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

Construction method of fault-lithology oil and gas reservoir oil and gas reserve prediction model Download PDF

Info

Publication number
CN114021804B
CN114021804B CN202111286572.7A CN202111286572A CN114021804B CN 114021804 B CN114021804 B CN 114021804B CN 202111286572 A CN202111286572 A CN 202111286572A CN 114021804 B CN114021804 B CN 114021804B
Authority
CN
China
Prior art keywords
oil
fault
reservoir
gas
lithology
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202111286572.7A
Other languages
Chinese (zh)
Other versions
CN114021804A (en
Inventor
马妍
孙永河
庞磊
刘召
刘露
娄瑞
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northeast Petroleum University
Original Assignee
Northeast Petroleum University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Northeast Petroleum University filed Critical Northeast Petroleum University
Priority to CN202111286572.7A priority Critical patent/CN114021804B/en
Publication of CN114021804A publication Critical patent/CN114021804A/en
Application granted granted Critical
Publication of CN114021804B publication Critical patent/CN114021804B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • General Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Agronomy & Crop Science (AREA)
  • Animal Husbandry (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Mining & Mineral Resources (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
  • Testing Or Calibration Of Command Recording Devices (AREA)
  • Examining Or Testing Airtightness (AREA)

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

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 a similar hydrocarbon reservoir (such as a buried hill hydrocarbon 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 contingency and lacks objectivity, and the reserve prediction model cannot be applied after being experimentally applied to prediction of the hydrocarbon reserve of a fault-lithologic hydrocarbon 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 reserves 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) taking the oil gas reserves and reservoir forming influence factors of the fault-lithology oil and gas reservoir as parameters for representing system characteristics, wherein the parameters comprise a system characteristic reference number sequence and a system factor comparison number 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 above-mentionedThe system factor comparison series is composed of a series of reservoir-forming influence factors of the fault-lithology hydrocarbon reservoir, and is expressed as X11，X12，X13…XjiAbbreviated as { XjiJ ═ 1,2.. 7, i ═ 1,2.. n, XjiAnd (4) representing the value of the ith fault-lithologic hydrocarbon reservoir in the jth system factor comparison sequence, wherein the hydrocarbon generation strength of the hydrocarbon source rock, 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-lithologic body are respectively recorded as { 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 a numerical value obtained after the oil and gas reserves of the ith fault-lithology oil and gas reservoir are normalized,
Figure BDA0003333122600000035
the average value of the oil and gas reserves of all fault-lithology oil and gas reservoirs is obtained;
non-dimensionalization departmentObtaining a new series after treatment, wherein the new series comprises the oil gas reserves, the hydrocarbon generation strength of the hydrocarbon source rock, 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 fault-sand contact length series which are respectively marked as
Figure BDA0003333122600000036
Figure BDA0003333122600000037
(3) Determining a correlation coefficient beta between a system characteristic reference number series (namely an oil gas reserves number series of the fault-lithologic oil and gas reservoir) and a system factor comparison number series (namely each reservoir forming influence factor number series); 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-lithologic hydrocarbon reservoir in the jth system factor comparison sequence, 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 weight coefficient of the jth reservoir forming influence factor of the fault-lithology reservoir;
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 the 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:
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, using equation (R) to calculate out decision index when m takes values of 3, 4, 5, 6 and 7 respectively
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 value 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
The comprehensive prediction model corresponding to the minimum value of the judgment index numerical value is used as the 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 hydrocarbon 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 hydrocarbon 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 is further described below with reference to the accompanying drawings:
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 do not 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 are 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 the sample property 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×K2E is the hydrocarbon generation strength of the hydrocarbon source rock (kg/m)2) 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 coefficient of restitution; k2Hydrocarbon production rate (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 body contact length (m) refers to the sand body length of the contact part of the sedimentary sand body developing 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-lithologic reservoir influence 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 is 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.
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) taking the oil gas reserves and reservoir forming influence factors of the fault-lithology oil and gas reservoir as parameters for representing system characteristics, wherein the parameters comprise a system characteristic reference number sequence and a system factor comparison number 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 above-mentionedThe system factor comparison series is composed of a series of reservoir-forming influence factors of the fault-lithology hydrocarbon reservoir, 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 a numerical value obtained after the oil and gas reserves of the ith fault-lithology oil and gas reservoir 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 number obtained in sequence according to the calculation method of the formula (ii) is:
hydrocarbon-formation strength of hydrocarbon source rock:
Figure BDA0003333122600000103
moving speed 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 of (1), 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-gas reserves number series of the fault-lithologic oil-gas reservoir) and a system factor comparison number series (namely an influence factor number series of each reservoir formation) according to a formula, wherein taking the influence factor of the sand rock porosity as an example, 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, according to the formula IV and the formula V, the correlation degrees of the hydrocarbon source rock hydrocarbon generation strength, the oil source fault activity rate, the sandstone porosity, the sandstone permeability, the sandstone area, the sandstone thickness and the fault-sandstone contact length with the fault-lithology reservoir oil and gas storage capacity are respectively:
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) is 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-lithologic hydrocarbon reservoir in the jth system factor comparison sequence, 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: b6＝0.17
Fault-sand contact length: b is7＝0.013
(3) And (4) sequentially calculating the weight coefficients of all the influence factors according to a formula (r):
Figure BDA0003333122600000132
in the formula, CjThe weight coefficient is the jth reservoir forming influence factor of the fault-lithology hydrocarbon reservoir;
calculating the weight coefficient of each influencing factor according to a formula, taking the influencing factor of the porosity of the sandstone as an example, and specifically calculating the weight coefficient according to the following steps:
Figure BDA0003333122600000133
similarly, the hydrocarbon generation intensity of the hydrocarbon source rock, 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 weight coefficient of the fault-sand contact length are sequentially obtained by using a formula (r):
hydrocarbon-formation strength of hydrocarbon source rock: c1＝0.032
Moving rate of oil source fault: c2＝0.143
Sand rock mass porosity: 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 the screened m high-relevance accumulation influencing factors and the weight coefficients thereof, according to the formulaNinthly, establishing a comprehensive prediction model of the oil and gas reserves of the fault-lithologic oil and gas reservoir:
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 hydrocarbon reservoir; x'jiThe numerical values after the unified normalization processing of all the reservoir forming influence factors of each oil and gas reservoir are obtained, wherein indexes which are not selected as dominant influence 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 of formulajIs 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 of formula0＝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 of formula0＝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, according to the sequencing result of the correlation degree in the third step, 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 intensity of the hydrocarbon source rock, the sandstone porosity and the fault-sand body contact length as dominant influence factors, and establishing a comprehensive prediction model of the oil and gas reserves of the fault-lithologic oil and gas reservoir according to the formula ninthly:
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, using equation (R) to calculate out decision index when m takes values of 3, 4, 5, 6 and 7 respectively
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 value 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 decision indexes when m takes values of 3, 4, 5, 6 and 7 are calculated in turn by using the formula (R)
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 sixth step
Figure BDA00033331226000001717
Is sequentially from high to low
Figure BDA00033331226000001718
Index of decision
Figure BDA00033331226000001719
And minimum, the corresponding comprehensive prediction model:
Figure BDA00033331226000001720
and (3) establishing a reserve prediction model of the fault-lithology hydrocarbon reservoir for the research area.
Taking a 28-2 structure in the Bohai as an example, selecting the sand body area, the sand body thickness, the sand body porosity and the oil source fault activity rate (shown in a table 2) of the 28-2 structure in the Bohai as a collection advantage influence factor, and bringing the collection advantage influence factor into a comprehensive prediction model:
Figure BDA00033331226000001721
Figure BDA00033331226000001722
in the method, the predicted reserves of the 28-2 structure in the Bohai are 4204.34 ten thousand tons, the predicted reserves are close to the actual oil gas reserves of 4500 ten thousand tons, and the relative error is only 6.5%, so that the reserves prediction model constructed by the method has reasonability and effectiveness. Otherwise, a 28-2 structure in the Bohai is obtained by adopting an oil-gas reservoir oil-gas reserve prediction model established by a traditional method and subjected to expert gradingCompared with the actual oil gas reserves of 4500 million tons, the reserves prediction model established by the method has higher accuracy and better effect on oil gas reserves prediction of the fault-lithology oil gas reservoir, and is more favorable for guiding oil gas exploration and target optimization of the fault-lithology oil gas reservoir.
28-2 in Bohai of Table 2 is constructed to store numerical values after influence factors are normalized 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 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):
Figure FDA0003662176900000011
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; 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 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…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 FDA0003662176900000021
Figure FDA0003662176900000022
in the formula (I), the compound is shown in the specification,
Figure FDA0003662176900000023
is the normalized value of the ith fault-lithology reservoir in the jth system factor comparison array,
Figure FDA0003662176900000024
is the average of the jth systematic factor,
Figure FDA0003662176900000025
is the value of the i-th fault-lithology hydrocarbon reservoir after the oil gas reserves are normalized,
Figure FDA0003662176900000026
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 FDA0003662176900000027
Figure FDA0003662176900000028
(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:
Figure FDA0003662176900000029
where ρ is a resolution coefficient, where ρ is 0.5, i is 1,2 … n, j is 1,2,3 … 7,
Figure FDA00036621769000000210
representing a series of system reference signatures
Figure FDA00036621769000000211
Comparing the ith value with the jth system factor
Figure FDA00036621769000000212
Absolute difference of the ith value, and
Figure FDA00036621769000000213
the minimum value in the sequence of absolute differences is indicated,
Figure FDA00036621769000000214
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 FDA00036621769000000215
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 FDA0003662176900000031
in the formula, BjIs 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)):
Figure FDA0003662176900000032
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 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:
Figure FDA0003662176900000033
in the formula (I), the compound is shown in the specification,
Figure FDA0003662176900000034
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 is performed in SPSS software and yields the regression coefficients k for each term of the model0、k1、k2…kjAnd a relationship diagram;
sixthly, using equation (R) to calculate out decision index when m takes values of 3, 4, 5, 6 and 7 respectively
Figure FDA0003662176900000041
And
Figure FDA0003662176900000042
Figure FDA0003662176900000043
in the formula (I), the compound is shown in the specification,
Figure FDA0003662176900000044
representing a judgment index, wherein m represents the number of dominant influence factors corresponding to the judgment index;
Figure FDA0003662176900000045
a calculated value representing the hydrocarbon reserve of the ith fault-lithology hydrocarbon reservoir; qiRepresenting the oil and gas reserves of the ith fault-lithology oil and gas reservoir;
Figure FDA0003662176900000046
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 FDA0003662176900000047
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.
CN202111286572.7A 2021-11-02 2021-11-02 Construction method of fault-lithology oil and gas reservoir oil and gas reserve prediction model Active CN114021804B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111286572.7A CN114021804B (en) 2021-11-02 2021-11-02 Construction method of fault-lithology oil and gas reservoir oil and gas reserve prediction model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111286572.7A CN114021804B (en) 2021-11-02 2021-11-02 Construction method of fault-lithology oil and gas reservoir oil and gas reserve prediction model

Publications (2)

Publication Number Publication Date
CN114021804A CN114021804A (en) 2022-02-08
CN114021804B true CN114021804B (en) 2022-07-19

Family

ID=80059622

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111286572.7A Active CN114021804B (en) 2021-11-02 2021-11-02 Construction method of fault-lithology oil and gas reservoir oil and gas reserve prediction model

Country Status (1)

Country Link
CN (1) CN114021804B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6101447A (en) * 1998-02-12 2000-08-08 Schlumberger Technology Corporation Oil and gas reservoir production analysis apparatus and method
CN104750884A (en) * 2013-12-26 2015-07-01 中国石油化工股份有限公司 Quantitative evaluation method of shale oil and gas enrichment index on the basis of multi-factor nonlinear regression
CN106156452A (en) * 2015-03-24 2016-11-23 中国石油化工股份有限公司 A kind of Reservoir Analysis method
CN107784599A (en) * 2016-08-26 2018-03-09 中国石油化工股份有限公司 A kind of method that quantitative calculating is carried out to prognostic reserves upgrading reliability
CN110489809A (en) * 2019-07-24 2019-11-22 中国石油天然气股份有限公司 A kind of basin petroleum resources overall evaluation method and device
CN111101924A (en) * 2019-11-15 2020-05-05 中国石油天然气股份有限公司大港油田分公司 Lithologic reservoir dominant facies band prediction method and device
CN111784065A (en) * 2020-07-09 2020-10-16 东北石油大学 Oil well productivity intelligent prediction method based on grey correlation
CN112966380A (en) * 2021-03-10 2021-06-15 东北石油大学 Method for determining steep slope sand body type gathering ridge oil and gas reserve

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9767421B2 (en) * 2011-10-26 2017-09-19 QRI Group, LLC Determining and considering petroleum reservoir reserves and production characteristics when valuing petroleum production capital projects

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6101447A (en) * 1998-02-12 2000-08-08 Schlumberger Technology Corporation Oil and gas reservoir production analysis apparatus and method
CN104750884A (en) * 2013-12-26 2015-07-01 中国石油化工股份有限公司 Quantitative evaluation method of shale oil and gas enrichment index on the basis of multi-factor nonlinear regression
CN106156452A (en) * 2015-03-24 2016-11-23 中国石油化工股份有限公司 A kind of Reservoir Analysis method
CN107784599A (en) * 2016-08-26 2018-03-09 中国石油化工股份有限公司 A kind of method that quantitative calculating is carried out to prognostic reserves upgrading reliability
CN110489809A (en) * 2019-07-24 2019-11-22 中国石油天然气股份有限公司 A kind of basin petroleum resources overall evaluation method and device
CN111101924A (en) * 2019-11-15 2020-05-05 中国石油天然气股份有限公司大港油田分公司 Lithologic reservoir dominant facies band prediction method and device
CN111784065A (en) * 2020-07-09 2020-10-16 东北石油大学 Oil well productivity intelligent prediction method based on grey correlation
CN112966380A (en) * 2021-03-10 2021-06-15 东北石油大学 Method for determining steep slope sand body type gathering ridge oil and gas reserve

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
"A New Approach to Predict the Location of Petroleum Reservoirs Using FFNN";Jaber, N.S.et al.;《2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)》;20191116;第168-175页 *
基于综合权重法的页岩气储量评价方法探讨;张玲等;《石油实验地质》;20170928(第05期);第694-699页 *
渤海湾盆地南堡凹陷油气成藏区带定量预测与评价;董月霞等;《石油学报》;20151215;第19-35页 *

Also Published As

Publication number Publication date
CN114021804A (en) 2022-02-08

Similar Documents

Publication Publication Date Title
CN109653725B (en) Mixed reservoir flooding degree logging interpretation method based on sedimentary microfacies and rock facies
WO2008028122A9 (en) History matching and forecasting in the production of hydrocarbons
CN107038516B (en) Quantitative evaluation method for water-flooding development effect of medium-permeability complex fault block oil reservoir
CN109800954B (en) Reservoir evaluation method based on logging data
CN104239743B (en) The method and apparatus for determining lithologic reservoir forming probability
CN111461386A (en) Shale gas sweet spot prediction method based on BP neural network
CN114818076B (en) Machine learning-based fault closed hydrocarbon column height evaluation method
CN110442951B (en) Hydrocarbon source rock total organic carbon content prediction method considering density factor
CN110242291A (en) A kind of superpower heterogeneous oil and gas reservoir heterogeneity characterizing method
CN113343574A (en) Mishrif group lithology logging identification method based on neural network
CN112459776A (en) Quantitative evaluation method and device for deep and ultra-deep carbonate rock ancient oil reservoirs
CN112034521A (en) Method for predicting overpressure of under-compacted and hydrocarbon-production mixed formation
CN114021804B (en) Construction method of fault-lithology oil and gas reservoir oil and gas reserve prediction model
CN108362621B (en) Method for simulating and calculating clastic rock reservoir porosity based on diagenetic facies logging identification technology
Zhang et al. The application of machine learning under supervision in identification of shale lamina combination types—A case study of Chang 73 sub-member organic-rich shales in the Triassic Yanchang Formation, Ordos Basin, NW China
CN111460725B (en) Shale gas dessert prediction based on multi-level fuzzy recognition
CN111199107A (en) Novel evaluation method of deltaic acid sandstone traps
CN106568918B (en) Shale organic carbon content TOC prediction method
CN111325441A (en) Quantitative evaluation method for shale gas target storage conditions
CN112966380B (en) Method for determining steep slope sand body type gathering ridge oil and gas reserve
CN112394401A (en) Clastic rock trap hydrocarbon-containing evaluation method based on risk probability
CN112612935B (en) Complete logging data acquisition method based on self-reasoning model
CN114482995A (en) Fine determination method for argillaceous content of fine-grain sediment
CN114943060B (en) Shale gas total organic carbon prediction method based on deep learning and interpolation regression
CN114076991B (en) Characterization method of macroscopic heterogeneity of reservoir

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant