CN114021804B - Construction method of fault-lithology oil and gas reservoir oil and gas reserve prediction model - Google Patents

Abstract

A construction method of a fault-lithology oil and gas reservoir oil and gas reserve prediction model. The method comprises the following steps: (1) determining reservoir formation influencing factors representing the fault-lithologic hydrocarbon reservoir; (2) calculating a weighting coefficient of the oil and gas reserves of the fault-lithologic oil and gas reservoir, and a quality relation and a weighting coefficient of each reservoir forming influence factor; (3) establishing a comprehensive prediction model of the oil and gas reserves based on the determined dominant reservoir forming influence factors, and estimating a model regression coefficient by using a least square method; (4) calculating a judgment index to test the prediction model, gradually increasing the number of dominant influence factors, and selecting the judgment index R²And the model corresponding to the minimum time is a reserve prediction model of the final fault-lithology oil and gas reservoir. The model constructed by the method of the invention not only can effectively predict the oil-gas reserves of the fault-lithology oil-gas reservoir, has small error of the prediction result and high accuracy, but also overcomes the defects of subjective influence of artificial weighted values and less drilling data, and reduces the experimental workload.

Description

A method for constructing oil and gas reserves prediction model of fault-lithologic oil and gas reservoirs

Technical field:

The invention relates to the technical field of oil and gas exploration, in particular to a method for how to construct a prediction model of oil and gas reserves.

Background technique:

Fault-lithologic reservoirs refer to oil and gas reservoirs formed by the dual factors of faults and lithology. During the rifting period of tectonic evolution, continental basins generally experience the stages of rapid basement subsidence, expansion of lake basin area, and rapid deepening of water bodies, which greatly promotes the development of high-quality lacustrine source rocks and deep delta sand body assemblages on the edge of lake basins. Generally speaking, the deep delta sand bodies developed on the edge of the lake basin have good reservoir properties, because the uplift around the lake basin has been weathered and denuded for a long time, and it is a good large-scale provenance area, which can be the sand body of the downturn side of the uplift. At the same time, the development of the steep slope break at the basin margin also provides a place for the deposition of clastic materials in the downturn, which is conducive to the formation of self-generation and self-storage type in-source oil and gas reservoirs. Since the Cenozoic, under the influence of tectonic activities, a series of oil source faults have been formed that connect the sand bodies in the shallow and deep source rocks. The accumulation characteristics and distribution law of . When the deep source rock matures and reaches the hydrocarbon generation threshold, hydrocarbon expulsion begins. Under the charging of the high-strength source rock hydrocarbon generation system, on the one hand, oil and gas migrate and accumulate directly from the source rock to the inner source sand body; , oil and gas migrate to shallow layers through oil source faults running through deep and shallow layers and accumulate to accumulate. Fault-lithologic reservoirs are an important way of accumulation under the coupling action of structure and lithology. At present, there is no technical scheme for directly determining the dominant influencing factors of fault-lithologic reservoirs and predicting oil and gas reserves in the open literature. The reserve prediction model of similar oil and gas reservoirs (such as buried hill oil and gas reservoirs) that can be used as a reference relies on expert judgment for weighting. This reserve prediction model does not make full use of objective information, resulting in strong human subjectivity and contingency, and lack of objectivity. , it was found to be inapplicable after experimental application to the prediction of oil and gas reserves in fault-lithologic reservoirs.

Invention content:

In order to solve the technical problems mentioned in the background art, the present invention provides a method for constructing a prediction model for oil and gas reserves of fault-lithologic oil and gas reservoirs. The oil and gas reserves prediction model constructed by this method has high prediction accuracy and strong applicability. specialty.

The technical scheme of the present invention is: the construction method of a fault-lithologic oil and gas reservoir oil and gas reserves prediction model, characterized in that the method comprises the following steps:

In the first step, according to the geological data of the fault-lithologic reservoir in the study area, and based on the three aspects of hydrocarbon generation capacity, storage capacity, and migration capacity, determine the influencing factors for the formation of fault-lithologic oil and gas reservoirs. The factors include hydrocarbon generation intensity of source rock, active rate of oil source fault, sand body porosity, sand body permeability, sand body area, sand body thickness and fault-sand body contact length;

The second step is to determine n fault-lithologic reservoir objects involved in the prediction of oil and gas reserves, and use formula ① to calculate the weighting coefficient of oil and gas reserves of each fault-lithologic reservoir:

Among them, n is the number of fault-lithologic reservoirs, which is a positive integer; i is the serial number of fault-lithologic reservoirs, which ranges from 1 to n and is an integer; δ _i is the ith fault - Weighting coefficient of oil and gas reserves of lithologic oil and gas reservoirs; Qi is the oil and gas reserves of _i -th fault-lithologic oil and gas reservoirs.

The third step is to determine and rank the correlation between the factors influencing the formation of fault-lithologic oil and gas reservoirs and the oil and gas reserves of the oil and gas reservoir. This step is carried out according to the following paths:

(1) The oil and gas reserves and accumulation influencing factors of fault-lithologic oil and gas reservoirs are used as parameters to characterize the system characteristics, and the parameters include the system characteristic reference series and the system factor comparison series; the system characteristic reference series consists of a series of faults -The composition of oil and gas reserves of lithologic oil and gas reservoirs, expressed as Q ₁ , Q ₂ , Q ₃ ...Q _i , abbreviated as {Q _i }, where i=1, 2... It consists of a series of fault-lithologic reservoir-forming factors, expressed as X ₁₁ , X ₁₂ , X ₁₃ ... X _ji , abbreviated as {X _ji }, where j=1,2...7 , i=1,2...n, X _ji represents the value of the i-th fault-lithologic reservoir in the j-th systematic factor comparison sequence, among which, the hydrocarbon generation intensity of the source rock and the activity rate of the oil source fault , sandstone body porosity, sandstone body permeability, sandstone body area, sandstone body thickness, and fault-sandbody contact length are recorded as {X _1i }, {X _2i }, {X _3i }, {X _4i }, {X , respectively _5i }, {X _6i }, {X _7i };

(2) carrying out dimensionless processing on the system factor comparison sequence and the system feature reference sequence using formula ② and formula ③ respectively;

是第j个系统因素比较数列中的第i个断层-岩性油气藏归一化后的数值，

是第j个系统因素的平均值，

是第i个断层-岩性油气藏油气储量归一化后的数值，

是所有断层-岩性油气藏油气储量的平均值；In the formula,

is the normalized value of the i-th fault-lithologic reservoir in the j-th systematic factor comparison sequence,

is the mean of the jth systematic factor,

is the normalized value of the oil and gas reserves of the i-th fault-lithologic reservoir,

is the average value of oil and gas reserves of all fault-lithologic reservoirs;

After dimensionless processing, a new series is obtained, including oil and gas reserves, hydrocarbon generation strength of source rocks, activity rate of oil source faults, sand body porosity, sand body permeability, sand body area, sand body thickness and fault-sand body contact Length sequence, denoted as

(3) Determine the correlation coefficient β between the reference sequence of system characteristics (that is, the sequence of oil and gas reserves of fault-lithologic reservoirs) and the sequence of comparison of systematic factors (that is, sequence of each accumulation influencing factor) according to formula (4); then, according to formula ⑤ Calculate the degree of association γ, and sort the degree of association in descending order:

The correlation coefficient is:

表示系统参考特征数列

中第i个数值与第j个系统因素比较数列

中第i个数值的绝对差，而

则表示绝对差序列中的最小值，

则表示绝对差序列中的最大值；In the formula, ρ is the resolution coefficient, usually ρ=0.5, i=1,2...n,j=1,2,3...7,

Represents the system reference characteristic sequence

The i-th value in compares the sequence with the j-th systematic factor

The absolute difference of the ith value in , and

then represents the minimum value in the sequence of absolute differences,

represents the maximum value in the absolute difference sequence;

The correlation degree is: γ(j)=δ ₁ ×β(Q ₁ , X _j1 )+δ ₂ ×β(Q ₂ , X _j2 )+…+δ _i ×β(Q _i , X _ji ) ⑤

The fourth step is to calculate the weight coefficient of each reservoir-forming factor. This step is carried out according to the following path:

(1) Use formula ⑥ to normalize the influencing factors with different dimensions and magnitudes:

In the formula, X _ji represents the value of the i-th fault-lithologic reservoir in the j-th systematic factor comparison sequence, min(X _ji ) represents the minimum value among all the accumulation influencing factors of each oil and gas reservoir, and max( X _ji ) represents the maximum value among all the accumulation influencing factors of each oil and gas reservoir, and X′ _ji represents the value after unified normalization of all the accumulation influencing factors of each oil and gas reservoir;

(2) Use formula ⑦ to calculate the entropy value of each reservoir-forming factor for the normalized value:

In the formula, B _j is the entropy value of the jth accumulation influencing factor of the fault-lithologic reservoir;

(3) Calculate the weight coefficient of each influencing factor in turn according to formula ⑧:

In the formula, C _j is the weight coefficient of the jth accumulation influencing factor of the fault-lithologic reservoir;

The fifth step, according to the correlation degree obtained in the third step, select the first m reservoir-forming influencing factors with high correlation degree as the dominant influencing factors. The index data is corrected to establish a comprehensive prediction model for the oil and gas reserves of several fault-lithologic reservoirs. The initial value of m is 3, and the subsequent values are 4, 5, 6 and 7 in turn.

This step is carried out according to the following path:

(1) On the premise of known screening of m highly correlated reservoir-forming factors and their weight coefficients, a comprehensive prediction model for oil and gas reserves of fault-lithologic oil and gas reservoirs is established according to Equation 9:

表示第i个断层-岩性油气藏油气储量的计算值；X′_ji为步骤四所述的“各油气藏所有成藏影响因素统一归一化处理后的数值”，其中，未被选为优势影响因素的指标不参与计算；C_j为是断层-岩性油气藏第j个成藏影响因素的权重系数；k_j是模型的回归系数；In the formula,

Represents the calculated value of oil and gas reserves of the i-th fault-lithologic reservoir; X′ _ji is the “value after unified normalization of all the accumulation influencing factors of each oil and gas reservoir” described in step 4, among which, is not selected as The index of the dominant influencing factor is not involved in the calculation; C _j is the weight coefficient of the jth accumulation influencing factor of the fault-lithologic reservoir; k _j is the regression coefficient of the model;

(2) The least squares method is used to determine the model regression coefficient of the constructed comprehensive prediction model; this step can be carried out in SPSS software to obtain the regression coefficients k ₀ , k ₁ , k ₂ . . . k _j and the relationship diagram .

和

The sixth step is to use formula ⑩ to calculate the judgment index when m is 3, 4, 5, 6, and 7 respectively.

and

表示判定指数，其中m表示该判定指数对应的优势影响因素的个数；

表示第i个断层-岩性油气藏油气储量的计算值；Q_i表示第i个断层-岩性油气藏油气储量；

表示所有断层-岩性油气藏油气储量的平均值；In the formula,

Represents the judgment index, where m represents the number of dominant influencing factors corresponding to the judgment index;

Represents the calculated value of the oil and gas reserves of the ith fault-lithologic reservoir; Q _i represents the oil and gas reserves of the ith fault-lithologic reservoir;

Represents the average value of oil and gas reserves of all fault-lithologic reservoirs;

的大小，以判定指数数值最小值所对应的综合预测模型为该地区断层-岩性油气藏的储量预测模型。Step 7: Compare the judgment index calculated in Step 6

The comprehensive prediction model corresponding to the minimum value of the judgment index is the reserve prediction model of the fault-lithologic reservoir in this area.

The present invention has the following beneficial effects: first, the present invention determines the reservoir-forming influencing factors that characterize fault-lithologic oil and gas reservoirs based on the investigation of a large amount of geological data, and determines the weighting coefficients of oil and gas reserves of oil and gas reservoirs by calculating the weighting coefficients. The information provided by the objective data is used to calculate the weight coefficient of each factor influencing the accumulation, which overcomes the shortage of index equalization and expert assignment, removes the subjective influence of human judgment, and then accurately selects the dominant influencing factors of oil and gas accumulation. In the construction of the prediction model of oil and gas reserves, based on the traditional multiple linear regression model, the index data is corrected by using the weight coefficient of each influencing factor, which substantially improves the fitting accuracy of the traditional multiple linear regression model. The judgment index of model accuracy is used to select the most reasonable oil and gas reserves prediction model. The construction of the prediction model under the method of the present invention not only overcomes the disadvantage of less drilling data, but also reduces a large amount of experimental workload, and provides theories and technologies for guiding the oil and gas exploration of fault-lithologic oil and gas reservoirs and improving the success rate of exploration. support. Secondly, the reserves prediction model constructed by this method has been proved through experimental application that the prediction accuracy is high, and it can be effectively applied to the prediction of oil and gas reserves in fault-lithologic reservoirs.

Description of drawings:

Figure 1 shows the relationship between the oil and gas reserves of fault-lithologic reservoirs and the calculated value of oil and gas reserves under the influence of three advantages.

Figure 2 shows the relationship between the oil and gas reserves of fault-lithologic reservoirs and the calculated value of oil and gas reserves under the influence of the four advantages.

Figure 3 shows the relationship between the oil and gas reserves of fault-lithologic reservoirs and the calculated value of oil and gas reserves under the influence of five advantages.

Figure 4 shows the relationship between the oil and gas reserves of fault-lithologic reservoirs and the calculated value of oil and gas reserves under the influence of six advantages.

Figure 5 shows the relationship between the oil and gas reserves of fault-lithologic reservoirs and the calculated value of oil and gas reserves under the influence of seven advantages.

Detailed ways:

The present invention will be further described below in conjunction with the accompanying drawings:

In order to make the purpose, research method and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the examples. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

This study takes the Bohai Sea as the research area, and selects seven typical fault-lithologic reservoirs, namely QHD35-4, BZ1-1, BZ2-1, BZ3-2, CFD6-4, BZ25-1, KL10 -1. Carry out sampling and experimental work for different fault-lithologic oil and gas reservoirs. The sample information is shown in Table 1. The porosity and permeability of the samples are experimentally tested and analyzed. At the same time, the hydrocarbon generation intensity of 7 fault-lithologic reservoirs, the comprehensive core histogram of typical wells (used to determine the thickness of the fault-lithologic reservoir sand body), and the sedimentary facies map and seismic data of the study area were collected and counted. The statistics of geological data show that the main factors affecting the oil and gas reserves of fault-lithologic oil and gas reservoirs include the hydrocarbon generation intensity of source rocks, the activity rate of oil source faults, the porosity of sandstone bodies, the permeability of sandstone bodies, the area of sandstone bodies, the thickness of sandstone bodies and faults. - Sand body contact length. In order to reflect the influence of various factors to the greatest extent, we screened the data in order to eliminate the influence of sample properties as much as possible. It should be noted that the data constructed by Bozhong 28-1 in Table 1 does not participate in the modeling, and the constructed data is used to verify the prediction accuracy of the model.

The specific implementation steps of using the above data to construct a fault-lithologic reservoir oil and gas reserve model are as follows:

In the first step, according to the geological data of the fault-lithologic reservoir in the study area, and based on the three aspects of hydrocarbon generation capacity, storage capacity, and migration capacity, determine the influencing factors for the formation of fault-lithologic oil and gas reservoirs. The factors include hydrocarbon generation intensity of source rock, active rate of oil source fault, sand body porosity, sand body permeability, sand body area, sand body thickness and fault-sand body contact length;

The formula for calculating the hydrocarbon generation strength of the source rock is E=h×ρ×Wc×K ₁ ×K ₂ , where E is the hydrocarbon generation strength of the source rock (kg/m ² ), h is the effective source rock thickness (m); ρ is the Source rock density (10 ⁸ t/km ³ ), Wc is the residual organic carbon mass fraction of source rock (%); K ₁ is the organic carbon recovery coefficient; K ₂ is the organic carbon hydrocarbon production rate (m ³ /t);

The activity rate of the oil source fault refers to the ratio of the drop (m) formed by the tectonic activity of the strata located on the upper wall and the foot wall of the oil source fault to the deposition time (Ma) of the stratum;

Sand body porosity (%) refers to the multi-point sampling of the fault-lithologic reservoir sand body and the porosity experimental test analysis, and the average value of the multiple experimental results is taken as the fault-lithologic reservoir sand body porosity. parameter;

The permeability of sandstone body (mD) refers to the multi-point sampling of fault-lithologic oil and gas reservoir sand body and the permeability experiment test analysis. ;

The area of the sandstone body (km ² ) is to use the seismic sedimentology method to finely describe the plane distribution range of the fault-lithologic oil and gas reservoir sandstone body. Combined with the sedimentary characteristics of the sublacustrine fan and the sedimentary difference of the surrounding mudstone, the distribution range of the sedimentary facies is used to predict the sublacustrine fan. The plane distribution area of the sand body;

The thickness of sandstone body (m) is obtained by accumulating the thicknesses corresponding to the sandstone layers according to the logging data to obtain the thickness of the fault-lithologic reservoir sandstone body;

The fault-sandbody contact length (m) refers to the length of the sand body in the contact part between the sedimentary sand body developed in the downside oil source rock and the oil source fault. The oil source fault and the sedimentary sand body in contact with it are identified by the seismic profile characteristics. And measure the length of the sand body in contact with the oil source fault;

The results obtained from the influencing factors of different fault-lithologic reservoirs are shown in the following table (Table 1):

Table 1 Statistics of influencing factors of different faults-lithologic reservoirs

The second step is to determine n fault-lithologic reservoir objects involved in the prediction of oil and gas reserves, and use formula ① to calculate the weighting coefficient of oil and gas reserves of each fault-lithologic reservoir:

Among them, n is the number of fault-lithologic reservoirs, which is a positive integer; i is the serial number of fault-lithologic reservoirs, which ranges from 1 to n and is an integer; δ _i is the ith fault - Weighting coefficient of oil and gas reserves of lithologic oil and gas reservoirs; Qi is the oil and gas reserves of _i -th fault-lithologic oil and gas reservoirs.

Using formula ①, the weighting coefficients of the oil and gas reserves of the seven fault-lithologic reservoirs are calculated as:

The third step is to determine and rank the correlation between the factors influencing the formation of fault-lithologic oil and gas reservoirs and the oil and gas reserves of the oil and gas reservoir. This step is carried out according to the following paths:

(1) The oil and gas reserves and accumulation influencing factors of fault-lithologic oil and gas reservoirs are used as parameters to characterize the system characteristics, and the parameters include the system characteristic reference series and the system factor comparison series; the system characteristic reference series consists of a series of faults -The composition of oil and gas reserves of lithologic oil and gas reservoirs, expressed as Q ₁ , Q ₂ , Q ₃ ...Q _i , abbreviated as {Q _i }, where i=1, 2... It consists of a series of fault-lithologic reservoir influencing factors, expressed as X ₁₁ , X ₁₂ , X ₁₃ ... X _ji , abbreviated as {X _ji }, where j=1,2...7 , i=1,2...n, X _ji represents the value of the i-th fault-lithologic reservoir in the j-th systematic factor comparison sequence, among which, the hydrocarbon-generating intensity of the source rock and the activity rate of the oil-source fault , sandstone body porosity, sandstone body permeability, sandstone body area, sandstone body thickness, and fault-sandbody contact length are recorded as {X _1i }, {X _2i }, {X _3i }, {X _4i }, {X , respectively _5i }, {X _6i }, {X _7i };

The oil and gas reserves of fault-lithologic reservoirs, the intensity of hydrocarbon generation of source rocks, the activity rate of oil source faults, the porosity of sandstone bodies, the permeability of sandstone bodies, the area of sandstone bodies, the thickness of sandstone bodies, and the contact length of faults and sand bodies are recorded as :

{Q _i }={716.12, 587.52, 832.08, 1246.72, 6057.63, 16601.57, 17995.54}

{X _1i }={2428.61, 3966.55, 6990.3, 4962.5, 2008, 8644, 8240}

{X _2i }={90.27, 75.82, 83.05, 77.41, 120.89, 141.23, 115.73}

{X _3i }={12.5, 13.77, 13.41, 13.88, 14.01, 14.32, 22.6}

{X _4i }={23.42, 19.36, 18.45, 15.98, 18.34, 20.12, 19.11}

{X _5i }={11.38, 4.84, 4.84, 10.92, 14.81, 15.08, 12.33}

{X _6i }={64.2, 5.47, 68.16, 26.7, 133.3, 122.25, 264.5}

{X _7i }={4.52, 3.51, 1.45, 3.25, 9.39, 10.91, 8.23}

(2) carrying out dimensionless processing on the system factor comparison sequence and the system feature reference sequence using formula ② and formula ③ respectively;

是第j个系统因素比较数列中的第i个断层-岩性油气藏归一化后的数值，

是第j个系统因素的平均值，

是第i个断层-岩性油气藏油气储量归一化后的数值，

是所有断层-岩性油气藏油气储量的平均值；In the formula,

is the normalized value of the i-th fault-lithologic reservoir in the j-th systematic factor comparison sequence,

is the mean of the jth systematic factor,

is the normalized value of the oil and gas reserves of the i-th fault-lithologic reservoir,

is the average value of oil and gas reserves of all fault-lithologic reservoirs;

After dimensionless processing, a new series is obtained, including oil and gas reserves, hydrocarbon generation strength of source rocks, activity rate of oil source faults, sand body porosity, sand body permeability, sand body area, sand body thickness and fault-sand body contact Length sequence, denoted as

Use the formula ② to perform dimensionless processing on the comparison series of systematic factors. Taking the influencing factor of sandstone body porosity as an example, the specific calculation process is as follows:

Therefore, the obtained porosity of the new sandstone body after dimensionless treatment is as follows:

Similarly, according to the calculation method of formula ②, the new numbers after dimensionless processing are as follows:

Hydrocarbon generation intensity of source rock:

Activity rate of oil source fault:

Sand body porosity:

Sand body permeability:

Sandstone body area:

Sandstone body thickness:

Fault-sandbody contact length series:

Similarly, the new number of oil and gas reserves after dimensionless treatment obtained according to the calculation method of formula ③ is:

(3) Determine the correlation coefficient β between the reference sequence of system characteristics (that is, the sequence of oil and gas reserves of fault-lithologic reservoirs) and the sequence of comparison of systematic factors (that is, sequence of each accumulation influencing factor) according to formula (4); then, according to formula ⑤ Calculate the degree of association γ, and sort the degree of association in descending order:

The correlation coefficient is:

表示系统参考特征数列

中第i个数值与第j个系统因素比较数列

中第i个数值的绝对差，而

则表示绝对差序列中的最小值，

则表示绝对差序列中的最大值；In the formula, ρ is the resolution coefficient, usually ρ=0.5, i=1,2...n,j=1,2,3...7,

Represents the system reference characteristic sequence

Compare the i-th value with the j-th systematic factor in the sequence

The absolute difference of the ith value in , and

then represents the minimum value in the sequence of absolute differences,

represents the maximum value in the absolute difference sequence;

The correlation degree is: γ(j)=δ ₁ ×β(Q ₁ , X _j1 )+δ ₂ ×β(Q ₂ , X _j2 )+…+δ _i ×β(Q _i , X _ji ) ⑤

According to formula ④, the correlation coefficient β between the reference series of system characteristics (i.e. the series of oil and gas reserves of fault-lithologic reservoirs) and the series of comparison of systematic factors (i.e. series of factors influencing the formation of reservoirs) is calculated. Taking the influencing factors as an example, the specific calculation process is as follows:

Then, according to formula ⑤, the correlation γ between the influence factor of sandstone porosity and the oil and gas reserves of fault-lithologic oil and gas reservoirs is calculated as:

γ(3)=δ ₁ ×β(Q ₁ , X ₃₁ )+δ ₂ ×β(Q ₂ , X ₃₂ )+…+δ ₇ ×β(Q ₇ , X ₃₇ )

=0.016×0.55+0.013×0.52+0.018×0.54+0.028×0.55+0.137×1+0.376×0.34+0.409×0.39

=0.46

Similarly, according to formula ④ and formula ⑤, the hydrocarbon generation strength of source rock, the active rate of oil source fault, the porosity of sandstone body, the permeability of sandstone body, the area of sandstone body, the thickness of sandstone body, and the contact length of fault-sand body and the fault- The correlation degrees of oil and gas reserves in lithologic reservoirs are:

Hydrocarbon generation intensity of source rock: γ(1)=0.48

Activity rate of oil source fault: γ(2)=0.52

Sandstone body porosity: γ(3)=0.46

Permeability of sandstone body: γ(4)=0.71

Sandstone body area: γ(5)=0.78

Thickness of sandstone body: γ(6)=0.55

Fault-sandbody contact length: γ(7)=0.32

The influencing factors of correlation degree from high to low are: sandstone body area, sandstone body permeability, sandstone body thickness, active rate of oil source faults, source rock hydrocarbon generation intensity, sandstone body porosity, and fault-sandbody contact length.

The fourth step is to calculate the weight coefficient of each reservoir-forming factor. This step is carried out according to the following path:

(1) Use formula ⑥ to normalize the influencing factors with different dimensions and magnitudes:

In the formula, X _ji represents the value of the i-th fault-lithologic reservoir in the j-th systematic factor comparison sequence, min(X _ji ) represents the minimum value among all the accumulation influencing factors of each oil and gas reservoir, and max( X _ji ) represents the maximum value of all the accumulation influencing factors of each oil and gas reservoir, and X′ _ji represents the value after unified normalization of all the accumulation influencing factors of each oil and gas reservoir;

According to formula ⑥, the influencing factors with different dimensions and orders of magnitude are normalized. Taking the influencing factor of sandstone body porosity as an example, the specific calculation process is as follows:

(2) Use formula ⑦ to calculate the entropy value of each reservoir-forming factor for the normalized value:

In the formula, B _j is the entropy value of the jth accumulation influencing factor of the fault-lithologic reservoir;

For the normalized values, the entropy value of each reservoir-forming factor is calculated by formula ⑦, taking the influence factor of sandstone porosity as an example, the specific calculation process is as follows:

Similarly, the entropy values of source rock hydrocarbon generation intensity, oil source fault activity rate, sandstone body porosity, sandstone body permeability, sandstone body area, sandstone body thickness, and fault-sandbody contact length can be obtained by formula ⑦, respectively: :

Hydrocarbon generation intensity of source rock: B ₁ =0.82

Activity rate of oil source fault: B ₂ =0.18

Sand body porosity: B ₃ =0.036

Permeability of sandstone body: B ₄ =0.045

Sandstone body area: B ₅ =0.025

Sandstone body thickness: B ₆ =0.17

Fault-sand body contact length: B ₇ =0.013

(3) Calculate the weight coefficient of each influencing factor in turn according to formula ⑧:

In the formula, C _j is the weight coefficient of the jth accumulation influencing factor of the fault-lithologic reservoir;

Calculate the weight coefficient of each influencing factor according to formula ⑧, taking the influencing factor of sandstone body porosity as an example, the specific calculation process is as follows:

Similarly, formula ⑧ is used to obtain the weight coefficients of source rock hydrocarbon generation intensity, oil source fault activity rate, sand body porosity, sand body permeability, sand body area, sand body thickness and fault-sand body contact length, respectively: :

Hydrocarbon generation intensity of source rock: C ₁ =0.032

Activity rate of oil source fault: C ₂ =0.143

Sand body porosity: C ₃ =0.168

Sand body permeability: C ₄ =0.167

Sandstone body area: C ₅ =0.170

Sandstone body thickness: C ₆ =0.145

Fault-sand body contact length: C ₇ =0.172

The fifth step, according to the correlation degree obtained in the third step, select the first m reservoir-forming influencing factors with high correlation degree as the dominant influencing factors. The index data is corrected to establish a comprehensive prediction model for the oil and gas reserves of several fault-lithologic reservoirs. The initial value of m is 3, and the subsequent values are 4, 5, 6 and 7 in turn.

This step is carried out according to the following path:

(1) On the premise of known screening of m highly correlated reservoir-forming factors and their weight coefficients, a comprehensive prediction model for oil and gas reserves of fault-lithologic oil and gas reservoirs is established according to Equation 9:

表示第i个断层-岩性油气藏油气储量的计算值；X′_ji为步骤四所述的“各油气藏所有成藏影响因素统一归一化处理后的数值”，其中，未被选为优势影响因素的指标不参与计算；C_j为是断层-岩性油气藏第j个成藏影响因素的权重系数；k_j是模型的回归系数；In the formula,

Represents the calculated value of oil and gas reserves of the i-th fault-lithologic reservoir; X′ _ji is the “value after unified normalization of all accumulation-influencing factors of each oil and gas reservoir” described in step 4, among which, is not selected as The index of the dominant influencing factor is not involved in the calculation; C _j is the weight coefficient of the jth accumulation influencing factor of the fault-lithologic reservoir; k _j is the regression coefficient of the model;

(2) Use the least square method to determine the model regression coefficient of the constructed comprehensive prediction model; this step can be carried out in SPSS software to obtain the regression coefficients k ₀ , k ₁ , k ₂ . . . k _j of the model and the relationship diagram .

When m=3, according to the ranking result of the correlation degree in the third step, the sandstone body area, sandstone body permeability, and sandstone body thickness are selected as the dominant influencing factors, and the comprehensive prediction of oil and gas reserves of fault-lithologic reservoirs is established according to formula 9. The model is:

The normalized value of the hydrocarbon accumulation influencing factors calculated in the fourth step (1) is put into formula ⑨, and the regression coefficients k ₀ , k ₅ , k ₄ , and k of the model are calculated by SPSS software. ₆ and the relationship diagram (Fig. 1), respectively: k ₀ =-31.66, k ₅ =20618.2, k ₄ =398021.6, k ₆ =511554.8;

Therefore, the obtained comprehensive prediction model of oil and gas reserves in fault-lithologic reservoirs is:

When m=4, according to the ranking result of the correlation degree in the third step, the sandstone body area, sandstone body permeability, sandstone body thickness, and the active rate of oil source faults are selected as the dominant influencing factors, and the fault-lithology is established according to formula ⑨ The comprehensive prediction model of oil and gas reserves in oil and gas reservoirs is:

The normalized value of the hydrocarbon accumulation influencing factors calculated in the fourth step (1) is put into formula ⑨, and the regression coefficients k ₀ , k ₅ , k ₄ , and k of the model are calculated by SPSS software. ₆ , k ₂ and the relationship diagram (Fig. 2), respectively: k ₀ =-70.29, k ₅ =46966.47, k ₄ =298713.17, k ₆ =607338.27, k ₂ =8712.413;

Therefore, the obtained comprehensive prediction model of oil and gas reserves in fault-lithologic reservoirs is:

When m=5, according to the ranking result of the correlation degree in the third step, the sandstone body area, the sandstone body permeability, the sandstone body thickness, the active rate of the oil source fault, and the hydrocarbon generation intensity of the source rock are selected as the dominant influencing factors. According to the formula ⑨ The comprehensive prediction model of oil and gas reserves in fault-lithologic reservoirs is established as follows:

The normalized value of the hydrocarbon reservoir influencing factors calculated in the fourth step (1) is put into formula ⑨, and the regression coefficients k ₀ , k ₅ , k ₄ , and k of the model are calculated by SPSS software. ₆ , k ₂ , k ₁ and the relationship diagram (Fig. 3), respectively: k ₀ =102.613, k ₅ =7813.5, k ₄ =45663.3, k ₆ =87986.3, k ₂ =1056.5, k ₁ =55460.3;

Therefore, the obtained comprehensive prediction model of oil and gas reserves in fault-lithologic reservoirs is:

When m=6, according to the ranking result of the correlation degree in the third step, the sandstone body area, the sandstone body permeability, the sandstone body thickness, the active rate of the oil source fault, the hydrocarbon generation intensity of the source rock, and the sandstone porosity are selected as the dominant influences. According to formula 9, the comprehensive prediction model of oil and gas reserves of fault-lithologic reservoirs is established as:

The normalized value of the hydrocarbon accumulation influencing factors calculated in the fourth step (1) is put into formula ⑨, and the regression coefficients k ₀ , k ₅ , k ₄ , and k of the model are calculated by SPSS software. ₆ , k ₂ , k ₁ , k ₃ and the relationship diagram (Fig. 4), respectively: k ₀ =162.22, k ₅ =6984.4, k ₄ =39956.3, k ₆ =74587.5, k ₂ =969.3, k ₁ =49691.3 , k ₃ =55597.5;

Therefore, the obtained comprehensive prediction model of oil and gas reserves in fault-lithologic reservoirs is:

When m=7, according to the ranking result of the correlation degree in the third step, select the area of sandstone body, permeability of sandstone body, thickness of sandstone body, active rate of oil source fault, hydrocarbon generation intensity of source rock, sandstone porosity, fault- The sand body contact length is the dominant influencing factor, and the comprehensive prediction model for the oil and gas reserves of fault-lithologic reservoirs established according to formula 9 is:

The normalized value of the hydrocarbon accumulation influencing factors calculated in the fourth step (1) is put into formula ⑨, and the regression coefficients k ₀ , k ₅ , k ₄ , and k of the model are calculated by SPSS software. ₆ , k ₂ , k ₁ , k ₃ , k ₇ and the relationship diagram (Fig. 5), respectively: k ₀ =139.486, k ₅ =5976.5, k ₄ =43669.1, k ₆ =69886.6, k ₂ =5756.1,k ₁ = 45452.6, k ₃ =46563.1, k ₇ =923391.2;

Therefore, the obtained comprehensive prediction model of oil and gas reserves in fault-lithologic reservoirs is:

和

The sixth step is to use formula ⑩ to calculate the judgment index when m is 3, 4, 5, 6, and 7 respectively.

and

表示判定指数，其中m表示该判定指数对应的优势影响因素的个数；

表示第i个断层-岩性油气藏油气储量的计算值；Q_i表示第i个断层-岩性油气藏油气储量；

表示所有断层-岩性油气藏油气储量的平均值；In the formula,

Represents the judgment index, where m represents the number of dominant influencing factors corresponding to the judgment index;

Represents the calculated value of oil and gas reserves of the ith fault-lithologic reservoir; Q _i represents the oil and gas reserves of the ith fault-lithologic reservoir;

Represents the average value of oil and gas reserves of all fault-lithologic reservoirs;

和

以判定指数

为例，具体的计算过程为：According to formula ⑩, calculate the judgment index when m is 3, 4, 5, 6, and 7 respectively.

and

to determine the index

For example, the specific calculation process is:

和

分别为：Similarly, formula ⑩ is used to calculate the judgment index when m is 3, 4, 5, 6, and 7 respectively.

and

They are:

的大小，以判定指数数值最小值所对应的综合预测模型为该地区断层-岩性油气藏的储量预测模型。Step 7: Compare the judgment index calculated in Step 6

The comprehensive prediction model corresponding to the minimum value of the judgment index is the reserve prediction model of the fault-lithologic reservoir in this area.

的大小，由高到低依次为

判定指数

最小，所对应的综合预测模型：

为研究区所建立的断层-岩性油气藏的储量预测模型。Compare the judgment index calculated in step 6

The size, from high to low, is

Judgment Index

Minimum, the corresponding comprehensive prediction model:

A reserve prediction model for fault-lithologic reservoirs established for the study area.

中，得到渤中28-2构造的预测储量为4204.34万吨，与实际油气储量4500万吨接近，相对误差仅为6.5％，从而验证了利用本发明所构建的储量预测模型具有合理性与有效性。反之，采用传统方法建立的专家赋分制的油气藏油气储量预测模型，得到渤中28-2构造的预测储量为3996.3万吨，与实际油气储量4500万吨的相对误差则为11.1％，相比之下，利用本发明中所述方法建立的储量预测模型精确度更高，对断层-岩性油气藏油气储量预测的效果更好，更有利于指导断层-岩性油气藏的油气勘探及目标优选。Taking the Bozhong 28-2 structure as an example, the sand body area, sand body thickness, sand body porosity and oil-source fault activity rate (Table 2) of the Bozhong 28-2 structure are selected as the dominant factors for accumulation. Bring in the integrated forecasting model:

, the predicted reserves of the Bozhong 28-2 structure are 42.0434 million tons, which is close to the actual oil and gas reserves of 45 million tons, and the relative error is only 6.5%, which verifies that the reserves prediction model constructed by the present invention is reasonable and effective. sex. On the contrary, the oil and gas reserves prediction model of oil and gas reservoirs based on the expert scoring system established by the traditional method, the predicted reserves of Bozhong 28-2 structure are 39.963 million tons, and the relative error with the actual oil and gas reserves of 45 million tons is 11.1%, which is similar to that of Bozhong 28-2 structure. In contrast, the reserve prediction model established by the method described in the present invention has higher accuracy, better effect on oil and gas reserve prediction of fault-lithologic oil and gas reservoirs, and is more conducive to guiding the oil and gas exploration and development of fault-lithologic oil and gas reservoirs. Target preference.

Table 2 Normalized values and predicted geological reserves of Bozhong 28-2 structural influencing factors

Claims (1)

1. A construction method of a fault-lithology hydrocarbon reservoir oil and gas reserve prediction model is characterized by comprising the following steps:

determining reservoir forming influence factors representing the fault-lithologic hydrocarbon reservoir based on hydrocarbon generation capacity, storage capacity and migration capacity according to geological data of the fault-lithologic hydrocarbon reservoir in a research area, wherein the influence factors comprise hydrocarbon source rock hydrocarbon generation strength, oil source fault activity rate, sandstone mass porosity, sandstone mass permeability, sandstone mass area, sandstone mass thickness and fault-sandstone contact length;

secondly, determining n fault-lithology hydrocarbon reservoir objects participating in hydrocarbon reserve prediction, and calculating the weighting coefficient of the hydrocarbon reserve of each fault-lithology hydrocarbon reservoir by using a formula (I):

wherein n is the number of fault-lithologic oil and gas reservoirs and takes a positive integer; i is the serial number of the fault-lithologic oil and gas reservoir, the value range is between 1 and n and is an integer; delta_iWeighting coefficients of oil and gas reserves of the ith fault-lithology oil and gas reservoir; q_iThe oil and gas reserves of the ith fault-lithology oil and gas reservoir;

thirdly, determining the association degree and sequencing between each reservoir forming influence factor of the fault-lithologic oil and gas reservoir and the oil and gas reserves of the oil and gas reservoir, wherein the step is carried out according to the following path:

(1) taking the oil and gas reserves and reservoir formation influence factors of the fault-lithology oil and gas reservoir as parameters for representing the system characteristics, wherein the parameters comprise a system characteristic reference series and a system factor comparison series; the system characteristic reference series is composed of the hydrocarbon reserves of a series of fault-lithology hydrocarbon reservoirs, denoted as Q₁，Q₂，Q₃…Q_iAbbreviated as { Q_iN, where i ═ 1,2.. n; the system factor comparison series is composed of a series of reservoir-forming influence factors of fault-lithology hydrocarbon reservoirs, and is expressed as X₁₁，X₁₂，X₁₃…X_jiAbbreviated as { X_jiJ ═ 1,2.. 7, i ═ 1,2.. n, X_jiAnd (3) representing the value of the ith fault-lithologic hydrocarbon reservoir in the jth system factor comparison sequence, wherein the hydrocarbon source rock hydrocarbon generation strength, the activity rate of the oil source fault, the sandstone porosity, the sandstone permeability, the sandstone area, the sandstone thickness and the fault-sandstone contact length are respectively recorded as { X (X) }_1i}、{X_2i}、{X_3i}、{X_4i}、{X_5i}、{X_6i}、{X_7i}；

(2) Carrying out non-dimensionalization processing on the system factor comparison number sequence and the system characteristic reference number sequence by using a formula II and a formula III respectively;

in the formula (I), the compound is shown in the specification,

is the normalized value of the ith fault-lithology reservoir in the jth system factor comparison array,

is the average of the jth systematic factor,

is the value of the i-th fault-lithology hydrocarbon reservoir after the oil gas reserves are normalized,

the average value of the oil and gas reserves of all fault-lithology oil and gas reservoirs is obtained;

obtaining a new series after dimensionless treatment, wherein the new series comprises the series of the oil gas reserves, the hydrocarbon generation strength of the hydrocarbon source rocks, the activity rate of the oil source fault, the porosity of the sandstone mass, the permeability of the sandstone mass, the area of the sandstone mass, the thickness of the sandstone mass and the contact length of the fault-sand mass, and the new series is respectively recorded as

(3) Determining a correlation coefficient beta between the system characteristic reference array and the system factor comparison array according to the formula IV; then, calculating the relevance gamma according to the formula (five), and sorting the relevance from big to small:

the correlation coefficient is:

where ρ is a resolution coefficient, where ρ is 0.5, i is 1,2 … n, j is 1,2,3 … 7,

representing a series of system reference signatures

Comparing the ith value with the jth system factor

Absolute difference of the ith value, and

the minimum value in the sequence of absolute differences is indicated,

then represents the maximum value in the absolute difference sequence;

the degree of association is: gamma (j) delta₁×β(Q₁，X_j1)+δ₂×β(Q₂，X_j2)+…+δ_i×β(Q_i，X_ji)⑤

Fourthly, calculating the weight coefficient of each occlusion influence factor, wherein the step is carried out according to the following path:

(1) and (3) carrying out normalization treatment on various influencing factors with different dimensions and magnitude levels by using a formula (I):

in the formula, X_jiRepresents the value of the ith fault-lithology reservoir in the jth system factor comparison array, min (X)_ji) Represents the minimum value, max (X), of all the reservoir-forming influence factors of each oil and gas reservoir_ji) Represents the maximum value X 'in all reservoir forming influence factors of each oil and gas reservoir'_jiRepresenting the numerical value of all reservoir forming influence factors of each oil and gas reservoir after unified normalization processing;

(2) calculating the entropy value of each accumulation influencing factor by using a formula (c):

in the formula, B_jIs the jth reservoir-forming influence factor of fault-lithology reservoirAn entropy value;

(3) and sequentially calculating the weight coefficients of all the influencing factors according to a formula ((R)):

in the formula, C_jIs the weight coefficient of the jth reservoir forming influence factor of the fault-lithology reservoir;

fifthly, sorting according to the relevance obtained in the third step, selecting the first m reservoir forming influence factors with high relevance as dominant influence factors, correcting the index data by using the weight coefficient of each influence factor on the basis of a multiple linear regression method, and establishing a comprehensive prediction model of the oil and gas reserves of a plurality of fault-lithologic oil and gas reservoirs, wherein the initial value of m is 3, and the subsequent values are 4, 5, 6 and 7 in sequence;

the steps are carried out according to the following paths:

(1) on the premise of knowing m screened formation influence factors with high association degree and weight coefficients thereof, establishing a comprehensive prediction model of the oil and gas reserves of the fault-lithologic oil and gas reservoir according to the formula ninthly:

in the formula (I), the compound is shown in the specification,

a calculated value representing the hydrocarbon reserve of the ith fault-lithology reservoir; x'_jiThe numerical values of all reservoir forming influence factors of the oil and gas reservoirs after unified normalization processing are obtained, wherein indexes which are not selected as dominant influence factors do not participate in calculation; c_jThe weight coefficient is the jth reservoir forming influence factor of the fault-lithology reservoir; k is a radical of_jIs the regression coefficient of the model;

(2) determining a model regression coefficient of the constructed comprehensive prediction model by adopting a least square method; this step is performed in SPSS software and yields the regression coefficients k for each term of the model₀、k₁、k₂…k_jAnd a relationship diagram;

sixthly, using equation (R) to calculate out decision index when m takes values of 3, 4, 5, 6 and 7 respectively

And

in the formula (I), the compound is shown in the specification,

representing a judgment index, wherein m represents the number of dominant influence factors corresponding to the judgment index;

a calculated value representing the hydrocarbon reserve of the ith fault-lithology hydrocarbon reservoir; q_iRepresenting the oil and gas reserves of the ith fault-lithology oil and gas reservoir;

representing the average of the oil and gas reserves of all fault-lithology oil and gas reservoirs;

seventh, comparing the judgment indexes calculated in the sixth step

And taking the comprehensive prediction model corresponding to the minimum value of the judgment index numerical value as a reserve prediction model of the fault-lithology oil and gas reservoir in the region.

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