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CN114021804A - A method for constructing oil and gas reserves prediction model of fault-lithologic oil and gas reservoirs - Google Patents

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

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CN114021804A
CN114021804A CN202111286572.7A CN202111286572A CN114021804A CN 114021804 A CN114021804 A CN 114021804A CN 202111286572 A CN202111286572 A CN 202111286572A CN 114021804 A CN114021804 A CN 114021804A
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马妍
孙永河
庞磊
刘召
刘露
娄瑞
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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 characterizing the fault-lithology hydrocarbon reservoir; (2) calculating a weighting coefficient of the oil and gas reserves of the fault-lithology oil and gas reservoir, and a good-bad 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

Construction method of fault-lithology oil and gas reservoir oil and gas reserve prediction model
The technical field is as follows:
the invention relates to the technical field of oil-gas exploration, in particular to a method for constructing an oil-gas reserve prediction model.
Background art:
a fault-lithology reservoir refers to a reservoir that is formed under the control of both fault and lithology factors. In the structural evolution crack stage, the continental basin generally undergoes stages of rapid sedimentation of a substrate, expansion of the area of a lake basin and rapid deepening of a water body, and the combined development of high-quality lake-facies hydrocarbon source rocks and deep delta sand bodies at the edge of the lake basin is greatly promoted. Generally speaking, deep delta sand bodies developing at the edge of a lake basin have better reservoir physical properties, because the peripheral bulge of the lake basin is subjected to weathering and corrosion for a long time, the deep delta sand bodies are good large-sized material source regions, sufficient material bases can be provided for the development of the sand bodies of the bulged descent tray, meanwhile, the development of the steep slope fold of the basin edge also provides places for the deposition of debris materials of the descent tray, and the deep delta sand bodies are favorable for forming self-generated self-storage type in-source oil and gas reservoirs. Since the new generation, under the influence of constructional activities, a series of oil source faults communicating sand bodies in shallow and deep hydrocarbon source rocks are formed, and the faults can be used as dredging channels for vertical migration of oil and gas to a certain extent and control the formation characteristics and the distribution rule of the oil and gas. When the deep hydrocarbon source rock is mature and reaches a hydrocarbon generation threshold, hydrocarbon is discharged, and under the filling of a high-strength hydrocarbon source rock hydrocarbon generation system, on one hand, oil gas is directly transported to sand bodies in the source from the hydrocarbon source rock to be accumulated into a reservoir; on the other hand, hydrocarbons migrate to the shallow layers by fracturing the oil source through the deep and shallow layers and accumulate into reservoirs. The fault-lithologic hydrocarbon reservoir is used as an important reservoir forming mode under the structural-lithologic coupling effect, and no technical scheme directly aiming at fault-lithologic hydrocarbon reservoir advantage influence factor judgment and a hydrocarbon reserve prediction model thereof exists in the current published literature. A reserve prediction model of similar oil and gas reservoirs (such as a buried hill oil and gas reservoir) which can be used as a reference is weighted by expert judgment, objective information is not fully utilized by the reserve prediction model, so that the reserve prediction model has strong artificial subjectivity and accidental property and lacks objectivity, and the reserve prediction model cannot be applied after being experimentally applied to the prediction of the oil and gas reserve of the fault-lithology oil and gas reservoir.
The invention content is as follows:
in order to solve the technical problems mentioned in the background technology, the invention provides a construction method of an oil and gas reserve prediction model of a fault-lithology oil and gas reservoir.
The technical scheme of the invention is as follows: the construction method of the fault-lithology hydrocarbon reservoir oil and gas reserve prediction model is characterized by comprising the following steps of:
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 reservoir objects participating in oil and gas reserve prediction, and calculating the weighting coefficient of the oil and gas reserve of each fault-lithology reservoir by using a formula (i):
Figure BDA0003333122600000021
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-lithology oil and gas reservoir, the value range is between 1 and n and is an integer; deltaiWeighting coefficients of oil and gas reserves of the ith fault-lithology oil and gas reservoir; qiThe hydrocarbon reserve of the ith fault-lithology reservoir.
Thirdly, determining the association degree between each reservoir forming influence factor of the fault-lithology reservoir and the oil and gas reserves of the reservoir and sequencing the relationship degrees, wherein the step is carried out according to the following path:
(1) using the oil gas reserves and reservoir formation influence factors of the fault-lithology oil gas reservoir as parameters for representing the system characteristics, wherein the parameters comprise a system characteristic reference sequence and a system factorA prime comparison array; the system characteristic reference series is composed of the hydrocarbon reserves of a series of fault-lithology hydrocarbon reservoirs, denoted as Q1，Q2，Q3…QiAbbreviated as { QiN, 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…XjiAbbreviated as { XjiJ ═ 1,2.. 7, i ═ 1,2.. n, XjiAnd (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}、{X2i}、{X3i}、{X4i}、{X5i}、{X6i}、{X7i}；
(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;
Figure BDA0003333122600000022
Figure BDA0003333122600000031
in the formula (I), the compound is shown in the specification,
Figure BDA0003333122600000032
is the normalized value of the ith fault-lithology reservoir in the jth system factor comparison array,
Figure BDA0003333122600000033
is the average of the jth systematic factor,
Figure BDA0003333122600000034
is the value of the i-th fault-lithology hydrocarbon reservoir after the oil gas reserves are normalized,
Figure BDA0003333122600000035
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
Figure BDA0003333122600000036
Figure BDA0003333122600000037
(3) Determining a correlation coefficient beta between a system characteristic reference number series (namely an oil and gas reserves number series of the fault-lithology oil and gas reservoir) and a system factor comparison number series (namely each reservoir forming influence factor number series) according to the formula IV; then, calculating the association degree gamma according to the formula, and sorting the association degrees in the descending order:
the correlation coefficient is:
Figure BDA0003333122600000038
where ρ is a resolution coefficient, and is usually 0.5, 1,2 … n, 1,2,3 … 7,
Figure BDA0003333122600000039
representing a series of system reference signatures
Figure BDA00033331226000000310
Comparing the ith value with the jth system factor
Figure BDA00033331226000000311
Absolute difference of the ith value, and
Figure BDA00033331226000000312
the minimum value in the sequence of absolute differences is indicated,
Figure BDA00033331226000000313
then represents the maximum value in the absolute difference sequence;
the degree of association is: gamma (j) delta1×β(Q1，Xj1)+δ2×β(Q2，Xj2)+…+δi×β(Qi，Xji) 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):
Figure BDA00033331226000000314
in the formula, XjiRepresents 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 reservoirji) 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):
Figure BDA0003333122600000041
in the formula, BjIs the entropy value of the jth reservoir formation influence factor of the fault-lithology reservoir;
(3) and sequentially calculating the weight coefficients of all the influencing factors according to a formula ((R)):
Figure BDA0003333122600000042
in the formula, CjIs the jth reservoir formation impact of fault-lithology reservoirA weight coefficient of the factor;
and 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 reservoir influence factors with high association degree and weight coefficients thereof, establishing a comprehensive prediction model of the oil and gas reserves of the fault-lithology oil and gas reservoir according to the formula ninthly:
Figure BDA0003333122600000043
in the formula (I), the compound is shown in the specification,
Figure BDA0003333122600000044
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; cjThe weight coefficient is the jth reservoir forming influence factor of the fault-lithology reservoir; k is a radical ofjIs 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 can be performed in SPSS software and yields the regression coefficients k for each term of the model0、k1、k2…kjAnd a relationship diagram.
Sixthly, calculating the judgment indexes when m takes values of 3, 4, 5, 6 and 7 respectively by using the formula of R
Figure BDA0003333122600000051
And
Figure BDA0003333122600000052
Figure BDA0003333122600000053
in the formula (I), the compound is shown in the specification,
Figure BDA0003333122600000054
representing a judgment index, wherein m represents the number of dominant influence factors corresponding to the judgment index;
Figure BDA0003333122600000055
a calculated value representing the hydrocarbon reserve of the ith fault-lithology reservoir; qiRepresenting the oil and gas reserves of the ith fault-lithology oil and gas reservoir;
Figure BDA0003333122600000056
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
Figure BDA0003333122600000057
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.
The invention has the following beneficial effects: firstly, the invention determines the reservoir forming influence factors representing and influencing the fault-lithologic oil and gas reservoir based on the investigation of a large amount of geological data, determines the weight coefficient of the oil and gas reservoir reserves by calculating the weight coefficient, and calculates the weight coefficient of each reservoir forming influence factor by fully utilizing the information provided by objective data, thereby overcoming the defects of index flat weight and expert assignment, removing the subjective influence of artificial judgment, and further accurately selecting the dominant influence factors of the oil and gas reservoir. In the construction of the oil and gas reserves prediction model, the index data is corrected by utilizing the weight coefficient of each influencing factor based on the traditional multiple linear regression model, the fitting precision of the traditional multiple linear regression model is substantially improved, the judgment indexes for sequentially representing the model precision are calculated circularly, and the most reasonable oil and gas reserves prediction model is optimized. The construction of the prediction model under the method of the invention not only overcomes the defect of less drilling data, but also reduces a large amount of experimental workload, and provides theoretical and technical support for guiding the oil-gas exploration of the fault-lithology oil-gas reservoir and improving the exploration success rate. The reserve prediction model constructed by the method is proved to have high prediction precision through experimental application, and can be effectively applied to prediction of the oil and gas reserve of the fault-lithology oil and gas reservoir.
Description of the drawings:
FIG. 1 is a graph of the relationship between hydrocarbon reserves and calculated hydrocarbon reserves of a fault-lithology hydrocarbon reservoir under the influence of three advantages.
FIG. 2 is a graph of the relationship between hydrocarbon reserves and calculated hydrocarbon reserves of a fault-lithology reservoir under the influence of four advantages.
FIG. 3 is a graph of the relationship between hydrocarbon reserves and calculated hydrocarbon reserves of a fault-lithology reservoir under the influence of five advantages.
FIG. 4 is a graph of the relationship of hydrocarbon reserves of a fault-lithology reservoir to calculated hydrocarbon reserves under six dominant effects.
FIG. 5 is a graph of the relationship between hydrocarbon reserves and calculated hydrocarbon reserves of a fault-lithology reservoir under seven dominant influences.
The specific implementation mode is as follows:
the invention will be further described with reference to the accompanying drawings in which:
in order to make the objects, research methods and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the research, a Bohai sea area is taken as a research area, 7 typical fault-lithologic oil and gas reservoirs, namely QHD35-4, BZ1-1, BZ2-1, BZ3-2, CFD6-4, BZ25-1 and KL10-1 are selected, sampling and experimental work is carried out on different fault-lithologic oil and gas reservoirs, sample information is shown in table 1, and experimental test analysis on porosity and permeability of a sampled sample is carried out. And simultaneously collecting and counting the hydrocarbon generation intensity of 7 fault-lithology hydrocarbon reservoirs, a core comprehensive column diagram of a typical well (used for determining the thickness of a sand body of the fault-lithology hydrocarbon reservoir) and a sedimentary facies diagram and seismic data of a research area. Geological data statistics shows that the main factors influencing the oil and gas reserves of the fault-lithologic oil and gas reservoir comprise hydrocarbon source rock hydrocarbon generation strength, the activity rate of an oil source fault, the porosity of a 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-sandstone mass. In order to reflect the influence of each factor to the maximum extent, screening is carried out during data statistics so as to eliminate the influence of sample properties as much as possible. Note that the data constructed by 28-1 in the bohai in table 1 is not involved in modeling, and the constructed data is used for the prediction accuracy verification of the model.
The specific implementation steps for constructing the oil and gas reserve model of the fault-lithology oil and gas reservoir by using the data are as follows:
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;
the hydrocarbon generation intensity of the hydrocarbon source rock is calculated by the formula E ═ h multiplied by rho multiplied by Wc multiplied by K1×K2And E is the hydrocarbon generation intensity (kg/m) of the hydrocarbon source rock2) H is effective source rock thickness (m); ρ is the density of the source rock (10)8t/km3) Wc is the residual organic carbon content fraction (%) of the source rock; k1Is an organic carbon recovery coefficient; k2Hydrocarbon yield (m) for organic carbon3/t)；
The activity rate of the oil source fault refers to the ratio of the fall (m) of the stratum on the upper plate and the lower plate of the oil source fault due to the formation activity to the stratum deposition time (Ma);
the porosity (%) of the sandstone mass refers to sampling the sand of the fault-lithology oil and gas reservoir for multiple times, performing porosity experiment test analysis, and taking the average value of multiple experiment results as the parameter of the porosity of the sand of the fault-lithology oil and gas reservoir;
the permeability (mD) of the sandstone mass is to sample the fault-lithologic hydrocarbon reservoir sand body for multiple times, perform permeability experiment test analysis, and take the average value of multiple experiment results as the parameter of the fault-lithologic hydrocarbon reservoir sand body permeability;
sand rock mass area (km)2) Finely depicting the plane distribution range of fault-lithology oil and gas reservoir sand rock bodies by using a seismic sedimentology method, and predicting the plane distribution area of lake bottom fan sand bodies by using the sedimentary facies distribution range in combination with the sedimentary characteristics of the lake bottom fan and the sedimentary difference of peripheral mudstone;
the thickness (m) of the sandstone mass is obtained by accumulating the thicknesses corresponding to the sandstone layers according to the logging information;
the fault-sand contact length (m) refers to the sand length of the contact part of the sedimentary sand body developed in the oil source rock of the descent tray and the oil source fault, the oil source fault and the sedimentary sand body in contact with the oil source fault are identified by using the seismic profile characteristics, and the length of the sand body in contact with the oil source fault is measured;
the results of obtaining different fault-lithology reservoir influencing factors are shown in the following table (table 1):
TABLE 1 statistics of different fault-lithology reservoir impact factors
Figure BDA0003333122600000071
Figure BDA0003333122600000081
Secondly, determining n fault-lithology reservoir objects participating in oil and gas reserve prediction, and calculating the weighting coefficient of the oil and gas reserve of each fault-lithology reservoir by using a formula (i):
Figure BDA0003333122600000082
wherein n is the number of fault-lithologic oil and gas reservoirs and takes the value of positive integerCounting; i is the serial number of the fault-lithology oil and gas reservoir, the value range is between 1 and n and is an integer; deltaiWeighting coefficients of oil and gas reserves of the ith fault-lithology oil and gas reservoir; qiThe hydrocarbon reserve of the ith fault-lithology reservoir.
The formula is used for calculating the weighting coefficients of the oil and gas reserves of the 7 fault-lithologic oil and gas reservoirs, wherein the weighting coefficients are respectively as follows:
Figure BDA0003333122600000083
Figure BDA0003333122600000084
Figure BDA0003333122600000085
Figure BDA0003333122600000086
Figure BDA0003333122600000087
Figure BDA0003333122600000088
Figure BDA0003333122600000089
thirdly, determining the association degree between each reservoir forming influence factor of the fault-lithology reservoir and the oil and gas reserves of the reservoir and sequencing the relationship degrees, wherein the step is carried out according to the following path:
(1) using 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 systemComparing the system factors in a sequence; the system characteristic reference series is composed of the hydrocarbon reserves of a series of fault-lithology hydrocarbon reservoirs, denoted as Q1，Q2，Q3…QiAbbreviated as { QiN, 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…XjiAbbreviated as { XjiJ ═ 1,2.. 7, i ═ 1,2.. n, XjiAnd (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}、{X2i}、{X3i}、{X4i}、{X5i}、{X6i}、{X7i}；
The oil gas reserves, hydrocarbon source rock hydrocarbon generation strength, the activity rate of the oil source faults, 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-sandstone mass of the fault-lithologic oil and gas reservoir are respectively recorded as:
{Qi}＝{716.12，587.52，832.08，1246.72，6057.63，16601.57，17995.54}
{X1i}＝{2428.61，3966.55，6990.3，4962.5，2008，8644，8240}
{X2i}＝{90.27，75.82，83.05，77.41，120.89，141.23，115.73}
{X3i}＝{12.5，13.77，13.41，13.88，14.01，14.32，22.6}
{X4i}＝{23.42，19.36，18.45，15.98，18.34，20.12，19.11}
{X5i}＝{11.38，4.84，4.84，10.92，14.81，15.08，12.33}
{X6i}＝{64.2，5.47，68.16，26.7，133.3，122.25，264.5}
{X7i}＝{4.52，3.51，1.45，3.25，9.39，10.91，8.23}
(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;
Figure BDA0003333122600000091
Figure BDA0003333122600000092
in the formula (I), the compound is shown in the specification,
Figure BDA0003333122600000093
is the normalized value of the ith fault-lithology reservoir in the jth system factor comparison array,
Figure BDA0003333122600000094
is the average of the jth systematic factor,
Figure BDA0003333122600000095
is the value of the i-th fault-lithology hydrocarbon reservoir after the oil gas reserves are normalized,
Figure BDA0003333122600000096
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
Figure BDA0003333122600000097
Figure BDA0003333122600000098
Carrying out non-dimensionalization treatment on the system factor comparison series by using a formula II, taking the influence factor of the porosity of the sand rock as an example, the specific calculation process is as follows:
Figure BDA0003333122600000099
Figure BDA00033331226000000910
Figure BDA00033331226000000911
Figure BDA00033331226000000912
Figure BDA00033331226000000913
Figure BDA00033331226000000914
Figure BDA0003333122600000101
therefore, the obtained new sand rock mass porosity after dimensionless treatment is as follows:
Figure BDA0003333122600000102
similarly, the new dimensionless sequence obtained in sequence according to the calculation method of formula (II) is:
hydrocarbon-formation strength of hydrocarbon source rock:
Figure BDA0003333122600000103
moving rate of oil source fault:
Figure BDA0003333122600000104
porosity of sand rock mass:
Figure BDA0003333122600000105
sand rock mass permeability:
Figure BDA0003333122600000106
sand rock mass area:
Figure BDA0003333122600000107
thickness of sand rock mass:
Figure BDA0003333122600000108
fault-sand contact length series:
Figure BDA0003333122600000109
similarly, the new number of the oil and gas reserves after the dimensionless treatment obtained by the calculation method of the formula (III) is as follows:
Figure BDA00033331226000001010
(3) determining a correlation coefficient beta between a system characteristic reference number series (namely an oil and gas reserves number series of the fault-lithology oil and gas reservoir) and a system factor comparison number series (namely each reservoir forming influence factor number series) according to the formula IV; then, calculating the association degree gamma according to the formula, and sorting the association degrees in the descending order:
the correlation coefficient is:
Figure BDA00033331226000001011
where ρ is a resolution coefficient, and is usually 0.5, 1,2 … n, 1,2,3 … 7,
Figure BDA00033331226000001012
representing a series of system reference signatures
Figure BDA00033331226000001013
Comparing the ith value with the jth system factor
Figure BDA00033331226000001014
Absolute difference of the ith value, and
Figure BDA00033331226000001015
the minimum value in the sequence of absolute differences is indicated,
Figure BDA00033331226000001016
then represents the maximum value in the absolute difference sequence;
the degree of association is: gamma (j) delta1×β(Q1，Xj1)+δ2×β(Q2，Xj2)+…+δi×β(Qi，Xji) ⑤
Calculating a correlation coefficient beta between a system characteristic reference number series (namely an oil and gas reserves number series of the fault-lithology oil and gas reservoir) and a system factor comparison number series (namely each reservoir forming influence factor number series) according to a formula, taking the influence factor of the sand rock mass porosity as an example, and the specific calculation process is as follows:
Figure BDA00033331226000001017
Figure BDA0003333122600000111
Figure BDA0003333122600000112
Figure BDA0003333122600000113
Figure BDA0003333122600000114
Figure BDA0003333122600000115
Figure BDA0003333122600000116
then, calculating the correlation degree gamma of the influence factor of the porosity of the sand rock body and the oil and gas reserve of the fault-lithology oil and gas reservoir according to a formula:
γ(3)＝δ1×β(Q1，X31)+δ2×β(Q2，X32)+…+δ7×β(Q7，X37)
＝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, the hydrocarbon-source rock hydrocarbon-producing strength, the oil source fault moving rate, the sandstone porosity, the sandstone permeability, the sandstone area, the sandstone thickness and the correlation between the fault-sandstone contact length and the fault-lithology hydrocarbon reservoir oil and gas reserves are respectively obtained according to the formula IV and the formula V:
hydrocarbon-formation strength of hydrocarbon source rock: gamma (1) ═ 0.48
Moving rate of oil source fault: gamma (2) 0.52
Porosity of sand rock mass: gamma (3) ═ 0.46
Sand rock mass permeability: gamma (4) ═ 0.71
Sand rock mass area: gamma (5) ═ 0.78
Thickness of sand rock mass: gamma (6) 0.55
Fault-sand contact length: gamma (7) ═ 0.32
The influence factors of the relevance from high to low are as follows: the area of the sandstone mass, the permeability of the sandstone mass, the thickness of the sandstone mass, the activity rate of an oil source fault, the hydrocarbon generation strength of a hydrocarbon source rock, the porosity of the sandstone mass and the contact length of the fault-sand body.
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):
Figure BDA0003333122600000121
in the formula, XjiRepresents 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 reservoirji) 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;
normalizing the influence factors with different dimensions and magnitude according to a formula, taking the influence factor of the porosity of the sand rock as an example, the specific calculation process is as follows:
Figure BDA0003333122600000122
Figure BDA0003333122600000123
Figure BDA0003333122600000124
Figure BDA0003333122600000125
Figure BDA0003333122600000126
Figure BDA0003333122600000127
Figure BDA0003333122600000128
(2) calculating the entropy value of each accumulation influencing factor by using a formula (c):
Figure BDA0003333122600000129
in the formula, BjIs the entropy value of the jth reservoir formation influence factor of the fault-lithology reservoir;
calculating the entropy value of each reservoir forming influence factor by using a formula (c) on the numerical value after normalization treatment, taking the sandstone porosity influence factor as an example, the specific calculation process is as follows:
Figure BDA0003333122600000131
similarly, the entropy values of hydrocarbon source rock hydrocarbon generation strength, the activity rate of an oil source fault, the porosity of a sand rock body, the permeability of the sand rock body, the area of the sand rock body, the thickness of the sand rock body and the fault-sand body contact length are obtained by a formula (c):
hydrocarbon-formation strength of hydrocarbon source rock: b is1＝0.82
Moving rate of oil source fault: b is2＝0.18
Porosity of sand rock mass: b is3＝0.036
Sand rock mass permeability: b is4＝0.045
Sand rock mass area: b is5＝0.025
Thickness of sand rock mass: b is6＝0.17
Fault-sand contact length: b is7＝0.013
(3) And sequentially calculating the weight coefficients of all the influencing factors according to a formula ((R)):
Figure BDA0003333122600000132
in the formula, CjIs the weight coefficient of the jth reservoir forming influence factor of the fault-lithology reservoir;
calculating the weight coefficient of each influence factor according to a formula, taking the influence factor of the sand rock porosity as an example, and the specific calculation process is as follows:
Figure BDA0003333122600000133
similarly, the weight coefficients of the hydrocarbon source rock hydrocarbon generation strength, the oil source fault activity rate, the sand rock porosity, the sand rock permeability, the sand rock area, the sand rock thickness and the fault-sand contact length are obtained by a formula (r):
hydrocarbon-formation strength of hydrocarbon source rock: c1＝0.032
Moving rate of oil source fault: c2＝0.143
Porosity of sand rock mass: c3＝0.168
Sand rock mass permeability: c4＝0.167
Sand rock mass area: c5＝0.170
Thickness of sand rock mass: c6＝0.145
Fault-sand contact length: c7＝0.172
And 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 reservoir influence factors with high association degree and weight coefficients thereof, establishing a comprehensive prediction model of the oil and gas reserves of the fault-lithology oil and gas reservoir according to the formula ninthly:
Figure BDA0003333122600000141
in the formula (I), the compound is shown in the specification,
Figure BDA0003333122600000142
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; cjThe weight coefficient is the jth reservoir forming influence factor of the fault-lithology reservoir; k is a radical ofjIs 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 can be performed in SPSS software and yields the regression coefficients k for each term of the model0、k1、k2…kjAnd a relationship diagram.
When m is 3, selecting the sand rock mass area, the sand rock mass permeability and the sand rock mass thickness as dominant influence factors according to the sequencing result of the relevance in the third step, and establishing a comprehensive prediction model of the oil and gas reserves of the fault-lithology oil and gas reservoir according to the formula ninx:
Figure BDA0003333122600000143
substituting the normalized reservoir formation influence factor value calculated in the fourth step (1) into a formula (ninx), and calculating regression coefficients k of the model by using SPSS software0、k5、k4、k6And a relational graph (fig. 1), respectively: k is a radical of0＝-31.66，k5＝20618.2，k4＝398021.6，k6＝511554.8；
Therefore, the obtained comprehensive prediction model of the oil and gas reserves of the fault-lithology hydrocarbon reservoir is as follows:
Figure BDA0003333122600000151
when m is 4, selecting the sand rock mass area, the sand rock mass permeability, the sand rock mass thickness and the oil source fault activity rate as dominant influence factors according to the sorting result of the correlation degree in the third step, and establishing a comprehensive prediction model of the fault-lithologic oil and gas reservoir oil and gas reserves according to the formula ninthly:
Figure BDA0003333122600000152
substituting the normalized reservoir formation influence factor value calculated in the fourth step (1) into a formula (ninx), and calculating regression coefficients k of the model by using SPSS software0、k5、k4、k6、k2And a relationship diagram (fig. 2), respectively: k is a radical of0＝-70.29，k5＝46966.47，k4＝298713.17，k6＝607338.27，k2＝8712.413；
Therefore, the obtained comprehensive prediction model of the oil and gas reserves of the fault-lithology hydrocarbon reservoir is as follows:
Figure BDA0003333122600000153
when m is 5, selecting the sand rock mass area, the sand rock mass permeability, the sand rock mass thickness, the oil source fault activity rate and the hydrocarbon source rock hydrocarbon generation strength as dominant influence factors according to the sorting result of the relevance in the third step, and establishing a comprehensive prediction model of the fault-lithologic oil and gas reservoir oil and gas reserves according to the formula ninthly:
Figure BDA0003333122600000154
substituting the normalized reservoir formation influence factor value calculated in the fourth step (1) into a formula (ninx), and calculating regression coefficients k of the model by using SPSS software0、k5、k4、k6、k2、k1And a relationship diagram (fig. 3), respectively: k is a radical of0＝102.613，k5＝7813.5，k4＝45663.3，k6＝87986.3，k2＝1056.5，k1＝55460.3；
Therefore, the obtained comprehensive prediction model of the oil and gas reserves of the fault-lithology hydrocarbon reservoir is as follows:
Figure BDA0003333122600000155
Figure BDA0003333122600000161
when m is 6, selecting the sand rock mass area, the sand rock mass permeability, the sand rock mass thickness, the oil source fault activity rate, the hydrocarbon source rock hydrocarbon production strength and the sandstone porosity as dominant influence factors according to the sequencing result of the correlation degree in the third step, and establishing a comprehensive prediction model of the fault-lithology hydrocarbon reservoir oil and gas reserves according to the formula ninx:
Figure BDA0003333122600000162
substituting the normalized reservoir formation influence factor value calculated in the fourth step (1) into a formula (ninx), and calculating regression coefficients k of the model by using SPSS software0、k5、k4、k6、k2、k1、k3And a relationship diagram (fig. 4), respectively: k is a radical of0＝162.22，k5＝6984.4，k4＝39956.3，k6＝74587.5，k2＝969.3，k1＝49691.3，k3＝55597.5；
Therefore, the obtained comprehensive prediction model of the oil and gas reserves of the fault-lithology hydrocarbon reservoir is as follows:
Figure BDA0003333122600000164
when m is 7, selecting the sand rock mass area, the sand rock mass permeability, the sand rock mass thickness, the activity rate of the oil source fault, the hydrocarbon generation strength of the hydrocarbon source rock, the sandstone porosity and the fault-sand body contact length as the dominant influence factors according to the sequencing result of the relevance in the third step, and establishing a comprehensive prediction model of the oil and gas reserves of the fault-lithology oil and gas reservoir according to the formula ninx:
Figure BDA0003333122600000163
substituting the normalized reservoir formation influence factor value calculated in the fourth step (1) into a formula (ninx), and calculating regression coefficients k of the model by using SPSS software0、k5、k4、k6、k2、k1、k3、k7And a relationship diagram (fig. 5), respectively: k is a radical of0＝139.486，k5＝5976.5，k4＝43669.1，k6＝69886.6，k2＝5756.1，k1＝45452.6，k3＝46563.1，k7＝923391.2；
Therefore, the obtained comprehensive prediction model of the oil and gas reserves of the fault-lithology hydrocarbon reservoir is as follows:
Figure BDA0003333122600000171
sixthly, calculating the judgment indexes when m takes values of 3, 4, 5, 6 and 7 respectively by using the formula of R
Figure BDA0003333122600000172
And
Figure BDA0003333122600000173
Figure BDA0003333122600000174
in the formula (I), the compound is shown in the specification,
Figure BDA0003333122600000175
representing a judgment index, wherein m represents the number of dominant influence factors corresponding to the judgment index;
Figure BDA0003333122600000176
a calculated value representing the hydrocarbon reserve of the ith fault-lithology reservoir; qiRepresenting the oil and gas reserves of the ith fault-lithology oil and gas reservoir;
Figure BDA0003333122600000177
representing the average of the oil and gas reserves of all fault-lithology oil and gas reservoirs;
calculating the judgment indexes when m takes values of 3, 4, 5, 6 and 7 respectively according to the formula (R)
Figure BDA0003333122600000178
Figure BDA0003333122600000179
And
Figure BDA00033331226000001710
to determine the index
Figure BDA00033331226000001711
For example, the specific calculation process is as follows:
Figure BDA00033331226000001712
similarly, the judgment indexes when m takes values of 3, 4, 5, 6 and 7 are calculated in sequence by using the formula (R & lt r & gt)
Figure BDA00033331226000001713
And
Figure BDA00033331226000001714
respectively as follows:
Figure BDA00033331226000001715
seventh, comparing the judgment indexes calculated in the sixth step
Figure BDA00033331226000001716
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.
Comparing the judgment indexes calculated in the step six
Figure BDA00033331226000001717
Is sequentially from high to low
Figure BDA00033331226000001718
Index of decision
Figure BDA00033331226000001719
And minimum, the corresponding comprehensive prediction model:
Figure BDA00033331226000001720
a reserve prediction model for fault-lithology hydrocarbon reservoirs is established for a research area.
Taking the structure 28-2 in the Bohai as an example, the sand body area, the sand body thickness, the sand body porosity and the oil source fault activity rate (table 2) of the structure 28-2 in the Bohai are selected as the burial dominant influence factors and are brought into a comprehensive prediction model:
Figure BDA00033331226000001721
Figure BDA00033331226000001722
in the method, the predicted reserve volume of the 28-2 structure in the Bohai is obtained to be 4204.34 ten thousand tons is close to the practical oil and gas reserves of 4500 ten thousand tons, and the relative error is only 6.5 percent, thereby verifying that the reserve prediction model constructed by the invention has rationality and effectiveness. On the contrary, the oil and gas reservoir oil and gas reserve prediction model established by the traditional method and assigned by the experts is adopted, the prediction reserve of the 28-2 structure in the Bohai is 3996.3 ten thousand tons, and the relative error with the actual oil and gas reserve of 4500 ten thousand tons is 11.1%.
28-2 in Bohai in Table 2 is constructed into numerical values after normalization of Tibetan influence factors and forecast geological reserves
Figure BDA0003333122600000181

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 reservoir objects participating in oil and gas reserve prediction, and calculating the weighting coefficient of the oil and gas reserve of each fault-lithology reservoir by using a formula (i):
Figure FDA0003333122590000011
wherein n is the number of fault-lithologic oil and gas reservoirs and takes a positive integer; i is fault-lithologic oilThe serial number of the gas reservoir ranges from 1 to n and is an integer; deltaiWeighting coefficients of oil and gas reserves of the ith fault-lithology oil and gas reservoir; qiThe oil and gas reserves of the ith fault-lithology oil and gas reservoir;
thirdly, determining the association degree between each reservoir forming influence factor of the fault-lithology reservoir and the oil and gas reserves of the reservoir and sequencing the relationship degrees, 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…QiAbbreviated as { QiN, 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…XjiAbbreviated as { XjiJ ═ 1,2.. 7, i ═ 1,2.. n, XjiAnd (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}、{X2i}、{X3i}、{X4i}、{X5i}、{X6i}、{X7i}；
(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;
Figure FDA0003333122590000021
Figure FDA0003333122590000022
in the formula，
Figure FDA0003333122590000023
Is the normalized value of the ith fault-lithology reservoir in the jth system factor comparison array,
Figure FDA0003333122590000024
is the average of the jth systematic factor,
Figure FDA0003333122590000025
is the value of the i-th fault-lithology hydrocarbon reservoir after the oil gas reserves are normalized,
Figure FDA0003333122590000026
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
Figure FDA0003333122590000027
Figure FDA0003333122590000028
(3) Determining a correlation coefficient beta between a system characteristic reference number series (namely an oil and gas reserves number series of the fault-lithology oil and gas reservoir) and a system factor comparison number series (namely each reservoir forming influence factor number series) according to the formula IV; then, calculating the association degree gamma according to the formula, and sorting the association degrees in the descending order:
the correlation coefficient is:
Figure FDA0003333122590000029
where ρ is a resolution coefficient, and is usually 0.5, 1,2 … n, 1,2,3 … 7,
Figure FDA00033331225900000210
representing a series of system reference signatures
Figure FDA00033331225900000211
Comparing the ith value with the jth system factor
Figure FDA00033331225900000212
Absolute difference of the ith value, and
Figure FDA00033331225900000213
the minimum value in the sequence of absolute differences is indicated,
Figure FDA00033331225900000214
then represents the maximum value in the absolute difference sequence;
the degree of association is: gamma (j) delta1×β(Q1，Xj1)+δ2×β(Q2，Xj2)+…+δi×β(Qi，Xji) 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):
Figure FDA00033331225900000215
in the formula, XjiRepresents 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 reservoirji) 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):
Figure FDA0003333122590000031
in the formula, BjIs the entropy value of the jth reservoir formation influence factor of the fault-lithology reservoir;
(3) and sequentially calculating the weight coefficients of all the influencing factors according to a formula ((R)):
Figure FDA0003333122590000032
in the formula, CjIs 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 reservoir influence factors with high association degree and weight coefficients thereof, establishing a comprehensive prediction model of the oil and gas reserves of the fault-lithology oil and gas reservoir according to the formula ninthly:
Figure FDA0003333122590000033
in the formula (I), the compound is shown in the specification,
Figure FDA0003333122590000034
a calculated value representing the hydrocarbon reserve of the ith fault-lithology reservoir; x'jiThe numerical value after the unified normalization processing of all reservoir forming influence factors of each oil and gas reservoir is obtained "Wherein the indexes which are not selected as dominant influencing factors do not participate in the calculation; cjThe weight coefficient is the jth reservoir forming influence factor of the fault-lithology reservoir; k is a radical ofjIs 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 can be performed in SPSS software and yields the regression coefficients k for each term of the model0、k1、k2…kjAnd a relationship diagram;
sixthly, calculating the judgment indexes when m takes values of 3, 4, 5, 6 and 7 respectively by using the formula of R
Figure FDA0003333122590000041
And
Figure FDA0003333122590000042
Figure FDA0003333122590000043
in the formula (I), the compound is shown in the specification,
Figure FDA0003333122590000044
representing a judgment index, wherein m represents the number of dominant influence factors corresponding to the judgment index;
Figure FDA0003333122590000045
a calculated value representing the hydrocarbon reserve of the ith fault-lithology reservoir; qiRepresenting the oil and gas reserves of the ith fault-lithology oil and gas reservoir;
Figure FDA0003333122590000046
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
Figure FDA0003333122590000047
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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CN119805576A (en) * 2025-03-13 2025-04-11 东北石油大学三亚海洋油气研究院 A method, system, device, medium and product for determining the amount of liquid produced by a fracture body

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