阅读说明：本技术 松辽盆地北部致密油储层甜点分类方法 (Dessert classification method for compact oil reservoir in northern part of Songliao basin ) 是由 姚东华 赵杰 付晨东 许淑梅 汪爱云 于 2019-09-20 设计创作，主要内容包括：本发明属于油气储层评价技术领域,尤其涉及松辽盆地北部致密油储层甜点分类方法。本发明考虑储层孔隙度、渗透率、含油饱和度以及加砂量,建立了合试层产能劈分原则；对研究区试油、试采资料进行统计分析,由采油强度确定了松辽盆地北部致密油储层“甜点”分类标准；再综合考虑储层的储油能力、渗流能力以及工程压裂难易,构建了储层宏观品质、微观孔隙结构品质以及工程品质三个品质因子,建立了三维致密油储层“甜点”分类图版和Fisher判别公式,提高了致密油储层“甜点”分类的判准率；本发明由核磁T<Sub>2GM</Sub>法分类计算储层的孔隙结构特征参数,避免中、小孔隙结构储层由核磁T<Sub>2</Sub>谱转换至伪毛管压力时产生的误差,提高了致密油储层孔隙结构特征参数的计算精度。(The invention belongs to the technical field of oil and gas reservoir evaluation, and particularly relates to a method for classifying desserts of a dense oil reservoir in the north of Songliao basin. According to the invention, the porosity, permeability, oil saturation and sand adding amount of the reservoir stratum are considered, and a yield splitting principle of a qualified stratum is established; performing statistical analysis on oil testing and production testing data in a research area, and determining a dessert classification standard of a compact oil reservoir in the north of the Songliao basin according to the oil production intensity; then comprehensively considering the oil storage capacity, the seepage capacity and the engineering fracturing difficulty of the reservoir, constructing three quality factors of the macroscopic quality, the micro-pore structure quality and the engineering quality of the reservoir, establishing a three-dimensional compact oil reservoir dessert classification chart and a Fisher discrimination formula, and improving the precision rate of the dessert classification of the compact oil reservoir; the invention consists of nuclear magnetic resonance T 2GM The method calculates the characteristic parameters of the pore structure of the reservoir layer in a classification way, and avoids the reservoir layer with the medium and small pore structures from nuclear magnetism T 2 The error generated when the spectrum is converted to the pseudo capillary pressure,the calculation precision of the pore structure characteristic parameters of the compact oil reservoir is improved.)

1. A method for classifying desserts in a dense oil reservoir in the north of Songliao basin is characterized by comprising the following steps: the classification method comprises the following steps:

s1: the oil extraction intensity calculation method and the classification standard of compact oil dessert are determined:

s1.1: establishing a single-layer productivity evaluation index M in the research area according to the oil testing data and the logging parameters of a plurality of wells in the research area_iOn the basis of the above steps, splitting the well-logging productivity in the research area to obtain the single-layer oil production strength Q_si，

Wherein Q is the total yield of the well combining test, h_iIs the thickness of the ith layer, phi_iPorosity of the i-th layer, K_iPermeability of the i-th layer, S_oiIs the oil saturation of the i-th layer, V_sThe total sand adding amount is calculated;

s1.2: dividing the 'desserts' of the reservoir into three types according to the regional conditions and the oil extraction intensity, and making classification standards;

s2: a compact oil reservoir dessert classification well logging characterization method comprises the following steps:

s2.1: investigating the correlation between each reservoir parameter and oil extraction strength, wherein the preferred oil extraction strength sensitive parameters comprise porosity, oil saturation, permeability, pore throat radius mean value, displacement pressure, brittleness index and fracture pressure; the linear relation between each sensitive parameter and the oil extraction intensity is used as the basis information for researching the subsequent development well in the area;

s2.2: the oil extraction strength sensitive parameters are normalized, and then the normalized seven oil extraction strength sensitive parameters are used for constructing the reservoir layer macroscopic quality RQ₁Micro-pore structure quality RQ₂And three quality factors of engineering quality CQ: RQ₁＝φ*S_o，Wherein K is the air permeability, D_mMean pore throat radius, P_dFor displacement pressure, BRIT is brittleness index, FPG is fracture;

s2.3: three quality factors are used for establishing a three-axis space rendezvous chart, and then a Fisher discrimination method is adopted for establishing discrimination expressions of three types of reservoir sweet spots, so that the compact oil layer sweet spots are classified.

2. The method for classifying desserts from tight oil reservoir in northern Songliao basin according to claim 1, wherein: the method for accurately calculating the parameters of the reservoir micro-pore structure quality comprises the following steps: using nuclear magnetic T_2GMRQ for calculating reservoir micro pore structure quality by classification₂Parameter D of_mAnd P_d。

Technical Field

The invention belongs to the technical field of oil and gas reservoir evaluation, and particularly relates to a method for classifying desserts of a dense oil reservoir in the north of Songliao basin.

Background

The compact oil resource in the northern part of the Songliao basin is rich, and the reservoir contains mud and calcium. Compared with conventional oil, the compact oil reservoir has poorer physical property and permeability and more complex pore structure. According to the general macroscopic logging parameters (the reservoir quality index RQI and the formation mobility zone index FZI) such as porosity, permeability and combination thereof, a two-dimensional space chart is established, which can only roughly divide the reservoir types of the compact oil and cannot finely distinguish the difference of the micro-pore structures of the reservoirs. In addition, the compact oil reservoir generally has no natural capacity, the industrial capacity can be formed only through large-scale fracturing modification, the difficulty degree of reservoir fracturing cannot be represented by a common reservoir classification method, accurate technical support cannot be provided for compact oil dessert optimization and fracturing construction design, and the requirements of exploration, development and evaluation of the compact oil in the north of the Songliao basin cannot be met.

Disclosure of Invention

In order to solve the problem that the existing method for classifying the 'sweetmeats' in the reservoir is not comprehensive and accurate enough in the background art, the invention provides the method for classifying the desserts in the compact oil reservoir in the northern part of the Songliaopelvic area.

The technical scheme provided by the invention is as follows: a classification method for compact oil reservoir desserts in the north of Songliao basin comprises the following steps:

s1: the oil extraction intensity calculation method and the classification standard of compact oil dessert are determined:

s1.1: establishing a single-layer productivity evaluation index M in the research area according to the oil testing data and the logging parameters of a plurality of wells in the research area_iOn the basis of the above steps, splitting the well-logging productivity in the research area to obtain the single-layer oil production strength Q_si，

Wherein Q is the total yield of the well combining test, h_iAs the i-th layerThickness phi_iPorosity of the i-th layer, K_iPermeability of the i-th layer, S_oiIs the oil saturation of the i-th layer, V_sThe total sand adding amount is calculated;

s1.2: dividing the 'desserts' of the reservoir into three types according to the regional conditions and the oil extraction intensity, and making classification standards;

s2: a compact oil reservoir dessert classification well logging characterization method comprises the following steps:

s2.1: investigating the correlation between each reservoir parameter and oil extraction strength, wherein the preferred oil extraction strength sensitive parameters comprise porosity, oil saturation, permeability, pore throat radius mean value, displacement pressure, brittleness index and fracture pressure; the linear relation between each sensitive parameter and the oil extraction intensity is used as the basis information for researching the subsequent development well in the area;

s2.2: the oil extraction strength sensitive parameters are normalized, and then the normalized seven oil extraction strength sensitive parameters are used for constructing the reservoir layer macroscopic quality RQ₁Micro-pore structure quality RQ₂And three quality factors of engineering quality CQ: RQ₁＝φ*S_o，

Wherein K is the air permeability, D_mMean pore throat radius, P_dFor displacement pressure, BRIT is brittleness index, FPG is fracture;

s2.3: three quality factors are used for establishing a three-axis space rendezvous chart, and then a Fisher discrimination method is adopted for establishing discrimination expressions of three types of reservoir sweet spots, so that the compact oil layer sweet spots are classified.

The method for accurately calculating the parameters of the reservoir micro-pore structure quality comprises the following steps: using nuclear magnetic T_2GMRQ for calculating reservoir micro pore structure quality by classification₂Parameter D of_mAnd P_d。

The invention has the beneficial effects that:

(1) according to the invention, the porosity, permeability, oil saturation and sand adding amount of the reservoir stratum are considered, and a yield splitting principle of a qualified stratum is established; statistical analysis is carried out on the oil testing and production testing data in the research area, and the classification standard of the dessert in the compact oil reservoir in the northern part of the Songliao basin is determined according to the oil production intensity.

(2) The invention comprehensively considers the oil storage capacity, the seepage capacity and the engineering fracturing difficulty of the reservoir, constructs three quality factors of the macroscopic quality, the micro pore structure quality and the engineering quality of the reservoir, establishes a three-dimensional compact oil reservoir dessert classification chart and a Fisher discrimination formula, improves the precision rate of compact oil reservoir dessert classification, and provides accurate technical support for compact oil dessert optimization and fracturing construction design.

(3) The invention consists of nuclear magnetic resonance T_2GMThe method calculates the characteristic parameters of the pore structure of the reservoir layer in a classification way, and avoids the reservoir layer with the medium and small pore structures from nuclear magnetism T₂The error generated when the spectrum is converted into the pseudo capillary pressure improves the calculation precision of the pore structure characteristic parameters of the compact oil reservoir, and provides guarantee for the accuracy rate of the 'sweet spot' of the reservoir.

Drawings

FIG. 1 is a graph of reservoir parameters versus oil recovery strength.

FIG. 2 is a classification evaluation standard of tight oil reservoir logging parameters.

Figure 3 is a cross-plot of reservoir macroscopic quality versus microscopic pore structure quality.

Figure 4 reservoir macroscopic quality vs. engineering quality cross-plot.

Figure 5 intersection of reservoir micro-pore structure quality with engineering quality.

FIG. 6 three-dimensional compact oil reservoir "sweet spot" classification chart.

FIG. 7 is a schematic diagram of a classification of pore structures.

FIG. 8 NMR T of a sample₂Distribution and mercury intrusion pore throat radius distribution are compared.

FIG. 9 Nuclear magnetic experiment T_2GMAnd the characteristic parameter relation graph of the pore structure.

FIG. 10 is a test chart of the "sweet spot" classification results for a single-layer fracturing well.

FIG. 11 is a comprehensive diagram of well logging interpretation of a test well A in Daqing oil field.

FIG. 12 is a classification chart of the tight oil reservoir in the northern part of Songliao basin.

FIG. 13 is a classification chart of dense oil "sweet spots" in the North of the Songliao basin.

In fig. 1: (a) porosity and oil recovery strength; (b) oil saturation and oil recovery intensity; (c) permeability and oil recovery strength; (d) average pore throat radius and oil extraction strength; (e) drainage pressure and oil recovery strength; (f) brittleness index and oil recovery strength; (g) burst pressure and oil recovery strength.

In fig. 2: (a) a porosity classification standard; (b) oil saturation classification standard; (c) a permeability classification standard; (d) classifying standard of pore throat radius mean; (e) a displacement pressure classification standard; (f) a brittleness index classification standard; (g) burst pressure classification criteria.

In FIG. 9: (a) nuclear magnetic experiment T_2GMAnd the radius mean; (b) nuclear magnetic experiment T_2GMAnd the displacement pressure.

Detailed Description

In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to line graphs, distribution diagrams, tables and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

A classification method for compact oil reservoir desserts in the north of Songliao basin comprises the following steps:

s1: the oil extraction intensity calculation method and the classification standard of compact oil dessert are determined: the oil production quantity of each meter of the reservoir, namely the oil production intensity, reflects the oil production capacity of the reservoir, and the category of the reservoir can be divided according to the oil production intensity.

S1.1: the method is characterized in that an oil layer group at the middle upper part in the north of the Songliao basin generally develops a thin-layer and sand shale thin interaction layer, oil testing is mainly performed by a multi-layer combined test, in order to evaluate the oil production capacity of a single layer, the productivity needs to be reasonably split, in order to eliminate the different influences of different well fracturing processes, the sand adding amount is considered and then combined with reservoir quality parameters, and a single-layer productivity evaluation index M is established according to the oil testing data and well logging parameters of multiple wells in a research area_i(formula 1) based thereon, toSplitting the well productivity to obtain the single-layer oil extraction strength Q_si(formula 2) in the above-mentioned manner,

wherein Q is the total yield of the well combining test, h_iIs the thickness of the ith layer, phi_iPorosity of the i-th layer, K_iPermeability of the i-th layer, S_oiIs the oil saturation of the i-th layer, V_sThe total sand adding amount is calculated;

s1.2: according to the regional situation, reservoir dessert is divided into three categories according to the oil extraction intensity:

class I-1 "dessert": the improved oil recovery has high yield and the oil recovery strength is more than 0.3 t/d/m;

class I-2 "dessert": the productivity is obviously improved after the transformation, and the oil extraction strength is between 0.03 and 0.3 t/d/m;

class II "dessert": the productivity is not obviously improved after the transformation, the further research is needed, and the oil extraction strength is less than 0.03 t/d/m;

s2: a compact oil reservoir dessert classification well logging characterization method comprises the following steps: after the oil extraction intensity is used for determining the reservoir dessert classification standard, in order to achieve the purpose of reservoir dessert classification evaluation, the well logging characterization of the reservoir dessert classification is required to be achieved;

s2.1: according to the oil testing and well logging information of 6 single test layers of 5 wells in the research area, the correlation between each reservoir parameter and the oil recovery intensity is inspected, and the preferred oil recovery intensity sensitive parameters comprise porosity, oil saturation, permeability, pore throat radius mean value, displacement pressure and brittleness index (under the laboratory condition, the peak intensity tau of a difference strain experiment is usually adopted_pAnd residual intensity τ_rEstablishing a brittleness index, wherein the rock brittleness index BI ═ tau_p-τ_r)/τ _p100 log calculations from mineralogical or elastic parameters) and fracture pressure, where linearity of each sensitive parameter with production intensityThe relationship is shown in FIG. 1; the linear relation between each sensitive parameter and the oil extraction intensity is used as the basis information for researching the subsequent development well in the area;

s2.2, selecting 7 shut-in pressure oil testing wells in the research area, calculating the oil production intensity of 49 layers according to the split principle, and intersecting each productivity sensitive parameter with the oil production intensity, wherein the classification evaluation standard of each reservoir logging parameter of different dessert categories can be obtained as shown in figure 2 and is shown in table 1. Considering that the variation intervals of the productivity sensitive logging parameters are different in size and influence on productivity is different, the oil production intensity sensitive parameters are normalized, and then the reservoir macroscopic quality RQ is constructed by the normalized seven oil production intensity sensitive parameters from the aspects of macroscopicity, microcosmicity and engineering₁Micro-pore structure quality RQ₂And three quality factors of engineering quality CQ:

RQ₁＝φ*S_o(formula 4)

Wherein K is the air permeability, D_mMean pore throat radius, P_dFor displacement pressure, BRIT is brittleness index, FPG is fracture;

TABLE 1 Classification evaluation standard table for logging parameters of tight oil reservoir

The "dessert" category

Porosity of

Degree of saturation of oil

Permeability rate of penetration

Mean value of radius

Displacement pressure

Index of brittleness

Burst pressure

'dessert of class I-1'

≥11

≥50

≥0.3

≥0.3

≤1

≥56

≤35

'dessert of class I-2'

[8,11)

[30,50)

[0.06,0.3)

[0.1,0.3)

(1,4]

[47,56)

(35,40]

"dessert" of class II "

<8

<30

<0.06

<0.1

>4

<47 or>60

>40

However, the three quality factors are intersected with each other, as shown in fig. 3-5, three types of reservoir "sweet spots" cannot be well distinguished, and a triaxial space intersection chart is established by the three quality factors, as shown in fig. 6, so that the three types of reservoir "sweet spots" can be well identified. And establishing a distinguishing expression of three types of reservoir dessert by adopting a Fisher distinguishing method:

it should be noted that: the Fisher discriminant method is a projection method, and projects points in a high-dimensional space to a low-dimensional space. In the original coordinate system, the sample can be difficult to separate, and the difference can be obvious after projection. In general, it can be projected onto one dimension (straight line) first, if the effect is not ideal, onto another straight line (thus forming a two-dimensional space), and so on. Each projection may establish a discriminant function.

In the invention, if the three-axis space intersection plate with three quality factors is difficult to observe and judge, a Fisher discrimination method can be adopted: specifically, the parameters are substituted into the three expressions to obtain Y₁、Y₂、Y₃Which value is the highest, which type of "sweet spot" is determined. Wherein Y is₁、Y₂And Y₃The discrimination formulas of the type I-1 dessert, the type I-2 dessert and the type II dessert respectively show that the discrimination rate of the 49 sample points is 45/49-91.8%.

As the compact oil reservoir in the Songliao basin generally contains mud and calcium, has poor porosity and permeability and complex pore structure, the pore structure is an important factor for controlling the fluid distribution of the compact oil-gas reservoir and has important influence on the liquid production property, the capacity and the well logging electrical characteristics of the reservoir. Therefore, the research on the pore structure characteristics of the compact oil reservoir must be carried out, and the characteristic parameters of the micro pore structure must be accurately obtained. The invention accurately obtains the micro-pore structure product of the reservoirThe method of the quality parameter is as follows: using nuclear magnetic T_2GMRQ for calculating reservoir micro pore structure quality by classification₂Parameter D of_mAnd P_d(ii) a According to the results of the nuclear magnetism and mercury pressure joint measurement experiment of 42 samples of 4 wells, the pore structures of the reservoir can be divided into three types by pores and seepage: large pore structure, medium pore structure and small pore structure, as shown in fig. 7. The reservoir in the local area is mainly of a medium pore structure and a small pore structure, the nuclear magnetic logging information of 3 wells in the research area is processed by a conventional pseudo capillary pressure method, and the calculated micro pore structure parameters have larger error compared with mercury intrusion information, as shown in figure 8 and table 2. In order to avoid the error generated when converting nuclear magnetic T2 spectrum to false capillary pressure, a method for directly utilizing nuclear magnetic resonance T is provided_2GMThe method (formulas 8 and 9) for calculating the micro-pore structure parameters such as the average radius of the pore throat, the expulsion pressure and the like improves the calculation precision of the micro-pore structure parameters, as shown in fig. 9 and table 2.

D_{m is small}＝0.0085*T_2GM+0.0055 P_{d is small}＝10.668*T_2GM ^-0.5432(formula 8)

D_{m in}＝0.0245*T_2GM+0.0123 P_{In d}＝7.207*T_2GM ^-1.0248(formula 9)

Wherein: d_{m is small}、D_{m in}、P_{d is small}、P_{In d}The average value of pore throat radius and displacement pressure of a small pore structure and a medium pore structure respectively; t is_2GMThe geometric mean value of the nuclear magnetic experiment.

TABLE 2 statistical table of micro-pore structure parameter calculation errors

In general, for nuclear magnetic logging, it can be based on nuclear magnetic T₂The spectrum is calculated to obtain T_2GMBut the T obtained from logging is affected by the resolution of the measuring instrument, the accuracy of the measurement and the presence of hydrocarbons in the pores_{2GM assay}T obtained by nuclear magnetic experiment_2GMThere is a large difference. Therefore, the T is fitted under two conditions of the magnetic logging data with or without the nuclear core_2GM：

① nuclear magnetic logging data:

T_{2GM is small}＝-0.0801+0.0275*T_{2GM assay}-0.1666 log (. DELTA.por) +551.3272 DT/GR/GR (formula 10)

T_{In 2GM}＝-4.0806+0.0405*T_{2GM assay}+2.6426 log (. DELTA.por) +2.0125 10000 DEN/GR/GR (formula 11)

② non-nuclear magnetic logging data:

T_{2GM is small}= 0.4766+0.1456 log (Δ por) +646.4483 DT/GR (formula 12)

T_{In 2GM}-3.9668+2.3111 log (Δ por) +2.6084 10000 DEN/GR (formula 13)

Wherein: t is_{2GM is small}、T_{In 2GM}Is a simulated nuclear magnetic experiment T with a small pore structure and a middle pore structure_2GMAnd delta por is a neutron and density porosity difference value, DT is an acoustic wave time difference value, DEN is a compensation density value, and GR is a natural gamma value.

The application effect of the invention is as follows:

by applying the seven-property logging parameter evaluation method and the dessert classification standard of the compact oil reservoir, 36 newly drilled wells and more than one hundred old wells in the research area are classified and evaluated, more than 300I-type dessert sections are provided, and a better application effect is achieved.

The reservoir was evaluated by classification using oil testing and conventional well logging data of six single test zones of 5 wells in the study area, and the classification results of the reservoir "sweet spots" are shown in fig. 10 and table 3. Fisher discriminant analysis shows that the six single-layer wells of the 5 wells are all I-1 type dessert, and the three quality calculation results fall into an I-1 type dessert area and are matched with the oil testing conclusion, so that the feasibility of the reservoir dessert classification method is verified.

TABLE 3 examination form of classification results of single-layer fracturing test oil well

Number of well	Layer number	Capacity of production	Strength of oil recovery	Fisher discrimination result
					Well
1	86	0.82	0.43	"dessert" of class I-1 "
					Well 1	90	1.28	0.65	"dessert" of class I-1 "
Well 2	85	6.56	2.98	"dessert" of class I-1 "
					Well 3	90	3.77	2.09	"dessert" of class I-1 "
Well 4	56	1.19	0.43	"dessert" of class I-1 "
					Well 5	50	6.252	2.08	"dessert" of class I-1 "

A new oil test A well in a research area is laminated and pressed at a depth section of 2120.0-2274.8 m10, and after pressing, a water pump discharges daily oil for 6.16t, namely an industrial oil layer, as shown in figure 11. The single-layer oil extraction strength is calculated according to the productivity split principle, and the oil extraction is classified according to the compact oil reservoir dessert classification standard, wherein the layers 136 II, 142 II, 153 III and 155 are compact oil I-1 dessert, the layers 151I, 170 II and 179 are compact oil I-2 dessert, and the layers 136I, 136 III and 170I are compact oil II dessert, which is shown in Table 4. According to the former porosity (phi) -pore structure indexAs shown in fig. 12 and table 4, layer No. 155 is interpreted as a tight oil i-2 layer due to its general physical properties, and does not match the test results. And the calculated three quality factors of the reservoir fall into a three-dimensional compact oil reservoir dessert classification chart, and through Fisher discriminant analysis, the dessert classification results of 155 layers are all consistent with the test oil splitting results (see figure 13 and table 4), and the accuracy of dessert classification by the three quality factors is verified again.

TABLE 4 comparison of the results of the A-reservoir classification and the "sweet spot" classification for the test well

(1) The porosity, permeability, oil saturation and sand adding amount of a reservoir are considered, and a yield splitting principle of a qualified stratum is established; statistical analysis is carried out on the oil testing and production testing data in the research area, and the classification standard of the dessert in the compact oil reservoir in the northern part of the Songliao basin is determined according to the oil production intensity.

(2) The method comprehensively considers the oil storage capacity, the seepage capacity and the engineering fracturing difficulty of the reservoir, constructs three quality factors of the macroscopic quality, the micro-pore structure quality and the engineering quality of the reservoir, establishes a three-dimensional compact oil reservoir dessert classification chart and a Fisher discrimination formula, and improves the accuracy of the dessert classification of the compact oil reservoir.

(3) From nuclear magnetism T_2GMThe method calculates the characteristic parameters of the pore structure of the reservoir layer in a classification way, and avoids the reservoir layer with the medium and small pore structures from nuclear magnetism T₂The error generated when the spectrum is converted into the pseudo capillary pressure improves the calculation precision of the pore structure characteristic parameters of the compact oil reservoir.