WO2023221491A1 - 一种氦气资源规模序列的确定方法、装置和设备 - Google Patents
一种氦气资源规模序列的确定方法、装置和设备 Download PDFInfo
- Publication number
- WO2023221491A1 WO2023221491A1 PCT/CN2022/139580 CN2022139580W WO2023221491A1 WO 2023221491 A1 WO2023221491 A1 WO 2023221491A1 CN 2022139580 W CN2022139580 W CN 2022139580W WO 2023221491 A1 WO2023221491 A1 WO 2023221491A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- helium
- gas
- reservoir
- reservoirs
- reserves
- Prior art date
Links
- 239000001307 helium Substances 0.000 title claims abstract description 687
- 229910052734 helium Inorganic materials 0.000 title claims abstract description 687
- SWQJXJOGLNCZEY-UHFFFAOYSA-N helium atom Chemical compound [He] SWQJXJOGLNCZEY-UHFFFAOYSA-N 0.000 title claims abstract description 687
- 238000000034 method Methods 0.000 title claims abstract description 70
- 239000007789 gas Substances 0.000 claims abstract description 441
- 238000011160 research Methods 0.000 claims abstract description 91
- 238000009826 distribution Methods 0.000 claims abstract description 61
- 238000004088 simulation Methods 0.000 claims abstract description 48
- 238000009825 accumulation Methods 0.000 claims description 116
- 230000001186 cumulative effect Effects 0.000 claims description 58
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 52
- 239000003345 natural gas Substances 0.000 claims description 26
- 238000005315 distribution function Methods 0.000 claims description 24
- 230000006870 function Effects 0.000 claims description 24
- 238000004590 computer program Methods 0.000 claims description 13
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 8
- 238000003860 storage Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 5
- 230000002349 favourable effect Effects 0.000 abstract description 4
- 238000004458 analytical method Methods 0.000 abstract description 3
- 230000035508 accumulation Effects 0.000 description 83
- 238000010586 diagram Methods 0.000 description 16
- 238000011156 evaluation Methods 0.000 description 11
- 230000008569 process Effects 0.000 description 8
- 238000011161 development Methods 0.000 description 7
- 239000011435 rock Substances 0.000 description 6
- 230000015572 biosynthetic process Effects 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 5
- 238000011065 in-situ storage Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000007476 Maximum Likelihood Methods 0.000 description 4
- 229910052770 Uranium Inorganic materials 0.000 description 4
- JFALSRSLKYAFGM-UHFFFAOYSA-N uranium(0) Chemical compound [U] JFALSRSLKYAFGM-UHFFFAOYSA-N 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- ZSLUVFAKFWKJRC-IGMARMGPSA-N 232Th Chemical compound [232Th] ZSLUVFAKFWKJRC-IGMARMGPSA-N 0.000 description 2
- 101100348341 Caenorhabditis elegans gas-1 gene Proteins 0.000 description 2
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- 101100447658 Mus musculus Gas1 gene Proteins 0.000 description 2
- 229910052776 Thorium Inorganic materials 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000013508 migration Methods 0.000 description 2
- 230000005012 migration Effects 0.000 description 2
- 238000004445 quantitative analysis Methods 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 1
- 239000004215 Carbon black (E152) Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910002092 carbon dioxide Inorganic materials 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 229910001873 dinitrogen Inorganic materials 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005755 formation reaction Methods 0.000 description 1
- 239000010438 granite Substances 0.000 description 1
- 229930195733 hydrocarbon Natural products 0.000 description 1
- 150000002430 hydrocarbons Chemical class 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 239000003921 oil Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000013488 ordinary least square regression Methods 0.000 description 1
- 230000005258 radioactive decay Effects 0.000 description 1
Images
Classifications
-
- 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
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/08—Probabilistic or stochastic CAD
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Definitions
- This application relates to the technical field of helium resource evaluation, and in particular to a method, device and equipment for determining the scale sequence of helium resources.
- China's helium resources account for 1.8% of the world's total, but its production only accounts for 0.3% of the world's total. China's dependence on foreign countries is extremely high (current data shows that it is greater than 95%).
- Helium resources have become an urgent strategic resource.
- helium resource evaluation it is important to understand the potential and distribution of helium resources in key areas at home and abroad, select favorable enrichment areas, and ensure national energy security, which has become an important research goal for technicians in this field.
- the helium percentage content method and the genesis method are mainly used to calculate helium reserves and resources.
- Zhang Fuli and others (2012) determined that the basin is highly helium-rich based on the composition characteristics of water-soluble gas components in the Weihe Basin.
- the amount of water-soluble helium resources is calculated using two methods: radioactive decay calculation method. Zhang Zuoxiang et al.
- the source rock attention should be paid to the uranium and thorium content of the rock formation.
- the current reference standard for natural gamma logging values is 120 to 108API.
- the source rock lithology is mainly shale, and gamma radioactivity contour maps are drawn accordingly.
- the model built by Brown (2010) was used to analyze the contents of uranium and thorium in helium-producing source rocks, limited to 500 Ma, to form an evaluation result of helium resources based on their origin. , further considering the reservoir porosity limit of 10% to obtain the final result.
- the present application is proposed to provide a method, device and equipment for determining a helium resource scale sequence that overcomes the above problems or at least partially solves the above problems.
- embodiments of the present application provide a method for determining the helium resource scale sequence, which may include:
- helium reserves of the helium gas reservoirs with known helium content in the known gas reservoirs and the helium reserves of each reservoir-forming combination determine the helium gas reservoirs with unknown helium content in the known gas reservoirs and their respective reservoir-forming combinations.
- the likelihood function distribution of the helium reserves of each accumulation combination of all helium gas reservoirs in the target research area is determined to simulate and construct the accumulation of the target research area.
- the helium content of different reservoir-forming combinations in the known gas reservoirs in the target study area is obtained to determine the helium gas reservoirs with known helium content in the known gas reservoirs and their respective reservoir-forming combinations.
- Helium reserves including:
- the helium content of the known helium content in the known gas reservoirs is determined using the percentage content method.
- the helium gas with unknown helium content in the known gas reservoir is determined based on the helium reserves of the helium gas reservoirs with known helium content in the known gas reservoir and the helium reserves of each reservoir-forming combination.
- the helium reserves of the reservoir and its various reservoir-forming combinations include:
- gas reservoir data of the same oil and gas field and/or nearby oil and gas fields with similar accumulation combinations of gas reservoirs with unknown helium content in the known gas reservoir; wherein, the gas reservoir data of the same oil and gas field and/or nearby oil and gas fields include: gas reservoirs Natural gas reserves and percentage of helium in gas reservoirs;
- analogies are made to determine the helium gas reservoir with unknown helium content in the known gas reservoir and the helium of each reservoir combination. gas reserves.
- the gas reservoir data of the same oil and gas field and/or nearby oil and gas fields for each reservoir-forming combination with unknown helium content in the known gas reservoir it also includes:
- the likelihood function distribution of the helium reserves of each accumulation combination of all helium gas reservoirs in the target research area is determined to simulate and construct the helium gas reserves.
- the cumulative gas reservoir model of the target study area is described, including:
- the minimum accumulation scale and maximum accumulation scale of the helium gas reservoir are brought into the Pareto distribution function, and the cumulative gas reservoir model of the target research area is simulated and constructed.
- the cumulative gas reservoir model of the target research area is constructed through simulation, it also includes:
- the distribution parameters, maximum accumulation scale and number of accumulation combinations of helium reservoirs in the Pareto distribution function are assigned least square weight values to improve large-scale helium reservoirs in the accumulation gas reservoir model. Impact weight on the cumulative gas reservoir model.
- the minimum accumulation scale and the maximum accumulation scale of the helium gas reservoir are brought into the Pareto distribution function, and the cumulative gas reservoir model of the target research area is simulated, including:
- a cumulative gas reservoir model of the target study area is constructed.
- the method further includes:
- the identified helium gas reservoirs are sorted and numbered according to their helium reserve scales to facilitate simulation and construction of the cumulative gas reservoir model.
- perform statistical simulation based on the cumulative gas reservoir model to obtain all helium reservoirs and their helium reserves in the target research area to determine the scale sequence of the helium resources including:
- Monte Carlo simulation method was used to conduct statistical simulation to obtain all helium reservoirs and their helium reserves in the target research area; all helium reservoirs and their helium reserves in the target research area include : Helium reservoirs and helium reserves within the maximum reservoir scale, as well as unknown helium reservoirs and helium resources under different probability conditions;
- the scale of helium resources in the target research area is obtained sequence.
- embodiments of the present application provide a device for determining a helium resource scale sequence, which may include:
- the data acquisition module is used to obtain the helium content of different accumulation combinations in known gas reservoirs in the target research area;
- the first determination module is used to determine the helium reserves of helium gas reservoirs with known helium content in the known gas reservoir and their respective reservoir combinations based on the helium gas contents of different reservoir-forming combinations in the known gas reservoir. ;
- the second determination module is used to determine the helium with unknown helium content in the known gas reservoir based on the helium reserves of the helium gas reservoirs with known helium content in the known gas reservoir and the helium reserves of each reservoir combination.
- the third determination module is used to determine the helium reserves of each helium gas reservoir combination based on the known helium content in the known gas reservoir, and the helium gas reserves of each helium gas reservoir with unknown helium content in the known gas reservoir.
- Helium reserves of reservoir-forming combinations determine the helium reserves of each reservoir-forming combination of helium reservoirs in the known gas reservoirs in the target research area;
- the simulation building module is used to determine the likelihood function distribution of the helium reserves of each accumulation combination of all helium gas reservoirs in the target research area based on the helium reserves of each accumulation combination of the helium gas reservoir, to simulate and construct the helium gas reserves.
- the cumulative gas reservoir model of the target study area is described;
- a statistical simulation module is used to perform statistical simulation based on the cumulative gas reservoir model to obtain all helium reservoirs and their helium reserves in the target research area, so as to determine the scale sequence of the helium resources.
- embodiments of the present application provide a computer-readable storage medium on which a computer program is stored.
- the program is executed by a processor, the method for determining the helium resource scale sequence described in the first aspect is implemented.
- embodiments of the present application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor.
- the processor executes the program, the first aspect is implemented.
- the embodiments of this application provide a method, device and equipment for determining the scale sequence of helium resources.
- This method first uses the known gas reservoirs in the target research area as sample data, and then obtains the data of different reservoir combinations in the known gas reservoirs. After determining the helium content, determine the helium reserves of each reservoir-forming combination of the helium reservoir with known helium content in the known gas reservoir; when collecting data and predicting helium reservoir reserves, perform quantitative analysis based on each reservoir-forming combination. The prediction results are more accurate, improving the accuracy of helium resource evaluation.
- the helium gas reservoirs with unknown helium content in the known gas reservoirs and the helium reserves of each reservoir-forming combination are determined.
- the helium reserves of each accumulation combination of helium reservoirs in the known gas reservoirs in the target study area are determined.
- a cumulative gas reservoir model of the helium reservoir in the target study area is constructed based on the simulation of the known helium reservoir, and finally a statistical simulation is performed on the cumulative gas reservoir model to estimate the helium reservoir and its helium content in the unknown gas reservoir in the target study area.
- This method builds a cumulative gas reservoir model through simulation, and then predicts the unknown helium reservoirs and their reserve scale in the research area, which is helpful for the helium resource potential and Analyze the distribution and select favorable enrichment areas to ensure the development and use of helium resources. Furthermore, this method can be used as a reliable basis for helium resource and helium asset assessment, long-term development planning of helium-containing gas fields, and integration of the entire helium industry chain.
- Figure 1 is a schematic flow chart of the method for determining the helium resource scale sequence provided in the embodiment of the present application
- FIG. 2 is a specific flow diagram of step S11;
- FIG. 3 is a specific flow diagram of step S12;
- FIG. 4 is a specific flow diagram of step S14;
- FIG. 5 is a specific flow diagram of step S143;
- FIG. 6 is a specific flow diagram of step S15;
- Figure 7 is an example of the free gas helium concentration histogram and cumulative distribution diagram in the study area provided in the embodiment of this application;
- Figure 8 is a schematic diagram of the truncated Pareto distribution provided in the embodiment of the present application.
- Figure 9 is a schematic diagram of the cumulative distribution of helium gas reservoirs provided in the embodiment of the present application.
- Figure 10 is a schematic structural diagram of a device for determining a helium resource scale sequence provided in an embodiment of the present application.
- the embodiment of the present application provides a method for determining the helium resource scale sequence.
- the method may include the following steps:
- Step S11 Obtain the helium content of different reservoir combinations in the known gas reservoirs in the target research area to determine the helium reserves of the helium reservoirs with known helium content in the known gas reservoirs and the helium reserves of each reservoir combination. .
- the known gas reservoirs in the target research area are used as sample data to obtain the helium content of different reservoir combinations in the known gas reservoirs.
- the above-mentioned gas reservoirs in the embodiments of the present application may be natural gas reservoirs, carbon dioxide gas reservoirs, nitrogen gas reservoirs, etc., which are not specifically limited in the embodiments of the present application.
- a gas sample when obtaining sample data, can be obtained from a known gas reservoir first, and then a helium detection device can be used to detect the helium content of the gas sample.
- a mass spectrometer can be used to detect the helium content of the gas sample.
- Content testing In order to improve the accuracy of helium content detection, the average value can be taken after multiple measurements.
- Step S12 Determine the helium gas reservoirs with unknown helium content in the known gas reservoirs and their respective reservoir-forming combinations based on the helium reserves of the helium gas reservoirs with known helium content in the known gas reservoirs and their respective reservoir-forming combinations. of helium reserves.
- This step is based on the analogy method to calculate the helium reserves of helium gas reservoirs with unknown helium content in known gas reservoirs and their respective reservoir combinations.
- the helium gas reservoir with known helium content in the known gas reservoir in the embodiment of the present application may be referred to as the known helium-containing gas reservoir or the first helium gas reservoir.
- the unknown helium gas in the known gas reservoir may be referred to as the first helium gas reservoir.
- a helium reservoir with unknown helium content may be referred to as a known helium reservoir with unknown helium content or a second helium reservoir.
- Step S13 based on the helium reserves of each reservoir-forming combination of helium reservoirs with known helium content in known gas reservoirs, and the helium reserves of each reservoir-forming combination of helium reservoirs with unknown helium content in known gas reservoirs, Determine the helium reserves of each accumulation combination of helium reservoirs in the known gas reservoirs in the target study area.
- the helium reserves of each reservoir-forming combination of helium reservoirs determined by different calculation methods in the known gas reservoir are summarized, that is, the helium reserves of the above-mentioned first helium reservoir and second helium reservoir in each reservoir-forming combination are summarized. gas reserves.
- Step S14 Based on the helium reserves of each accumulation combination of the helium gas reservoir, determine the likelihood function distribution of the helium reserves of each accumulation combination of all helium gas reservoirs in the target research area, so as to simulate and build a cumulative gas reservoir model of the target research area .
- This step is to simulate the cumulative gas reservoir model of the helium gas reservoir in the target research area to prepare for subsequent simulation evaluation.
- Step S15 Perform statistical simulation based on the cumulative gas reservoir model to obtain all helium reservoirs and their helium reserves in the target research area to determine the scale sequence of helium resources.
- This step is to perform statistical simulation on the cumulative gas reservoir model to estimate the helium reservoir and its helium reserves in the unknown gas reservoirs in the target research area, and finally determine the scale sequence of helium resources in the target research area.
- the known gas reservoirs in the target research area are first used as sample data. After obtaining the helium content of different reservoir combinations in the known gas reservoirs, the known helium content in the known gas reservoirs is determined. Helium reserves of each accumulation combination of helium gas reservoirs; when collecting data and predicting helium reservoir reserves, quantitative analysis is performed based on each accumulation combination, and the prediction results are more accurate, which improves the accuracy of helium resource evaluation.
- the helium gas reservoirs with unknown helium content in the known gas reservoirs and the helium reserves of each reservoir-forming combination are determined.
- the helium reserves of each accumulation combination of helium reservoirs in the known gas reservoirs in the target study area are determined.
- a cumulative gas reservoir model of the helium reservoir in the target study area is constructed based on the simulation of the known helium reservoir, and finally a statistical simulation is performed on the cumulative gas reservoir model to estimate the helium reservoir and its helium content in the unknown gas reservoir in the target study area.
- This method builds a cumulative gas reservoir model through simulation, and then predicts the unknown helium reservoirs and their reserve scale in the research area, which is helpful for the helium resource potential and Analyze the distribution and select favorable enrichment areas to ensure the development and use of helium resources.
- This method can be used as a reliable basis for helium resource and helium asset assessment, long-term development planning of helium-containing gas fields, and integration of the entire helium industry chain.
- step S11 may specifically include:
- Step S111 Obtain the helium content of different reservoir combinations in the known gas reservoirs in the target research area.
- Step S112 Determine the helium content of the known helium content in the known gas reservoir based on the helium content of different reservoir combinations in the known gas reservoir in the target research area and the natural gas reserves of the known gas reservoir using the percentage content method.
- the helium reserves of each accumulation combination of the helium gas reservoir with known helium content in the above-mentioned known gas reservoir can be determined by the following formula I:
- ⁇ He represents the helium reserve of the target gas reservoir, in m 3 ;
- ⁇ gas represents the helium natural gas reserve in the target gas reservoir, in m 3 ;
- C 1 represents the percentage content of helium in the target gas reservoir, in vol%.
- step S12 can be implemented through the following steps:
- Step S121 Obtain gas reservoir data of the same oil and gas field and/or nearby oil and gas fields with similar accumulation combinations of unknown helium content in the known gas reservoir; wherein, the gas reservoir data of the same oil and gas field and/or nearby oil and gas fields include: gas Natural gas reserves and helium content of the gas reservoir.
- Step S122 Determine the helium reservoir similarity coefficients of each reservoir-forming combination of gas reservoirs with unknown helium content in known gas reservoirs and gas reservoirs in the same oil and gas field and/or nearby oil and gas fields.
- Step S123 Based on the gas reservoir data and the helium reservoir similarity coefficient of the same oil and gas field and/or nearby oil and gas fields, make an analogy to determine the helium reserves of helium gas reservoirs with unknown helium content in known gas reservoirs and their respective reservoir-forming combinations.
- the helium reserves of each reservoir combination of the unknown helium content in the above-mentioned known gas reservoir can be determined by the following formula II:
- ⁇ He1 represents the helium reserve of the target gas reservoir, in m 3 ;
- ⁇ gas1 represents the helium natural gas reserve of the target gas reservoir, in m 3 ;
- C represents the percentage content of helium in the analog gas reservoir, in vol%
- ⁇ represents the similarity coefficient between two helium-containing gas reservoirs, which is dimensionless.
- step S121 before performing the above step S121, it is also necessary to analyze the helium reservoir formation characteristics of each accumulation combination with unknown helium content in the known gas reservoir to determine whether it is consistent with the unknown helium content in the known gas reservoir.
- Each accumulation combination with helium content forms gas reservoirs with similar reservoir characteristics.
- this step is to analyze the accumulation characteristics of different helium reservoirs for helium-containing gas reservoirs with unknown helium content, and determine it by analogy with other gas reservoirs in the same gas field or nearby gas fields with known helium reserves.
- Helium reserves of helium reservoirs with unknown helium content are to analyze the accumulation characteristics of different helium reservoirs for helium-containing gas reservoirs with unknown helium content, and determine it by analogy with other gas reservoirs in the same gas field or nearby gas fields with known helium reserves.
- step S13 is to summarize the accumulation combination of the first helium gas reservoir in the above-mentioned step S11 and the second helium gas reservoir in step S12, which can be determined by the following formula III:
- N represents the number of reservoir-forming combinations, the unit is units;
- ⁇ He1 represents the summary helium reserves of the reservoir-forming combination, in m 3 ;
- ⁇ He2 represents the helium reserves of a single accumulation combination, in m 3 .
- the simulation and construction of the cumulative gas reservoir model of the target research area in the above step S14 may specifically include the following steps:
- Step S141 Based on the known helium gas reservoirs and the helium reserves of each reservoir-forming combination, determine the likelihood function distribution of the helium reserves of each reservoir-forming combination for all helium reservoirs in the target study area.
- n the number of reservoir-forming combinations, the unit is units;
- L represents the likelihood probability, dimensionless
- x 1 , x 2 , x 3 ,..., x n represent the helium gas reservoir in the sample
- X 1 , X 2 ,..., X n represent discrete random variables
- P represents probability, dimensionless
- ⁇ represents the helium gas reserve in the helium reservoir, the unit is m 3 .
- Step S142 Based on the likelihood function distribution of helium reserves of each accumulation combination of all helium reservoirs in the target research area, determine the minimum accumulation scale and the maximum accumulation scale of the helium reservoir in the target research area.
- finding the maximum likelihood value of parameter ⁇ is the maximum likelihood estimation method, which is to select the parameter value that maximizes L( ⁇ ) within the possible value range of parameter ⁇ as the estimated value of parameter ⁇ .
- the maximum likelihood function refers to formula V as follows:
- Step S143 Bring the minimum accumulation scale and maximum accumulation scale of the helium gas reservoir into the Pareto distribution function, and simulate and construct a cumulative gas reservoir model of all helium gas reservoirs in the target research area.
- the distribution of helium gas reservoirs in the target study area in the embodiment of this application can be represented by the truncated Pareto distribution function (TPD).
- TPD truncated Pareto distribution function
- the truncated Pareto distribution, its gas reservoir prediction and empirical non-normalized cumulative distribution function Show good correspondence. Refer to formula VI as follows:
- ⁇ is a distribution parameter, dimensionless
- ⁇ is the helium gas reservoir reserve, the unit is m 3 ;
- ⁇ 0 and ⁇ max are respectively the minimum and maximum expected accumulation scale of naturally accumulated helium reservoirs.
- step S142 after executing the above step S142, it may also include: assigning least square weights to the distribution parameters of the helium reservoir, the maximum reservoir scale, and the number of reservoir combinations in the Pareto distribution function. value to increase the weight of the influence of large-scale helium reservoirs on the cumulative gas reservoir model in the cumulative gas reservoir model.
- step S143 may specifically include the following steps:
- Step S1431 Enter the minimum accumulation scale and the maximum accumulation scale of the helium gas reservoir into the Pareto distribution function.
- Step S1432 Preset a predetermined number of maximum helium gas reservoir accumulation areas as distribution parameters for helium gas reservoir distribution.
- the largest helium reservoir will be discovered first. Therefore, for zones with a higher degree of exploration, it can be assumed that a certain number of maximum helium reservoir accumulation areas have been discovered, and the parameters of the distribution of continuous-scale helium reservoirs can use the subset m (m> 3), the stability of parameter estimates can be used as a sufficient criterion for selecting the value of m.
- the non-standardized cumulative distribution function is calculated by formula (VIII).
- N is the total number of times accumulated in the system, number
- ⁇ is the helium gas reserve in the helium reservoir, the unit is m 3 .
- Step S1433 Simulate the Pareto distribution function to identify helium reservoirs and helium reserves that are greater than a predetermined number in the target research area.
- the identified helium reservoirs are also sorted and numbered according to their helium reserve scales to facilitate simulation and construction of a cumulative gas reservoir model.
- all helium reservoirs identified in a specific helium system will be numbered starting from the largest scale, assuming that at least m helium reservoirs are identified in the largest scale level, and ⁇ 1 ⁇ ⁇ 2 ⁇ ... ⁇ ⁇ m .
- Step S1434 Construct a cumulative gas reservoir model of all helium reservoirs in the target research area based on the determined helium reservoir, that is, helium reserve simulation.
- a series of cumulative gas reservoir models are generated using simulation methods. Simulations are performed in each case until m gas reservoirs are obtained whose reserves are greater than the reserves ⁇ (m) of the mth gas reservoir in the natural cluster. For some gas reservoirs with reserves less than ⁇ (m), the helium reservoir reserves can be calculated based on the empirical distribution of helium concentration.
- step S15 may specifically include the following steps:
- Step S151 Perform statistical simulation using the Monte Carlo simulation method based on the cumulative gas reservoir model to obtain all helium reservoirs and their helium reserves in the target research area; all helium reservoirs and their helium reserves in the target research area include: maximum formation Helium reservoirs and helium reserves within the reservoir scale, as well as unknown helium reservoirs and helium reserves under different probability conditions.
- This step counts the expected number of discovered gas reservoirs under different minimum gas reservoir scale conditions. For example, through Monte Carlo simulation 5,000 times, the geological resource distribution of free gas and helium within the maximum free gas reserve scale is generated, as well as the total in-situ prospective resources of free gas and helium in oil and gas reservoirs to be discovered under different probability conditions. Clarify different levels of helium reserves and resources.
- Step S152 Based on the helium reservoirs and helium reserves within the maximum reservoir scale, the unknown helium reservoirs and helium resources under different probability conditions, and the preset probability values, obtain the helium resources in the target research area. scale sequence.
- the embodiments of the present application can be used to calculate helium-containing natural gas reserves with known helium content, known gas reservoir reserves with unknown helium content, and can also be used to calculate the distribution of helium-containing natural gas geological resources in gas reservoirs to be discovered. Helium reserves smaller than the minimum size will be truncated on the left side of the Pareto distribution.
- the empirical data used in this method can represent various helium reservoirs with different distribution characteristics, as well as the degree of research and understanding of these helium reservoirs, such as different stratigraphy, lithology, structures, and composite oil and gas reservoirs.
- the degree of deviation in reserves and resource estimates is related to the exploration maturity of the oil field.
- S105 uses the likelihood function to approximate the distribution, and then calculates the maximum likelihood number to be 100 million cubic meters according to the formulas (IV, V).
- S105 uses the simulation method to generate a series of simulated accumulation gas reservoirs according to formulas (VI, VII, VIII) after calculating the parameters of the truncated Pareto distribution (1). If the minimum helium reservoir size ⁇ 0 is 3 million cubic meters (the number of known gas reservoirs smaller than this size is 15), the estimated number of discovered gas reservoirs N is 12,250 (the number of discovered gas reservoirs is 336); If the minimum gas reservoir size ⁇ 0 is 30 million cubic meters (the number of gas reservoirs smaller than this size is known to be 317), it is expected that the number of discovered gas reservoirs N will be 1320, forming the free helium gas in the study area as shown in Figure 7 Concentration histograms and cumulative distribution plots.
- the original geological reserves distribution of helium is derived through the known helium reserves in the gas reservoir.
- the estimated values of the truncated Pareto distribution parameters are I of 1.98 and ⁇ max of 12 billion cubic meters.
- Figure 8 shows that for gas reservoirs with reserves less than 300 million cubic meters, there is a good correspondence between gas reservoir prediction and empirical non-normalized cumulative distribution functions.
- the embodiment of the present application also provides a device for determining helium resources.
- the device may include: a data acquisition module 101, a first determination module 102, a second determination module 103,
- the working principles of the third confirmation module 104, the simulation building module 105 and the statistical simulation module 106 are as follows:
- the data acquisition module 101 is used to obtain the helium content of different gas accumulation combinations in the known gas reservoirs in the target research area;
- the first determination module 102 is configured to determine the helium reserves of helium gas reservoirs with known helium content in the known gas reservoir and their respective reservoir combinations based on the helium gas contents of different reservoir-forming combinations in the known gas reservoir;
- the second determination module 103 is used to determine the helium gas reservoirs with unknown helium content in the known gas reservoirs and their helium reserves based on the helium reserves of the helium gas reservoirs with known helium content in the known gas reservoirs and their respective reservoir combinations. Helium reserves of each accumulation combination;
- the third determination module 104 is used to determine the helium reserves of each accumulation combination of helium gas reservoirs with known helium content in known gas reservoirs, and the helium gas reserves of each accumulation combination of helium gas reservoirs with unknown helium content in known gas reservoirs.
- Helium reserves determine the helium reserves of each reservoir-forming combination in the known gas reservoirs in the target study area;
- the simulation building module 105 is used to determine the likelihood function distribution of the helium reserves of each accumulation combination of all helium gas reservoirs in the target research area based on the helium reserves of each accumulation combination in the helium gas reservoir, so as to simulate and construct all the helium reserves in the target research area.
- the statistical simulation module 106 is used to perform statistical simulation based on the cumulative gas reservoir model to obtain all helium reservoirs and their helium reserves in the target research area, so as to determine the scale sequence of helium resources.
- the above-mentioned first determination module 102 is specifically configured to base on the helium content of different accumulation combinations in the known gas reservoirs in the target research area, and the natural gas reserves of the known gas reservoirs.
- the fractional content method determines the helium reserves of helium gas reservoirs with known helium content in known gas reservoirs and their respective reservoir-forming combinations.
- the data acquisition module 101 is also used to acquire gas reservoir data of the same oil and gas field and/or nearby oil and gas fields with similar accumulation combinations of gas reservoirs with unknown helium content in the known gas reservoir;
- the gas reservoir data of the same oil and gas field and/or nearby oil and gas fields include: gas reservoir natural gas reserves and gas reservoir helium percentage content;
- the second determination module 103 is used to determine the helium reservoir similarity coefficients of each gas reservoir combination with unknown helium content in the known gas reservoir and the gas reservoirs in the same oil and gas field and/or nearby oil and gas fields; and based on the same oil and gas field And/or the gas reservoir data of nearby oil and gas fields and the similarity coefficient of the helium gas reservoir are compared to determine the helium reserves of the helium gas reservoir with unknown helium content in the known gas reservoir and its respective reservoir combinations.
- the data acquisition module 101 before the data acquisition module 101 obtains gas reservoir data of similar oil and gas fields and/or nearby oil and gas fields, it also analyzes the characteristics of helium gas reservoirs for each reservoir-forming combination with unknown helium content in the known gas reservoirs. In order to determine the gas reservoirs with similar reservoir-forming characteristics as the reservoir-forming combinations with unknown helium content in the known gas reservoirs.
- the simulation building module 105 is specifically configured to: determine the helium reserves of all helium reservoirs in the target research area based on the known helium reservoirs and the helium reserves of each reservoir combination. Likelihood function distribution of helium reserves in each accumulation combination;
- the minimum accumulation scale and maximum accumulation scale of the helium gas reservoir are brought into the Pareto distribution function, and the cumulative gas reservoir model of all helium gas reservoirs in the target research area is simulated and constructed.
- the above-mentioned simulation building module 105 is also used to: assign the distribution parameters of the helium reservoir, the maximum reservoir scale, and the number of reservoir combinations in the Pareto distribution function to the least squares
- the weight value is used to increase the weight of the influence of large-scale helium gas reservoirs on the cumulative gas reservoir model in the cumulative gas reservoir model.
- the simulation building module 105 is also used to: bring the minimum accumulation scale and the maximum accumulation scale of the helium gas reservoir into the Pareto distribution function;
- a cumulative gas reservoir model of all helium reservoirs in the target research area is constructed.
- the simulation building module 105 identifies helium gas reservoirs and helium gas reserves that are greater than the predetermined number in the target research area, it is also used to: analyze the identified helium gas reservoirs according to their The helium reserve scales are sorted and numbered to facilitate the simulation and construction of the cumulative gas reservoir model.
- the statistical simulation module 106 is specifically configured to perform statistical simulation using the Monte Carlo simulation method based on the cumulative gas reservoir model to obtain all helium reservoirs and their helium reserves in the target research area.
- All helium reservoirs and helium reserves in the target research area include: helium reservoirs and helium reserves within the maximum reservoir scale, as well as unknown helium reservoirs and helium resources under different probability conditions;
- the scale of helium resources in the target research area is obtained sequence.
- embodiments of the present application also provide a computer-readable storage medium on which a computer program is stored.
- the program is executed by a processor, the method for determining the above-mentioned helium resource scale sequence is implemented.
- embodiments of the present application also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor.
- the processor executes the program, the above helium resource scale is achieved. How to determine the sequence.
- embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment that combines software and hardware aspects. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, magnetic disk storage and optical storage, etc.) embodying computer-usable program code therein.
- a computer-usable storage media including, but not limited to, magnetic disk storage and optical storage, etc.
- These computer program instructions may also be stored in a computer-readable memory that causes a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction means, the instructions
- the device implements the functions specified in a process or processes of the flowchart and/or a block or blocks of the block diagram.
- These computer program instructions may also be loaded onto a computer or other programmable data processing device, causing a series of operating steps to be performed on the computer or other programmable device to produce computer-implemented processing, thereby executing on the computer or other programmable device.
- Instructions provide steps for implementing the functions specified in a process or processes of a flowchart diagram and/or a block or blocks of a block diagram.
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geophysics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本申请公开了一种氦气资源规模序列的确定方法、装置和设备。该方法包括：获取目标研究区内的已知气藏中不同成藏组合的氦气含量，以确定已知气藏中的已知氦气含量的氦气藏及各成藏组合的氦气储量和未知氦气含量的氦气藏及各成藏组合的氦气储量；确定已知气藏中氦气藏各成藏组合的氦气储量；基于氦气藏各成藏组合的氦气储量，确定目标研究区内所有氦气藏的各成藏组合氦气储量的似然函数分布，以仿真构建目标研究区内所有氦气藏的累积气藏模型；基于累积气藏模型进行统计模拟，得到目标研究区内所有氦气藏及其氦气储量，以确定氦气资源的规模序列。该方法有助于对氦气资源潜力及分布进行分析，优选有利富集区，保障氦气资源开发和使用。
Description
相关申请
本申请要求于2022年05月18日递交的申请号为202210551884.4的中国发明专利申请的优先权，并引用其公开的内容作为本申请的一部分。
本申请涉及氦气资源评价技术领域，特别涉及一种氦气资源规模序列的确定方法、装置和设备。
中国氦气资源占全球比例为1.8％，而产量仅占全球比例0.3％，对外依存度极高(目前数据显示大于95％)，氦气资源已成为急缺战略资源。现今通过开展氦气资源评价，摸清国内外重点领域氦气资源潜力及分布，优选有利富集区，保障国家能源安全，成为本领域技术人员的重要攻关目标。发明人发现，尽管氦气与天然气赋存于同一圈闭中，但氦气成因、聚集、资源分布特征与和天然气资源毫无关联，氦气形成、运移及聚集有其自身规律，目前国内外尚没有成熟的氦气资源评价方法。
目前主要采用氦气百分含量法和成因法计算氦气储量及资源量。例如，张福礼(2012)等根据渭河盆地水溶气组分组成特征明确盆地富氦程度高，在氦同位素分析确定燕山期富铀花岗岩为氦气主要来源基础上，应用含氦水溶气计算法与铀放射性衰变计算法两种方法计算出水溶氦气资源量。张作祥等对天然气中氦气资源评价方法进行了讨论，提出国内主要应用容积法、蒙特卡洛法、残烃法、热模拟法，灰色系统预测法、油藏规模序列法及动态法对天然气资源进行预测，进而根据已发现氦气田氦气含量测试结果与天然气资源量相乘计算氦气资源量。J.Richard Bowersox(2019)介绍了针对美国中肯塔基的氦气资源评价，指出当地氦气资源评价中，氦含量的最低标准为0.2％(被认为存在商业价值)。认为氦气与油气资源在运移聚集上具有相似性，存在着近乎一致的圈闭成藏要素。其中对“源岩”，应关注岩层铀、钍的含量，目前采用的自然伽马测井值参考标准为120～108API。源岩岩性以页岩为主，并据此绘制伽马放射性等值线图。再计算产氦源岩体积的基础上，利用Brown(2010)所建模型，以500Ma为限，通过产氦源岩中铀、钍的含量进行分析，形成对氦气资源的基于成因的评价结果，进一步考以储层孔隙度10％为限，从而获得最终结果。
发明内容
发明人发现，氦气百分含量法虽然计算准确，但其依赖于氦气数据点的数量和质量、天然气储量的准确性，同时氦源岩体积的计算误差很大，资源评价结果可信度低，因此氦气百分含量法和成因法应用范围有限，难以大范围应用。发明人还发现，尽管氦气与天然气聚集在同一圈闭中，从研究区天然气中氦气含量和部分气藏中氦气的分布趋势对比认为，天然气储量与氦气储量分布无直接关联，氦气藏的含量及储量分布有其自身的规律。
鉴于上述问题，提出了本申请以便提供一种克服上述问题或者至少部分地解决上述问题的一种氦气资源规模序列的确定方法、装置和设备。
第一方面，本申请实施例提供了一种氦气资源规模序列的确定方法，可以包括：
获取目标研究区内的已知气藏中不同成藏组合的氦气含量，以确定已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量；
根据所述已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量，确定所述已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量；
基于所述已知气藏中已知氦气含量的氦气藏各成藏组合的氦气储量，和所述已知气藏中未知氦气含量的氦气藏各成藏组合的氦气储量，确定所述目标研究区内的已知气藏中氦气藏各成藏组合的氦气储量；
基于所述氦气藏各成藏组合的氦气储量，确定所述目标研究区内所有氦气藏的各成藏组合氦气储量的似然函数分布，以仿真构建所述目标研究区的累积气藏模型；
基于所述累积气藏模型进行统计模拟，得到所述目标研究区内所有氦气藏及其氦气储量，以确定所述氦气资源的规模序列。
可选的，所述获取目标研究区内的已知气藏中不同成藏组合的氦气含量，以确定已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量，包括：
获取目标研究区内的已知气藏中不同成藏组合的氦气含量；
基于所述目标研究区内的已知气藏中不同成藏组合的氦气含量，以及已知气藏的天然气储量以百分含量法确定已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量。
可选的，所述根据所述已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量，确定所述已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量，包括：
获取所述已知气藏中未知氦气含量的气藏各成藏组合相似的同一油气田和/或附近油气田的气藏数据；其中，同一油气田和/或附近油气田的气藏数据包括：气藏天然气储量和气藏氦气百分含量度；
确定所述已知气藏中未知氦气含量的气藏各成藏组合以及同一油气田和/或附近油气田的气藏的氦气藏相似系数；
基于所述同一油气田和/或附近油气田的气藏数据和所述氦气藏相似系数，进行类比 以确定所述已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量。
可选的，所述获取所述已知气藏中未知氦气含量的各成藏组合同一油气田和/或附近油气田的气藏数据之前，还包括：
对所述已知气藏中未知氦气含量的各成藏组合进行氦气藏成藏特征剖析，以确定与所述已知气藏中未知氦气含量的各成藏组合成藏特征相似的气藏。
可选的，所述基于所述氦气藏各成藏组合的氦气储量，确定所述目标研究区内所有氦气藏的各成藏组合氦气储量的似然函数分布，以仿真构建所述目标研究区的累积气藏模型，包括：
基于所述已知氦气藏及其各成藏组合的氦气储量，确定所述目标研究区内的所有氦气藏的各成藏组合氦气储量的似然函数分布；
基于所述目标研究区内的所有氦气藏的各成藏组合氦气储量的似然函数分布，确定所述目标研究区内的氦气藏的最小成藏规模和最大成藏规模；
将所述氦气藏的最小成藏规模和最大成藏规模带入帕累托分布函数，仿真构建所述目标研究区的累积气藏模型。
可选的，所述将所述氦气藏的最小成藏规模和最大成藏规模带入帕累托分布函数，仿真构建所述目标研究区的累积气藏模型之后，还包括：
将所述帕累托分布函数中的氦气藏的分布参数、最大成藏规模以及成藏组合个数赋予最小二乘权重值，以提升所述累积气藏模型中大规模类型的氦气藏对所述累积气藏模型的影响权重。
可选的，所述将所述氦气藏的最小成藏规模和最大成藏规模带入帕累托分布函数，仿真构建所述目标研究区的累积气藏模型，包括：
将所述氦气藏的最小成藏规模和最大成藏规模带入帕累托分布函数；
预设预定数量的最大氦气藏聚集区，以作为所述氦气藏分布的分布参数；
对所述帕累托分布函数进行模拟，以识别所述目标研究区内大于所述预定数量的氦气藏及氦气储量；
基于确定的氦气藏即氦气储量仿真构建所述目标研究区的累积气藏模型。
可选的，所述识别所述目标研究区内大于所述预定数量的氦气藏及氦气储量之后，还包括：
对识别出的所述氦气藏依据其氦气储量规模进行排序编号，以便于仿真构建所述累积气藏模型。
可选的，所述基于所述累积气藏模型进行统计模拟，得到所述目标研究区内所有氦气藏及其氦气储量，以确定所述氦气资源的规模序列，包括：
基于所述累积气藏模型以蒙特卡罗模拟法进行统计模拟，得到所述目标研究区内所有氦气藏及其氦气储量；所述目标研究区内所有氦气藏及其氦气储量包括：最大成藏规模范 围内的氦气藏和氦气储量，以及不同概率条件下的未知氦气藏和氦气资源量；
基于最大成藏规模范围内的氦气藏和氦气储量，不同概率条件下的未知氦气藏和氦气资源量，以及预设的概率值，得到所述目标研究区内氦气资源的规模序列。
第二方面，本申请实施例提供了一种氦气资源规模序列的确定装置，可以包括：
数据获取模块，用于获取目标研究区内的已知气藏中不同成藏组合的氦气含量；
第一确定模块，用于基于所述已知气藏中不同成藏组合的氦气含量，确定已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量；
第二确定模块，用于根据所述已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量，确定所述已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量；
第三确定模块，用于基于所述已知气藏中已知氦气含量的氦气藏各成藏组合的氦气储量，和所述已知气藏中未知氦气含量的氦气藏各成藏组合的氦气储量，确定所述目标研究区内的已知气藏中氦气藏各成藏组合的氦气储量；
仿真构建模块，用于基于所述氦气藏各成藏组合的氦气储量，确定所述目标研究区内所有氦气藏的各成藏组合氦气储量的似然函数分布，以仿真构建所述目标研究区的累积气藏模型；
统计模拟模块，用于基于所述累积气藏模型进行统计模拟，得到所述目标研究区内所有氦气藏及其氦气储量，以确定所述氦气资源的规模序列。
第三方面，本申请实施例提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如第一方面所述的氦气资源规模序列的确定方法。
第四方面，本申请实施例提供了一种计算机设备，包括存储器，处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如第一方面所述的氦气资源规模序列的确定方法。
本申请实施例提供的上述技术方案的有益效果至少包括：
本申请实施例提供了一种氦气资源规模序列的确定方法、装置和设备，该方法首先以目标研究区内已知气藏作为样本数据，在获取已知气藏中的不同成藏组合的氦气含量之后，确定已知气藏中的已知氦气含量的氦气藏各成藏组合的氦气储量；在采集数据和预测氦气藏储量时，以各成藏组合进行定量分析，预测结果更加准确，提高了氦气资源评价的准确性。然后，根据已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量，确定已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量。并通过汇总确定出目标研究区内已知气藏中氦气藏各成藏组合的氦气储量。进而根据已知氦气藏仿真构建目标研究区氦气藏的累积气藏模型，最终对累积气藏模型进行统计模拟，以估算出目标研究区内未知气藏中的氦气藏及其氦气储量，得到目标研究区内氦气资源的规模序列，本方法通过仿真构建累积气藏模型，进而预测研究区内未知氦气藏及其储量规模，有助于对目标研究区氦气资源潜力及分布进行分析，优选有利富集区，保障氦气资源开发和使用。进一步的， 该方法可作为氦气资源和氦气资产评估、含氦气田的长期开发规划及氦气全产业链一体化的可靠依据。
本申请的其它特征和优点将在随后的说明书中阐述，并且，部分地从说明书中变得显而易见，或者通过实施本申请而了解。本申请的目的和其他优点可通过在所写的说明书以及附图中所特别指出的结构来实现和获得。
下面通过附图和实施例，对本申请的技术方案做进一步的详细描述。
附图用来提供对本申请的进一步理解，并且构成说明书的一部分，与本申请的实施例一起用于解释本申请，并不构成对本申请的限制。在附图中：
图1为本申请实施例中提供的氦气资源规模序列的确定方法的流程示意图；
图2为步骤S11的具体流程示意图；
图3为步骤S12的具体流程示意图；
图4为步骤S14的具体流程示意图；
图5为步骤S143的具体流程示意图；
图6为步骤S15的具体流程示意图；
图7为本申请实施例中提供的研究区游离气氦气浓度直方图和累积分布图的示例；
图8为本申请实施例中提供的截断后的帕累托分布的示意图；
图9为本申请实施例中提供的氦气藏累积分布的示意图；
图10为本申请实施例中提供的氦气资源规模序列的确定装置的结构示意图。
下面将参照附图更详细地描述本公开的示例性实施例。虽然附图中显示了本公开的示例性实施例，然而应当理解，可以以各种形式实现本公开而不应被这里阐述的实施例所限制。相反，提供这些实施例是为了能够更透彻地理解本公开，并且能够将本公开的范围完整的传达给本领域的技术人员。
本申请实施例中提供了一种氦气资源规模序列的确定方法，参照图1所示，该方法可以包括以下步骤：
步骤S11、获取目标研究区内的已知气藏中不同成藏组合的氦气含量，以确定已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量。
本步骤中以目标研究区内的已知气藏作为样本数据，获取已知气藏中不同成藏组合的氦气含量。本申请实施例中的上述气藏可以是天然气藏、二氧化碳气藏、氮气藏等，本申请实施例对此并不作具体限定。
本实施例中，在获取样本数据时，可以先从已知气藏中进行采样以获取气样，再采用 氦气检测设备对气样进行氦气含量检测，比如可以采用质谱色谱仪进行氦气含量检测。为了提高氦气含量检测的准确性，可以多次测量后取平均值。
需要说明的是，在获取已知气藏中不同成藏组合的氦气含量时，即在采集数据时，尤其是针对同一气藏进行采样时，需要采集气藏内具有代表性的不同部位的气样，以减少系统误差，若引用其他数据来源，需要核实各个数据点的测试条件是否具有一致性。具体的采样要求可以是：同一气藏内所获得的氦气百分含量系统误差小于10％，气藏氦气百分含量采用所有采样点或数据点的加权平均值。
步骤S12、根据已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量，确定已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量。
本步骤是基于类比法，计算已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量。需要说明的是，本申请实施例中的已知气藏中的已知氦气含量的氦气藏可简称为已知含氦气藏或者第一氦气藏，已知气藏中未知氦气含量的氦气藏可简称为未知氦气含量的已知氦气藏或者第二氦气藏。
步骤S13、基于已知气藏中已知氦气含量的氦气藏各成藏组合的氦气储量，和已知气藏中未知氦气含量的氦气藏各成藏组合的氦气储量，确定目标研究区内的已知气藏中氦气藏各成藏组合的氦气储量。
本步骤中将已知气藏中不同计算方法确定的氦气藏的各成藏组合的氦气储量进行汇总，即汇总各成藏组合中上述第一氦气藏和第二氦气藏的氦气储量。
步骤S14、基于氦气藏各成藏组合的氦气储量，确定目标研究区内所有氦气藏的各成藏组合氦气储量的似然函数分布，以仿真构建目标研究区的累积气藏模型。本步骤是仿真出目标研究区的氦气藏的累积气藏模型，以备后续模拟评价。
步骤S15、基于累积气藏模型进行统计模拟，得到目标研究区内所有氦气藏及其氦气储量，以确定氦气资源的规模序列。
本步骤是对累积气藏模型进行统计模拟，以估算目标研究区内的未知气藏中的氦气藏及其氦气储量，最终确定出目标研究区的氦气资源的规模序列。
本申请实施例中首先以目标研究区内已知气藏作为样本数据，在获取已知气藏中的不同成藏组合的氦气含量之后，确定已知气藏中的已知氦气含量的氦气藏各成藏组合的氦气储量；在采集数据和预测氦气藏储量时，以各成藏组合进行定量分析，预测结果更加准确，提高了氦气资源评价的准确性。然后，根据已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量，确定已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量。并通过汇总确定出目标研究区内已知气藏中氦气藏各成藏组合的氦气储量。进而根据已知氦气藏仿真构建目标研究区氦气藏的累积气藏模型，最终对累积气藏模型进行统计模拟，以估算出目标研究区内未知气藏中的氦气藏及其氦气储量，得到目标研究区内氦气资源的规模序列，本方法通过仿真构建累积气藏模型，进而预测研究区内未知氦气藏及其储量规 模，有助于对目标研究区氦气资源潜力及分布进行分析，优选有利富集区，保障氦气资源开发和使用。
进一步的，通过统计已发现氦气藏的个数、规模，作为已发现天然气储量和待发现天然气资源中，开展氦气储量和资源量概率估计的基础。该方法可作为氦气资源和氦气资产评估、含氦气田的长期开发规划及氦气全产业链一体化的可靠依据。
本实施例中，还可以包括以下步骤：
根据确定的氦气资源的规模序列，确定是否对目标研究区的氦气藏进行氦气开发。
具体可以包括以下步骤：
将目标研究区内所有氦气藏的氦气储量分别与预设可开发储量进行比较，若氦气藏的氦气储量大于或等于预设可开发储量，在该氦气藏部署勘探井，以进行氦气开发；若氦气藏的氦气储量小于预设可开发储量，不对该氦气藏进行氦气开发。
在一个可选的实施例中，参照图2所示，上述步骤S11具体可以包括：
步骤S111、获取目标研究区内的已知气藏中不同成藏组合的氦气含量。
步骤S112、基于目标研究区内的已知气藏中不同成藏组合的氦气含量，以及已知气藏的天然气储量以百分含量法确定已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量。
本步骤中上述已知气藏中的已知氦气含量的氦气藏(第一氦气藏)各成藏组合的氦气储量可以通过下述公式I确定：
θ
He＝θ
gas×C
1 (I)
式(I)中，
θ
He表示目标气藏氦气储量，单位为m
3；
θ
gas表示目标气藏氦天然气储量，单位为m
3；
C
1表示目标气藏氦气百分含量度，单位为vol％。
在另一个可选的实施例中，参照图3所示，上述步骤S12具体可以通过下述步骤实现：
步骤S121、获取已知气藏中未知氦气含量的气藏各成藏组合相似的同一油气田和/或附近油气田的气藏数据；其中，同一油气田和/或附近油气田的气藏数据包括：气藏天然气储量和气藏氦气百分含量度。
步骤S122、确定已知气藏中未知氦气含量的气藏各成藏组合以及同一油气田和/或附近油气田的气藏的氦气藏相似系数。
步骤S123、基于同一油气田和/或附近油气田的气藏数据和氦气藏相似系数，进行类比以确定已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量。
本步骤中上述已知气藏中的未知氦气含量的氦气藏(第二氦气藏)各成藏组合的氦气储量可以通过下述公式II确定：
θ
He1＝θ
gas1×C×γ (II)
式(II)中，
θ
He1表示目标气藏氦气储量，单位为m
3；
θ
gas1表示目标气藏氦天然气储量，单位为m
3；
C表示类比气藏氦气百分含量度，单位为vol％；
γ表示两个含氦气藏相似系数，无量纲。
在一个具体的实施例中，在执行上述步骤S121之前，还需要对已知气藏中未知氦气含量的各成藏组合进行氦气藏成藏特征剖析，以确定与已知气藏中未知氦气含量的各成藏组合成藏特征相似的气藏。
需要说明的是，本步骤是针对未知氦气含量的含氦气藏，开展不同氦气藏成藏特征解剖，通过类比已知氦气储量的同一气田或附近气田的其他层系气藏来确定未知氦气含量的氦气藏的氦气储量。
上述步骤S13是对上述步骤S11中第一氦气藏和步骤S12第二氦气藏的成藏组合进行汇总，可以通过以下公式Ⅲ确定：
θ
He1＝N×θ
He2 (III)
式(III)中，
N表示成藏组合个数，单位为个；
θ
He1表示成藏组合汇总氦气储量，单位为m
3；
θ
He2表示单个成藏组合氦气储量，单位为m
3。
在另一个可选的实施例中，参照图4所示，上述步骤S14中仿真构建目标研究区的累积气藏模型具体可以包括以下步骤：
步骤S141、基于已知氦气藏及其各成藏组合的氦气储量，确定目标研究区内的所有氦气藏的各成藏组合氦气储量的似然函数分布。
本申请实施例中，将目标研究区内已知氦气藏取值为x
1，x
2，x
3，…，x
n，则发现氦气藏{X
1＝x
1，X
2＝x
2，…，X
n＝x
n}的概率参照公式Ⅳ所示如下：
式(IV)中，
n表示成藏组合个数，单位为个；
L表示似然概率，无量纲；
x
1，x
2，x
3，…，x
n，表示样本中的氦气藏；
X
1，X
2，…，X
n，表示离散型随机变量；
P表示概率，无量纲；
θ表示氦气藏氦气储量，单位为m
3。
从上述公式Ⅳ可知，概率随θ氦气藏氦气储量的变化而变化。
步骤S142、基于目标研究区内的所有氦气藏的各成藏组合氦气储量的似然函数分布，确定目标研究区内的氦气藏的最小成藏规模和最大成藏规模。
本步骤中求参数θ的极大似然值就是极大似然估计法就是在参数θ的可能取值范围内，选取使L(θ)达到最大的参数值，作为参数θ的估计值。极大似然函数参照公式Ⅴ如下：
通过求解方程dL(θ)/dθ＝0。
步骤S143、将氦气藏的最小成藏规模和最大成藏规模带入帕累托分布函数，仿真构建目标研究区所有氦气藏的累积气藏模型。
本申请实施例中的目标研究区氦气藏的分布可以通过截断后的帕累托分布函数(TPD)表示，截断后的帕累托分布，其气藏预测和经验非归一化累积分布函数呈现良好的对应关系。参照公式Ⅵ如下：
式(VI)中，
λ为分布参数，无量纲；
θ为氦气藏储量，单位为m
3；
θ
0、θ
max分别为自然聚集的氦气藏的最小和最大预期成藏规模。
在另一个具体的实施例中，在执行完上述步骤S142之后还可以包括：将帕累托分布函数中的氦气藏的分布参数、最大成藏规模以及成藏组合个数赋予最小二乘权重值，以提升累积气藏模型中大规模类型的氦气藏对累积气藏模型的影响权重。
本步骤针对λ，θmax和N的复杂化的问题可简化为加权方差之和的最小值，本申请实施例中引入权重函数Pi，如果Pi＝(i)-1或Pi＝qi，则大规模类别的氦气藏(数量较少)的数据对结果的影响更大，Pi＝1的情况对应于普通最小二乘，见公式(VII)：
在一个具体的实施例中，参照图5所示，上述步骤S143具体可以包括以下步骤：
步骤S1431、将氦气藏的最小成藏规模和最大成藏规模带入帕累托分布函数。
步骤S1432、预设预定数量的最大氦气藏聚集区，以作为氦气藏分布的分布参数。
根据氦气勘探发现规律，最大规模氦气藏将首先被发现。因此，对于勘探程度较高的 区带，可以假设已经发现了一定数量的最大氦气藏聚集区，连续规模的氦气藏分布的参数可以使用已发现最大气藏规模的子集m(m>3)，参数估值的稳定性可作为选择m值的一个充分准则。
所述非标准化累积分布函数由公式(VIII)计算得到。
式(VIII)中，
函数
为等于大于θ的累积量的数量，个；
N为系统中累积的总次数，个；
θ为氦气藏氦气储量，单位为m
3。
步骤S1433、对帕累托分布函数进行模拟，以识别目标研究区内大于预定数量的氦气藏及氦气储量。
需要说明的是，识别目标研究区内大于预定数量的氦气藏及氦气储量之后，还对识别出的氦气藏依据其氦气储量规模进行排序编号，以便于仿真构建累积气藏模型。
例如，在一个特定的氦气系统中识别出的所有氦气藏将从最大规模开始编号，假设在最大规模级别中至少识别出m个氦气藏，且θ
1≥θ
2≥...≥θ
m。
步骤S1434、基于确定的氦气藏即氦气储量仿真构建目标研究区所有氦气藏的累积气藏模型。
在引入函数
的情况下，为描述氦气藏自然集群相关特征，引入参数λ,θmax和N值，这样第i个氦气藏规模非标准化的值在某种程度上接近数量i，见公式(IX)：
Φ(θ
i)≈i，i＝1...m. (IX)
本步骤中在计算截断的帕累托分布(1)的参数后，采用仿真方法生成了一系列的累积气藏模型。对每种情况下进行模拟，直到得到储量大于自然聚类第m个气藏的储量θ(m)的m个气藏。对于一些储量小于θ(m)的气藏，可以根据氦气浓度经验分布计算氦气藏储量。
在另一个可选的实施例中，参照图6所示，上述步骤S15具体可以包括以下步骤：
步骤S151、基于累积气藏模型以蒙特卡罗模拟法进行统计模拟，得到目标研究区内所有氦气藏及其氦气储量；目标研究区内所有氦气藏及其氦气储量包括：最大成藏规模范围内的氦气藏和氦气储量，以及不同概率条件下的未知氦气藏和氦气储量。
本步骤分别统计在不同最小气藏规模条件下的预计发现气藏个数。例如通过蒙特卡洛模拟5000次，生成最大游离气储量规模范围内的游离气和氦气地质资源分布，以及不同概率条件下的待发现油气藏中游离气和氦气全部原地远景资源量，明确不同级别的氦气储 量和资源量。
步骤S152、基于最大成藏规模范围内的氦气藏和氦气储量，不同概率条件下的未知氦气藏和氦气资源量，以及预设的概率值，得到目标研究区内氦气资源的规模序列。
本申请实施例可以用来计算已知氦气含量的含氦天然气储量、未知氦气含量的已知气藏储量，也可以计算待发现气藏中的含氦天然气地质资源量分布。小于最小规模储量的氦气藏，将在帕累托分布的左侧被截断。
本方法中使用的经验数据可以代表具有不同分布特征的各种氦气藏，以及这些氦气藏研究认识程度，如不同的地层、岩性、构造及复合油气藏等。储量及资源量估算的偏差程度与该油田勘探成熟度有关。
在一个具体的示例中，以某目标研究区为例进行说明如下：
通过采集已知气藏中不同成藏组合(4个成藏组合)氦气含量和收集氦气数据。研究区已发现78个油气田，其中68个油田332个气藏具有储量数据，其中32个油田245个气藏有氦气含量数据，13个气田87个气藏中已知含氦，但无氦气含量数据。采用百分含量法计算已知氦气含量的含氦气藏中的氦气储量，根据公式(I)得到4个成藏组合已知氦气储量汇总表1(表1)，研究区已知氦气原始地质储量为199.5亿方。针对未知氦气含量的含氦气藏，通过开展13个气田游离气气藏与已知氦气藏的类比研究，确定87个气藏的氦气含量，根据公式(II)得到目标氦气藏储量(表1)，研究区通过类比法求出的氦气原始地质储量为57.1亿方。
表1目标研究区不同成藏组合已知氦气储量汇总表
统计各成藏组合的已知氦气储量和类比氦气储量，汇总不同成藏组合氦气储量，根据 公式(III)得到加权氦气含量(表1)。研究区全部氦气储量为256.6亿方，加权氦气含量为0.309％。
作为本申请一个优选的实施方式，S105利用似然函数近似分布后，根据公式(IV、V)求得最大似然数为1亿方。
作为本申请一个优选的实施方式，S105在计算截断的帕累托分布(1)的参数后，根据公式(VI、VII、VIII)，采用模拟仿真方法生成了一系列的模拟累积气藏。若最小氦气藏规模θ
0为3百万方(已知小于该规模气藏个数为15个)，预计发现气藏个数N为12250个(已发现气藏个数为336个)；若最小气藏规模θ
0为3千万方(已知小于该规模气藏个数为317个)，预计发现气藏个数N为1320个，形成图7所示的研究区游离气氦气浓度直方图和累积分布图。
根据蒙特卡洛法，通过已知气藏中氦气储量来推导氦气原始地质储量分布。研究区内氦气储量大于3亿方的最大气藏90个，截断后的帕累托分布参数估计值为I为1.98，θmax为120亿方。在图8中显示，对于储量小于3亿方的气藏，气藏预测和经验非归一化累积分布函数呈现良好的对应关系。
通过蒙特卡洛模拟5000次运行，生成图9所示的研究区游离气原地储量小于3亿方的氦资源频率和近似累积分布，该储量规模对应的已发现气藏中氦气总储量为3亿方。待发现氦气藏全部原地远景资源量的概率估计值为0.9，最小值为123亿方，最大值为170亿方(表2)。取中位数P50，则研究区4个成藏组合的氦气远景资源量为146亿方，全部氦气原地储量和远景资源量合计为402亿方(表3)。
表2研究区游离天然气和氦气原地资源量
| 概率分布 | 游离天然气资源量Tcm | 氦气资源量Bcm |
| P50 | 4.8 | 14.6 |
| P10 | 5.7 | 17.0 |
| P90 | 4.1 | 12.3 |
| P5 | 5.9 | 17.9 |
| P95 | 3.9 | 11.7 |
| P2.5 | 6.1 | 18.5 |
| P97.5 | 3.7 | 11.2 |
表3研究区不同成藏组合氦气储量和资源量一览表
需要说明的是，本申请实施例中所用的实例数据非常接近于截断的帕累托分布，但从理论上分析，氦气藏实际分布形态可能多种多样，可能在特定规模的氦气藏分布形态不一定为帕累托。在经典的油气成藏规模分布的理论模型中，规模相对较小的油气藏规模可能出现与帕累托分布的偏差。
基于同一发明构思，本申请实施例中还提供了一种氦气资源的确定装置，参照图10所示，该装置可以包括：数据获取模块101、第一确定模块102、第二确定模块103、第三确认模块104、仿真构建模块105以及统计模拟模块106，其工作原理如下：
数据获取模块101用于获取目标研究区内的已知气藏中不同成藏组合的氦气含量；
第一确定模块102用于基于已知气藏中不同成藏组合的氦气含量，确定已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量；
第二确定模块103用于根据已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量，确定已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量；
第三确定模块104用于基于已知气藏中已知氦气含量的氦气藏各成藏组合的氦气储量，和已知气藏中未知氦气含量的氦气藏各成藏组合的氦气储量，确定目标研究区内的已知气藏中氦气藏各成藏组合的氦气储量；
仿真构建模块105用于基于氦气藏各成藏组合的氦气储量，确定目标研究区内所有氦气藏的各成藏组合氦气储量的似然函数分布，以仿真构建目标研究区内所有氦气藏的累积气藏模型；
统计模拟模块106用于基于累积气藏模型进行统计模拟，得到目标研究区内所有氦气藏及其氦气储量，以确定氦气资源的规模序列。
在一个可选的实施例中，上述第一确定模块102具体用于基于所述目标研究区内的已知气藏中不同成藏组合的氦气含量，以及已知气藏的天然气储量以百分含量法确定已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量。
在另一个可选的实施例中，数据获取模块101还用于获取所述已知气藏中未知氦气含量的气藏各成藏组合相似的同一油气田和/或附近油气田的气藏数据；其中，同一油气田和/或附近油气田的气藏数据包括：气藏天然气储量和气藏氦气百分含量度；
第二确定模块103用于确定所述已知气藏中未知氦气含量的气藏各成藏组合以及同一油气田和/或附近油气田的气藏的氦气藏相似系数；并基于所述同一油气田和/或附近油气 田的气藏数据和所述氦气藏相似系数，进行类比以确定所述已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量。
具体的，数据获取模块101获取相似的同一油气田和/或附近油气田的气藏数据之前，还对所述已知气藏中未知氦气含量的各成藏组合进行氦气藏成藏特征剖析，以确定与所述已知气藏中未知氦气含量的各成藏组合成藏特征相似的气藏。
在另一个可选的实施例中，仿真构建模块105具体用于：基于所述已知氦气藏及其各成藏组合的氦气储量，确定所述目标研究区内的所有氦气藏的各成藏组合氦气储量的似然函数分布；
基于所述目标研究区内的所有氦气藏的各成藏组合氦气储量的似然函数分布，确定所述目标研究区内的氦气藏的最小成藏规模和最大成藏规模；
将所述氦气藏的最小成藏规模和最大成藏规模带入帕累托分布函数，仿真构建所述目标研究区所有氦气藏的累积气藏模型。
在另一个可选的实施例中，上述仿真构建模块105还用于：将所述帕累托分布函数中的氦气藏的分布参数、最大成藏规模以及成藏组合个数赋予最小二乘权重值，以提升所述累积气藏模型中大规模类型的氦气藏对所述累积气藏模型的影响权重。
在另一个具体的实施例中，仿真构建模块105还用于：将所述氦气藏的最小成藏规模和最大成藏规模带入帕累托分布函数；
预设预定数量的最大氦气藏聚集区，以作为所述氦气藏分布的分布参数；
对所述帕累托分布函数进行模拟，以识别所述目标研究区内大于所述预定数量的氦气藏及氦气储量；
基于确定的氦气藏即氦气储量仿真构建所述目标研究区所有氦气藏的累积气藏模型。
在另一个具体的实施例中，仿真构建模块105识别所述目标研究区内大于所述预定数量的氦气藏及氦气储量之后，还用于：对识别出的所述氦气藏依据其氦气储量规模进行排序编号，以便于仿真构建所述累积气藏模型。
在另一个可选的实施例中，统计模拟模块106具体用于基于所述累积气藏模型以蒙特卡罗模拟法进行统计模拟，得到所述目标研究区内所有氦气藏及其氦气储量；所述目标研究区内所有氦气藏及其氦气储量包括：最大成藏规模范围内的氦气藏和氦气储量，以及不同概率条件下的未知氦气藏和氦气资源量；
基于最大成藏规模范围内的氦气藏和氦气储量，不同概率条件下的未知氦气藏和氦气资源量，以及预设的概率值，得到所述目标研究区内氦气资源的规模序列。
基于同一发明构思，本申请实施例中还提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现上述氦气资源规模序列的确定方法。
基于同一发明构思，本申请实施例中还提供了一种计算机设备，包括存储器，处理器及存储在存储器上并可在处理器上运行的计算机程序，处理器执行程序时实现上述氦气资 源规模序列的确定方法。
本申请实施例中的上述装置、介质和相关设备所解决问题的原理与前述方法相似，因此其实施可以参见前述方法的实施，重复之处不再赘述。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器和光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
显然，本领域的技术人员可以对本申请进行各种改动和变型而不脱离本申请的精神和范围。这样，倘若本申请的这些修改和变型属于本申请权利要求及其等同技术的范围之内，则本申请也意图包含这些改动和变型在内。
Claims (12)
- 一种氦气资源规模序列的确定方法，其中，包括：获取目标研究区内的已知气藏中不同成藏组合的氦气含量，以确定已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量；根据所述已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量，确定所述已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量；基于所述已知气藏中已知氦气含量的氦气藏各成藏组合的氦气储量，和所述已知气藏中未知氦气含量的氦气藏各成藏组合的氦气储量，确定所述目标研究区内的已知气藏中氦气藏各成藏组合的氦气储量；基于所述氦气藏各成藏组合的氦气储量，确定所述目标研究区内所有氦气藏的各成藏组合氦气储量的似然函数分布，以仿真构建所述目标研究区的累积气藏模型；基于所述累积气藏模型进行统计模拟，得到所述目标研究区内所有氦气藏及其氦气储量，以确定所述氦气资源的规模序列。
- 根据权利要求1所述的方法，其中，所述获取目标研究区内的已知气藏中不同成藏组合的氦气含量，以确定已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量，包括：获取目标研究区内的已知气藏中不同成藏组合的氦气含量；基于所述目标研究区内的已知气藏中不同成藏组合的氦气含量，以及已知气藏的天然气储量以百分含量法确定已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量。
- 根据权利要求1所述的方法，其中，所述根据所述已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量，确定所述已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量，包括：获取所述已知气藏中未知氦气含量的气藏各成藏组合相似的同一油气田和/或附近油气田的气藏数据；其中，同一油气田和/或附近油气田的气藏数据包括：气藏天然气储量和气藏氦气百分含量度；确定所述已知气藏中未知氦气含量的气藏各成藏组合以及同一油气田和/或附近油气田的气藏的氦气藏相似系数；基于所述同一油气田和/或附近油气田的气藏数据和所述氦气藏相似系数，进行类比以确定所述已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量。
- 根据权利要求3所述的方法，其中，所述获取所述已知气藏中未知氦气含量的各成藏组合同一油气田和/或附近油气田的气藏数据之前，还包括：对所述已知气藏中未知氦气含量的各成藏组合进行氦气藏成藏特征剖析，以确定与所 述已知气藏中未知氦气含量的各成藏组合成藏特征相似的气藏。
- 根据权利要求1所述的方法，其中，所述基于所述氦气藏各成藏组合的氦气储量，确定所述目标研究区内所有氦气藏的各成藏组合氦气储量的似然函数分布，以仿真构建所述目标研究区的累积气藏模型，包括：基于所述已知氦气藏及其各成藏组合的氦气储量，确定所述目标研究区内的所有氦气藏的各成藏组合氦气储量的似然函数分布；基于所述目标研究区内的所有氦气藏的各成藏组合氦气储量的似然函数分布，确定所述目标研究区内的氦气藏的最小成藏规模和最大成藏规模；将所述氦气藏的最小成藏规模和最大成藏规模带入帕累托分布函数，仿真构建所述目标研究区的累积气藏模型。
- 根据权利要求5所述的方法，其中，所述将所述氦气藏的最小成藏规模和最大成藏规模带入帕累托分布函数，仿真构建所述目标研究区的累积气藏模型之后，还包括：将所述帕累托分布函数中的氦气藏的分布参数、最大成藏规模以及成藏组合个数赋予最小二乘权重值，以提升所述累积气藏模型中大规模类型的氦气藏对所述累积气藏模型的影响权重。
- 根据权利要求6所述的方法，其中，所述将所述氦气藏的最小成藏规模和最大成藏规模带入帕累托分布函数，仿真构建所述目标研究区的累积气藏模型，包括：将所述氦气藏的最小成藏规模和最大成藏规模带入帕累托分布函数；预设预定数量的最大氦气藏聚集区，以作为所述氦气藏分布的分布参数；对所述帕累托分布函数进行模拟，以识别所述目标研究区内大于所述预定数量的氦气藏及氦气储量；基于确定的氦气藏即氦气储量仿真构建所述目标研究区的累积气藏模型。
- 根据权利要求7所述的方法，其中，所述识别所述目标研究区内大于所述预定数量的氦气藏及氦气储量之后，还包括：对识别出的所述氦气藏依据其氦气储量规模进行排序编号，以便于仿真构建所述累积气藏模型。
- 根据权利要求1～8中任一项所述的方法，其中，所述基于所述累积气藏模型进行统计模拟，得到所述目标研究区内所有氦气藏及其氦气储量，以确定所述氦气资源的规模序列，包括：基于所述累积气藏模型以蒙特卡罗模拟法进行统计模拟，得到所述目标研究区内所有氦气藏及其氦气储量；所述目标研究区内所有氦气藏及其氦气储量包括：最大成藏规模范围内的氦气藏和氦气储量，以及不同概率条件下的未知氦气藏和氦气资源量；基于最大成藏规模范围内的氦气藏和氦气储量，不同概率条件下的未知氦气藏和氦气资源量，以及预设的概率值，得到所述目标研究区内氦气资源的规模序列。
- 一种氦气资源规模序列的确定装置，其中，包括：数据获取模块，用于获取目标研究区内的已知气藏中不同成藏组合的氦气含量；第一确定模块，用于基于所述已知气藏中不同成藏组合的氦气含量，确定已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量；第二确定模块，用于根据所述已知气藏中的已知氦气含量的氦气藏及其各成藏组合的氦气储量，确定所述已知气藏中未知氦气含量的氦气藏及其各成藏组合的氦气储量；第三确定模块，用于基于所述已知气藏中已知氦气含量的氦气藏各成藏组合的氦气储量，和所述已知气藏中未知氦气含量的氦气藏各成藏组合的氦气储量，确定所述目标研究区内的已知气藏中氦气藏各成藏组合的氦气储量；仿真构建模块，用于基于所述氦气藏各成藏组合的氦气储量，确定所述目标研究区内所有氦气藏的各成藏组合氦气储量的似然函数分布，以仿真构建所述目标研究区的累积气藏模型；统计模拟模块，用于基于所述累积气藏模型进行统计模拟，得到所述目标研究区内所有氦气藏及其氦气储量，以确定所述氦气资源的规模序列。
- 一种计算机可读存储介质，其上存储有计算机程序，其中，该程序被处理器执行时实现如权利要求1～9中任一项所述的氦气资源规模序列的确定方法。
- 一种计算机设备，包括存储器，处理器及存储在存储器上并可在处理器上运行的计算机程序，其中，所述处理器执行所述程序时实现如权利要求1～9中任一项所述的氦气资源规模序列的确定方法。
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210551884.4 | 2022-05-18 | ||
| CN202210551884.4A CN115221675B (zh) | 2022-05-18 | 2022-05-18 | 一种氦气资源规模序列的确定方法、装置和设备 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2023221491A1 true WO2023221491A1 (zh) | 2023-11-23 |
Family
ID=83608093
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/CN2022/139580 WO2023221491A1 (zh) | 2022-05-18 | 2022-12-16 | 一种氦气资源规模序列的确定方法、装置和设备 |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN115221675B (zh) |
| WO (1) | WO2023221491A1 (zh) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115221675B (zh) * | 2022-05-18 | 2024-03-01 | 中国石油天然气集团有限公司 | 一种氦气资源规模序列的确定方法、装置和设备 |
| CN116071191B (zh) * | 2023-04-03 | 2023-06-23 | 北京大学 | 稳定克拉通盆地富氦气田中氦源岩贡献份额定量表征方法 |
| CN117150207B (zh) * | 2023-08-02 | 2024-01-26 | 中国地质大学（北京） | 伴生氦气资源量的评价方法、设备及存储介质 |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130262069A1 (en) * | 2012-03-29 | 2013-10-03 | Platte River Associates, Inc. | Targeted site selection within shale gas basins |
| CN110490436A (zh) * | 2019-07-31 | 2019-11-22 | 中国地质科学院地质力学研究所 | 一种油气资源量评价方法及评价系统 |
| CN115221675A (zh) * | 2022-05-18 | 2022-10-21 | 中国石油天然气集团有限公司 | 一种氦气资源规模序列的确定方法、装置和设备 |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103246808A (zh) * | 2013-04-27 | 2013-08-14 | 中国石油天然气股份有限公司 | 一种低渗砂岩含水气藏储层分类方法 |
| CN104166796A (zh) * | 2014-08-13 | 2014-11-26 | 中国石油大学(北京) | 确定成藏体系内最大单一油气藏规模的方法及装置 |
| CN106295835A (zh) * | 2015-05-21 | 2017-01-04 | 中国石油化工股份有限公司 | 油气藏规模分布的预测方法 |
| CN111767624A (zh) * | 2019-03-15 | 2020-10-13 | 中国石油化工股份有限公司 | 一种基于地质帕累托参数先验求解的资源评价方法 |
| CN110334431B (zh) * | 2019-07-02 | 2022-08-12 | 西南石油大学 | 一种低渗透致密气藏单井控制储量计算及剩余气分析方法 |
| CN111338001B (zh) * | 2020-03-17 | 2021-06-01 | 中国石油化工股份有限公司 | 一种氦气气藏的勘探方法 |
| CN111308577B (zh) * | 2020-03-17 | 2021-06-01 | 中国石油化工股份有限公司 | 一种针对氦气气藏定量勘探中地区参数的确定方法 |
| CN114252498B (zh) * | 2020-09-24 | 2023-12-26 | 中国石油天然气股份有限公司 | 一种天然气中氦气含量的综合检测方法 |
-
2022
- 2022-05-18 CN CN202210551884.4A patent/CN115221675B/zh active Active
- 2022-12-16 WO PCT/CN2022/139580 patent/WO2023221491A1/zh unknown
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130262069A1 (en) * | 2012-03-29 | 2013-10-03 | Platte River Associates, Inc. | Targeted site selection within shale gas basins |
| CN110490436A (zh) * | 2019-07-31 | 2019-11-22 | 中国地质科学院地质力学研究所 | 一种油气资源量评价方法及评价系统 |
| CN115221675A (zh) * | 2022-05-18 | 2022-10-21 | 中国石油天然气集团有限公司 | 一种氦气资源规模序列的确定方法、装置和设备 |
Non-Patent Citations (1)
| Title |
|---|
| SHIQI NI, WANG ZHIXIN; SONG JIYE; PANG LIN'AN: "Analysis and resource evaluation of hydrocarbon plays in the west Natuna Basin,southwestern part of the South China Sea", MARINE GEOLOGY & QUATERNARY GEOLOGY, vol. 38, no. 5, 28 October 2018 (2018-10-28), pages 130 - 143, XP093108187 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115221675B (zh) | 2024-03-01 |
| CN115221675A (zh) | 2022-10-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2023221491A1 (zh) | 一种氦气资源规模序列的确定方法、装置和设备 | |
| CN110717249B (zh) | 页岩气储层测井孔隙度快速预测方法及系统 | |
| AU2012253529B2 (en) | System and method for characterizing reservoir formation evaluation uncertainty | |
| CA2892058C (en) | Energy deposit discovery system and method | |
| CN107728231A (zh) | 一种预测核磁共振测井横向弛豫时间t2分布的方法 | |
| CN105158796A (zh) | 确定toc含量的方法和装置 | |
| CN116427915A (zh) | 基于随机森林的常规测井曲线裂缝密度预测方法及系统 | |
| CN114529110A (zh) | 一种基于深度神经网络模型的岩相反演方法及系统 | |
| Torrado et al. | Optimal sequential drilling for hydrocarbon field development planning | |
| CN114114414A (zh) | 一种页岩储层“甜点”信息人工智能预测方法 | |
| CN108828680B (zh) | 地层电阻率和极化率的确定方法和装置 | |
| RU2591239C1 (ru) | Системы и способы для оценивания возможности коллекторной системы | |
| Sircar et al. | Genetic-based Monte Carlo Modeling of geothermal prospects: Indian examples | |
| US20240133293A1 (en) | Concentration Prediction in Produced Water | |
| Shao et al. | Carbonate log interpretation models based on machine learning techniques | |
| Hood et al. | Use of geographic information systems in hydrocarbon resource assessment and opportunity analysis | |
| Hatampour et al. | Prediction of flow units in heterogeneous carbonate reservoirs using intelligently derived formula: Case study in an Iranian reservoir | |
| CN111967677A (zh) | 一种非常规资源甜点分布的预测方法及装置 | |
| Ashayeri et al. | Using the Adaptive Variable Structure Regression Approach in Data Selection and Data Preparation for Improving Machine Learning-Based Performance Prediction in Unconventional Plays | |
| CN108132486A (zh) | 一种重磁梯度与地震数据联合界面反演的优化模拟退火法 | |
| CN115619788B (zh) | 一种三维地质模型质量的自动化定量评价方法 | |
| CN116523183B (zh) | 一种废弃矿山高陡边坡安全与生态修复综合评价方法 | |
| CN115470664B (zh) | 一种裂缝油藏裂缝敏感性分析方法 | |
| US20220228483A1 (en) | Systems and methods for updating reservoir static models | |
| Eaton et al. | Estimating confidence intervals for gravel bed surface grain size distributions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 22942511 Country of ref document: EP Kind code of ref document: A1 |