terminal device, it should be understood that the processor may be a central processing unit (English: Central Processing Unit, CPU for short), and may also be other general-purpose processors, digital signal processors (English: Digital Signal Processor, referred to as: DSP), application specific integrated circuit (English: Application Specific Integrated Circuit, referred to as: ASIC), etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like. The steps of the methods disclosed in this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: It is still possible to modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the various embodiments of the present invention. scope.

Claims (10)

1. A reservoir classification method, characterized in that, comprising: According to the geological data of the target well and the logging data of the target well, the lithology sensitivity parameters of each rock formation in the target well are obtained; Determining the reservoir in the target well according to the lithology sensitive parameters of each rock formation in the target well and the pre-stored reservoir conditions; Acquire the quality factor of each rock layer in the reservoir according to the lithology sensitive parameters of each rock layer in the reservoir and the pre-stored quality factor model, and the quality factor is used to indicate the reservoir of each rock layer in the reservoir type. 2. The method for classifying reservoirs according to claim 1, wherein the logging data comprises: lithology indicating parameters, density parameters and rock-electric parameters of each rock formation in the target well; According to the geological data of the target well and the logging data of the target well, the lithology sensitive parameters of each rock formation in the target well are obtained, including: Obtaining multiple rock formations in the target well according to the geological data of the target well; According to the lithology indicating parameters of each rock formation in the target well, the shale content of each rock formation in the target well is obtained; the lithology indicating parameter is natural gamma ray, resistivity, porosity or spontaneous potential; According to the shale content and density parameters of each rock formation in the target well, the effective porosity of each rock formation in the target well is obtained; Obtaining the oil saturation of each rock formation in the target well according to the effective porosity and lithoelectric parameters of each rock formation in the target well; According to the effective porosity, lithoelectric parameters and oil saturation of each rock layer in the target well, the lithology sensitive parameters of each rock layer in the target well are obtained. 3. reservoir classification method according to claim 2, is characterized in that, described density parameter comprises: skeleton density, pore fluid density, shale density and logging density of each formation in described target well; According to the shale content and density parameters of each rock formation in the target well, the effective porosity of each rock formation in the target well is obtained, including: Obtain the effective porosity of each rock formation in the target well according to the following formula one: Wherein, ψ _D is the effective porosity of each rock formation in the described target well, ρ _ma is the skeleton density of each rock formation in the described target well, ρ _b is the logging density of each rock formation in the described target well, and ρ _f is the density of each rock formation in the described target well. The pore fluid density of each rock formation, ρSH is the shale density of each rock layer in the target well, and _SH is the shale content of each rock layer in the target well. 4. reservoir classification method according to claim 3, is characterized in that, described lithoelectric parameter comprises: the resistivity of each formation water resistivity and each formation in described target well in the described target well; According to the effective porosity and lithoelectric parameters of each rock formation in the target well, the oil saturation of each rock formation in the target well is obtained, including: Obtain the oil saturation of each rock formation in the target well according to the following formula two: Wherein, S ₀ is the oil saturation of each rock formation in the described target well, a is the first lithology coefficient, b is the second lithology coefficient, R is the formation water _resistivity of each rock formation in the described target well, and R _t is The resistivity of each rock formation in the target well, m is the cementation index, and n is the saturation index. 5. The reservoir classification method according to claim 4, wherein the pre-stored reservoir conditions include: the minimum effective porosity value, the minimum resistivity value and the minimum oil saturation value of the target well ; The determining the reservoir in the target well according to the lithology sensitive parameters of each rock formation in the target well and the pre-stored reservoir conditions includes: Determining rock formations with effective porosity greater than the minimum effective porosity value, resistivity greater than the minimum resistivity value, and oil saturation greater than the minimum oil saturation value as reservoirs in the target well. 6. The method for classifying reservoirs according to claim 2, wherein the rock-electric parameters comprise: the impedance of each rock formation in the target well; According to the lithology sensitive parameters of the reservoir and the pre-stored quality factor model, obtaining the quality factor of each rock layer in the reservoir includes: Acquire the quality factor of each rock formation in the reservoir according to the following formula three: P _Z ＝x·(R _t -y·AI)·ψ _D Formula 3 Wherein, P _Z is the quality factor of each rock formation in the reservoir, x is the quality coefficient of the target well, y is the impedance coefficient of the target well, and AI is the impedance of each rock formation in the reservoir. 7. The reservoir classification method according to claim 1, characterized in that, after obtaining the quality factor of each rock formation in the reservoir according to the pre-stored quality factor model, it also includes: Acquiring the quality factor of each rock formation in the reservoirs of multiple adjacent wells of the target well; The quality factor of each rock formation in the reservoir of the target area is acquired according to the quality factors of each rock formation in the reservoir of the target well and the plurality of adjacent wells. 8. A reservoir classification device, characterized in that it comprises: A lithology sensitive parameter acquisition module, configured to acquire the lithology sensitive parameters of each rock formation in the target well according to the geological data of the target well and the logging data of the target well; A reservoir determination module, configured to determine the reservoir in the target well according to the lithology sensitive parameters of each rock formation in the target well and the pre-stored reservoir conditions; A quality factor acquisition model, used to acquire the quality factor of each rock layer in the reservoir according to the lithology sensitive parameters of each rock layer in the reservoir and the pre-stored quality factor model, and the quality factor is used to indicate the Reservoir type for each rock formation in the layer. 9. A reservoir classification device, comprising: at least one processor and a memory; the memory stores computer-executable instructions; The at least one processor executes the computer-executed instructions stored in the memory, so that the reservoir classification device executes the reservoir classification method according to any one of claims 1-7. 10. A computer-readable storage medium, characterized in that computer-executable instructions are stored on the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, the computer-readable storage medium according to any one of claims 1-7 is implemented. The reservoir classification method described above.