Method and system for determining gas-water distribution of tight sandstone gas reservoir

Abstract

The invention provides a method and system for determining gas-water distribution of a tight sandstone gas reservoir, and the method comprises the steps: building a multi-factor hierarchical control mode of the gas-water distribution of the tight sandstone gas reservoir, and embedding control factors step by step in the modeling process of bound water and movable water based on the multi-factor hierarchical control mode of the gas-water distribution, and a total water saturation model is established by taking a bound water and movable water merging model as spatial constraint, so that quantitative characterization of gas-water distribution under multi-factor control is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field exploration and development. Specifically, it relates to a method and system for determining the gas-water distribution in a tight sandstone gas reservoir. Background Art

[0002] The western area of the Sulige Gas Field is a typical tight sandstone gas reservoir rich in water. The gas-water distribution in such gas reservoirs is jointly controlled by multiple factors such as lithology, physical properties, structure, hydrocarbon source, and faults, resulting in a complex gas-water distribution law. The gas-water distribution is the combined result of multiple factors superimposed control. Whether using single-factor decomposition or multi-factor joint analysis, it is very difficult to quantitatively characterize the spatial distribution law of gas and water. Summary of the Invention

[0003] The purpose of the present invention is to solve at least one of the above-mentioned deficiencies existing in the prior art. For example, one of the purposes of the present invention is to provide a technology capable of quantitatively characterizing the gas-water distribution controlled by multiple factors.

[0004] To achieve the above purpose, one aspect of the present invention provides a method for determining the gas-water distribution in a tight sandstone gas reservoir. The method includes: establishing a multi-factor hierarchical control model for the gas-water distribution in a tight sandstone gas reservoir, and based on the multi-factor hierarchical control model of the gas-water distribution, stepwise embed control factors during the modeling process of irreducible water and mobile water, and establish a total water saturation model with the combined model of irreducible water and mobile water as the spatial constraint, so as to realize the quantitative characterization of the gas-water distribution controlled by multiple factors.

[0005] Optionally, establishing a multi-factor hierarchical control model for the gas-water distribution in a tight sandstone gas reservoir may include: analyzing the control effects of different control factors on the gas-water distribution, clarifying the control effects and characteristics of different control factors on the gas-water distribution; and classifying different control factors affecting the gas-water distribution by level, and establishing a multi-factor hierarchical control model for the gas-water distribution in a tight sandstone gas reservoir; wherein, the control factors include source rocks, transport systems, tectonic controls, and reservoirs.

[0006] Optionally, establishing the control methods of different control factors on the gas-water distribution according to the control characteristics of different control factors may include: the lithology and physical properties of the reservoir control the occurrence and saturation of irreducible water, and the control method is facies control; the source rock controls the gas-bearing differences in different horizons, and the control method is horizon control; the transport system controls the regional distribution of mobile water, and the control method is regional control; the structure controls the local gas-water differentiation, and the control method is local point control.

[0007] Optionally, the bound water modeling process may include: establishing a reservoir classification lithofacies model; establishing a porosity parameter model under the facies control of the established reservoir classification lithofacies model based on the coarsened data of logging curves; establishing a functional relationship between porosity and bound water saturation according to core phase permeability tests and nuclear magnetic resonance logging data to interpret the bound water saturation; interpreting the water saturation of a single well through the Archie formula based on rock-electricity experiments; calculating the movable water saturation using the difference between the water saturation and the bound water saturation; and establishing a bound water saturation model based on the functional relationship between the bound water saturation and porosity on the basis of the porosity parameter model.

[0008] Optionally, the sequential Gaussian simulation algorithm may be used to perform porosity parameter interpolation to establish the porosity parameter model.

[0009] Optionally, the movable water modeling process may include: guided by the hierarchical control mode of gas-water distribution, based on the coarsened data of well point movable water saturation, under multi-level facies control, through data analysis and collaborative constraint means, reflecting the control effects of different control factors such as source rocks, transport systems, and tectonic controls step by step in the model during the modeling process to establish a movable water saturation model.

[0010] Optionally, data analysis is carried out under the regional control of the transport system. Through zonal data analysis and statistics, the regional control of the transport system on the movable water distribution is realized; data analysis of the movable water saturation is carried out under the facies control of different types of reservoirs in different regions to realize the phase control of reservoir lithology and physical properties on the movable water distribution; during the zonal and phase-based movable water modeling process, the micro-structure after removing the tectonic trend is used as a collaborative constraint condition to realize the local control of the micro-structure on the movable water saturation distribution; and under the superposition control and constraint of multiple factors, a movable water saturation model is established, and the multiple factors include lithology and physical properties, source rocks, transport systems, and micro-structures.

[0011] Optionally, establishing the total water saturation model may include: adding the bound water saturation model and the movable water saturation model to obtain a total water saturation constraint model; based on the total water saturation interpreted by logging, using the added total water saturation constraint model as a spatial constraint, and adopting the sequential Gaussian simulation algorithm to establish the total water saturation model.

[0012] Optionally, the steps of establishing a reservoir classification lithofacies model include: formulating a reservoir hierarchical configuration division plan based on outcrop, satellite image measurement, core and well log facies identification, constructing a geological knowledge base of configuration parameters, and dividing the hierarchical configuration units of a single well; establishing a reservoir classification standard based on configuration control and dividing the reservoir types of a single well; using the multi-point geostatistical simulation algorithm as the core, jointly applying deterministic simulation, sequential indicator simulation, object-based simulation and sequential Gaussian simulation algorithms, and hierarchically establishing a reservoir classification lithofacies model that simultaneously includes multi-level configuration units through hierarchical facies control and collaborative constraints.

[0013] Optionally, hierarchically establishing a reservoir classification lithofacies model that simultaneously includes multi-level configuration units through hierarchical facies control and collaborative constraints includes: establishing a fifth-level configuration model of channel sandstone and overbank deposits; under the facies control of the channel sandstone model, based on the reservoir classification data of a single well, using the pattern model as the training image and the probability volume model as the distribution constraint, establishing an I+II reservoir model representing the high-energy channel configuration and a class III reservoir model representing the low-energy channel by using the multi-point geostatistical method; and determining the geometric shape and parameters of the point bar and lateral bar configurations according to the established geological knowledge base of the fluvial facies reservoir configuration, using the I+II reservoir model as the facies control, and establishing a class I reservoir model representing the point bar configuration and a class II reservoir model representing the braided channel by using the object-based simulation method based on the coarsened reservoir classification data of a single well.

[0014] On the other hand, the present invention provides a system for determining gas-water distribution in a tight sandstone gas reservoir. The determination system includes: a reservoir classification lithofacies module configured to establish a reservoir classification lithofacies model; a porosity parameter module configured to establish a porosity parameter model based on well logging curve coarsening data under the facies control of the established reservoir classification lithofacies model; an irreducible water saturation logging interpretation module configured to establish a functional relationship between porosity and irreducible water saturation according to core phase permeability test and nuclear magnetic resonance logging data, interpret the irreducible water saturation, interpret the single-well water saturation through Archie's formula based on rock-electricity experiment, and calculate the mobile water saturation using the difference between the water saturation and the irreducible water saturation; an irreducible water saturation module configured to establish an irreducible water saturation model based on the functional relationship between the irreducible water saturation and porosity on the basis of the porosity parameter model; a mobile water saturation module configured to be guided by a hierarchical control mode of gas-water distribution, based on well point mobile water saturation data, and through data analysis and collaborative constraint means under multi-level facies control, reflect the control effects of different control factors such as hydrocarbon source rock, transport system, and tectonic control step by step in the modeling process, and establish a mobile water saturation model; and a total water saturation module configured to add the irreducible water saturation model and the mobile water saturation model to obtain a total water saturation constraint model, and based on the total water saturation interpreted by well logging and using the total water saturation constraint model obtained by addition as a spatial constraint, establish a total water saturation model using the sequential Gaussian simulation algorithm.

[0015] On yet another aspect, the present invention provides a computer device, characterized in that the computer device includes: a processor; and a memory storing a computer program, and when the computer program is executed by the processor, the method for determining gas-water distribution in a tight sandstone gas reservoir as described above is implemented.

[0016] On still another aspect, the present invention provides a computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the method for determining gas-water distribution in a tight sandstone gas reservoir as described above is implemented.

[0017] Compared with the prior art, the beneficial effects of the present invention include: adopting the modeling idea of splitting and modeling irreducible water and mobile water and combining constraints, and through data analysis and collaborative constraint control means in the multi-level facies control modeling process, reflecting the control effects of various factors such as hydrocarbon source conditions, tectonic conditions, reservoir conditions, and transport conditions on gas-water distribution step by step in the modeling, and realizing the quantitative characterization of gas-water distribution under multi-factor control. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Through the following description with reference to the accompanying drawings, the above and other objects and / or features of the present invention will become more apparent, wherein:

[0019] Figure 1 Shows the multi - factor hierarchical control mode diagram of gas - water distribution for the method of determining gas - water distribution in a tight sandstone gas reservoir according to an exemplary embodiment of the present invention.

[0020] Figure 2A Shows the flow chart of multi - factor controlled gas - water distribution modeling for the method of determining gas - water distribution in a tight sandstone gas reservoir according to an exemplary embodiment of the present invention.

[0021] Figure 2B Shows the flow chart of establishing a reservoir classification lithofacies model for the method of determining gas - water distribution in a tight sandstone gas reservoir according to an exemplary embodiment of the present invention.

[0022] Figure 3 Shows the division scheme of fluvial facies reservoir architecture for the method of determining gas - water distribution in a tight sandstone gas reservoir according to an exemplary embodiment of the present invention.

[0023] Figure 4A Shows the sliced lithofacies model of channel - belt sandstone and flood - plain mudstone constructed by the method of determining gas - water distribution in a tight sandstone gas reservoir according to an exemplary embodiment of the present invention.

[0024] Figure 4B Is Figure 4A A black - and - white schematic diagram of

[0025] Figure 5A Shows the sliced lithofacies model of Class I + II reservoirs and Class III reservoirs constructed by the method of determining gas - water distribution in a tight sandstone gas reservoir according to an exemplary embodiment of the present invention.

[0026] Figure 5B Is Figure 5A A black - and - white schematic diagram of

[0027] Figure 6A Shows the sliced lithofacies model of reservoir classification constructed by the method of determining gas - water distribution in a tight sandstone gas reservoir according to an exemplary embodiment of the present invention.

[0028] Figure 6B Is Figure 6A A black - and - white schematic diagram of

[0029] Figure 7A Shows the grid diagram of reservoir porosity model constructed by the method of determining gas - water distribution in a tight sandstone gas reservoir according to an exemplary embodiment of the present invention.

[0030] Figure 7B Is Figure 7A A black - and - white schematic diagram of

[0031] Figure 8A Shows the grid diagram of reservoir irreducible water saturation model constructed by the method of determining gas - water distribution in a tight sandstone gas reservoir according to an exemplary embodiment of the present invention.

[0032] Figure 8B is Figure 8A a black-and-white schematic diagram of

[0033] Figure 9A showing a cross-sectional view of the reservoir movable water saturation model constructed by the method for determining the gas-water distribution in a tight sandstone gas reservoir according to an exemplary embodiment of the present invention.

[0034] Figure 9B is Figure 9A a black-and-white schematic diagram of

[0035] Figure 10A showing a cross-sectional view of the reservoir water saturation model constructed by the method for determining the gas-water distribution in a tight sandstone gas reservoir according to an exemplary embodiment of the present invention.

[0036] Figure 10B is Figure 10A a black-and-white schematic diagram of

[0037] Figure 11 showing a schematic diagram of the system for determining the gas-water distribution in a tight sandstone gas reservoir according to an exemplary embodiment of the present invention.

[0038] Description of reference numerals:

[0039] 10 - reservoir classification lithofacies module, 20 - porosity parameter module, 30 - irreducible water saturation log interpretation module, 40 - irreducible water saturation module, 50 - movable water saturation module, and 60 - total water saturation module. Detailed implementation manners

[0040] In the following, the method and system for determining the gas-water distribution in a tight sandstone gas reservoir of the present invention will be described in detail in conjunction with exemplary embodiments.

[0041] The applicant has found that: relevant research mainly focuses on the qualitative analysis of the influence of different main control factors on gas-water distribution, and there is less research on the quantitative description of the gas-water spatial distribution under the control of multiple factors, which is not conducive to directly guiding the screening of enrichment areas and well location deployment in tight sandstone gas-rich reservoirs.

[0042] Regarding the problem of gas-water distribution law under the control of complex multiple factors in tight sandstone gas-rich reservoirs, the research mainly focuses on the qualitative analysis of main control factors. For example, a gas-water distribution pattern is established based on the analysis of main control factors; in terms of gas-water distribution modeling, the related technology mainly uses the sequential Gaussian simulation method to perform spatial interpolation on the well point water saturation, and some use the phase control or co-constraint method to control the interpolation between wells. However, this modeling method cannot achieve simultaneous constraints of multiple constraint conditions, and thus cannot establish a gas-water distribution model under the control of multiple factors.

[0043] Aiming at the problems of unclear understanding of the gas-water distribution law in tight sandstone rich gas reservoirs controlled by multiple factors and great difficulty in quantitative characterization, the present invention provides a method for quantitatively characterizing gas-water distribution based on three-water stepwise modeling. Here, "three waters" refers to irreducible water, mobile water, and total water. That is, the present invention is a method for quantitatively characterizing gas-water distribution based on stepwise modeling of irreducible water saturation, mobile water saturation, and total water saturation. Guided by the multi-factor hierarchical control mode of gas-water distribution, control factors are embedded step by step during the process of separately modeling irreducible water and mobile water, and a total water saturation model is established with the combined model of irreducible water and mobile water as the spatial constraint, so as to realize the quantitative characterization of gas-water distribution controlled by multiple factors.

[0044] Exemplary Embodiment 1

[0045] On the one hand, the present invention provides a method for determining the gas-water distribution of a tight sandstone gas reservoir. In Exemplary Embodiment 1, with reference to Figure 2A , the method for determining the gas-water distribution of a tight sandstone gas reservoir includes:

[0046] S100: Analyze the control effects of factors such as source rocks, transport systems, structures, and reservoirs on gas-water distribution, and clarify the control effects and characteristics of different factors on gas-water distribution.

[0047] Classify different control factors affecting gas-water distribution by level, establish a multi-factor hierarchical control mode for the gas-water distribution of tight sandstone rich gas reservoirs, and on this basis, establish the control methods of different control factors on gas-water distribution according to the control characteristics of different control factors.

[0048] S200: According to field outcrops, satellite image measurements, core and logging facies identification, a reservoir 3-6 level configuration division scheme suitable for fluvial facies in tight sandstone rich gas reservoirs (such as the Sulige Gas Field) is formulated. In the 4-level configuration unit division, a new method for dividing high-energy channel and low-energy waterway configuration units is proposed, a geological knowledge base of configuration parameters is constructed, and single wells are divided into hierarchical configuration units. According to the fluvial facies geological characteristics of the study area (refer to Appendix Figure 3 ).

[0049] In this application, core data includes core relative permeability tests, rock electricity experiments, etc. Logging data includes conventional logging data, logging porosity curves, nuclear magnetic resonance logging data, etc.

[0050] S300: Based on step S200, establish a reservoir classification standard based on configuration control, and classify the reservoir types of individual wells. Specifically, the reservoir classification standard can be: divide the reservoir into three categories, including the first type of reservoir (i.e., type I reservoir), the second type of reservoir (i.e., type II reservoir), and the third type of reservoir (i.e., type III reservoir). Among them, type I and type II reservoirs correspond to high-energy channel configuration units, and type I reservoirs correspond to mid-channel bar configurations. In the embodiment, type I reservoirs correspond to mid-channel bar configurations, type II reservoirs correspond to braided channels, type III reservoirs correspond to low-energy channel configurations, and mudstones correspond to floodplain configurations.

[0051] S400: Based on steps S200 and S300, guided by the hierarchical configuration division of the reservoir, adopt the facies modeling idea of "hierarchical configuration control and multi-step joint simulation", organically combine the deterministic modeling and stochastic modeling methods, with the multi-point geostatistical simulation algorithm as the core, and jointly use multiple algorithms such as deterministic simulation, Kriging simulation, sequential indicator simulation, object-based simulation, and sequential Gaussian simulation. Through hierarchical facies control and collaborative constraints, hierarchically establish a reservoir classification lithofacies model that simultaneously includes multi-level configuration units. The fluvial facies geological model established by this method has good consistency with well data, conforms to the fluvial facies geological pattern, can finely depict the spatial distribution characteristics and connectivity of different-level reservoir configurations, and effectively overcomes the limitations of various single facies modeling methods for modeling complex geometric form geological bodies in fluvial facies. For example, the lithofacies model established by the sequential indicator simulation method is too scattered, the model established by the object-based simulation method cannot faithfully conform to the well data; the Kriging simulation method cannot finely depict the details of sand body configurations; the deterministic modeling method relies too much on manual delineation; the multi-point geostatistical modeling method is difficult to obtain high-quality training images and probability constraint conditions without the assistance of other modeling methods, and its application is also restricted. The reservoir classification lithofacies model established by this method is an important facies control condition for gas-water distribution modeling, laying a foundation for the quantitative characterization of complex gas-water distributions.

[0052] Specifically, as Figure 2B shown, the establishment of the reservoir classification lithofacies model includes the following steps:

[0053] S410: Based on well logging stratification and seismic fault structure interpretation, divide the model grid to establish a stratigraphic framework model; based on step S200, establish a channel pattern model using the deterministic modeling method according to the 5-level configuration planar distribution delineation; establish a probability volume model (i.e., sand body probability distribution model) using the sequential indicator simulation and Kriging simulation algorithms; based on the single well coarsening data, using the channel pattern model as the training image and the probability volume model as the distribution constraint, establish a 5-level configuration model of channel sandstone and overbank deposits using the multi-point geostatistical algorithm (refer to Figure 4A and Figure 4B ).

[0054] S420: Characterize through the planar distribution of the fourth-level configuration, establish high-energy channel and low-energy waterway pattern models within the channel using a deterministic modeling method. Under the facies control of the channel sandstone configuration model established in step S410, based on the single-well reservoir classification data, using the channel pattern model as the training image and the probability volume model as the distribution constraint, establish an I+II reservoir model representing the high-energy channel configuration and a Class III reservoir model representing the low-energy waterway using the multiple-point geostatistics algorithm (refer to Figure 5A and Figure 5B ).

[0055] S430: Based on step S200, determine the geometric morphology and parameters of configurations such as mid-channel bars according to the established geological knowledge base of fluvial facies reservoir configuration. Using the high-energy channel model (i.e., the I+II reservoir model) established in step S420 as the facies control and based on the single-well reservoir classification data, establish an I-class reservoir model representing the mid-channel bar configuration and a II-class reservoir model representing the braided channel using the object-based simulation method (refer to Figure 6A and Figure 6B ).

[0056] S500: Return to refer to Figure 2A , coarsen the log porosity curve to the model grid, and under the facies control of step S400, use the sequential Gaussian simulation algorithm to perform porosity parameter interpolation and establish a porosity parameter model (refer to Figure 7A and Figure 7B ). In this embodiment, the sequential Gaussian simulation algorithm is used for model construction, but the present invention is not limited thereto, and one or more of the multiple-point geostatistics, sequential Gaussian, and object-based algorithms can also be used for modeling.

[0057] S600: Based on the core phase permeability experiment and nuclear magnetic logging joint calibration, establish the functional relationship between porosity and irreducible water saturation, and interpret the irreducible water saturation; based on the rock electricity experiment, interpret the single-well water saturation through the Archie formula, and calculate the mobile water saturation using the difference between the single-well water saturation and the irreducible water saturation. It should be noted that in this application, the water saturation is also referred to as the total water saturation.

[0058] S700: Based on the porosity parameter model established in step S500, establish an irreducible water saturation model (also known as the irreducible water model) through the functional relationship between irreducible water saturation and porosity, refer to Figure 8A and Figure 8B , and the irreducible water saturation model mainly reflects the control effect of lithology and physical properties.

[0059] S800: Guided by the hierarchical control mode of gas-water distribution, based on the movable water saturation data of well points, under multi-level phase control, through data analysis and collaborative constraint means, the control effects of source rocks, transport systems, and tectonic controls are gradually reflected in the model during the modeling process, and a movable water saturation model is established (refer to Figure 9A and Figure 9B ).

[0060] S900: Add the irreducible water saturation model established in step S700 to the movable water saturation model established in step S800 to obtain the overall water saturation model. The added water saturation model can reflect both the control of lithology and physical properties and the spatial constraints of source rocks, transport conditions, and tectonic conditions; since this model is obtained by direct addition, there is an error between the model data and the water saturation interpreted by well point reservoir logging, and it cannot be directly used, but it can be used as the model data volume for trend constraints; then, based on the single-well water saturation data interpreted by logging in S800 and using the added water saturation model as the spatial constraint, a sequential Gaussian simulation algorithm is used to establish a fine water saturation model. In this application, the water saturation model is also called the total water saturation model (refer to Figure 10A and Figure 10B ).

[0061] The method for quantitatively characterizing the gas-water distribution of a tight sandstone rich gas reservoir controlled by multiple factors provided by the exemplary embodiments of the present invention can establish a gas-water distribution model under the simultaneous control of multiple factors.

[0062] Exemplary Embodiment 2

[0063] According to another aspect of the present invention, a system for determining the gas-water distribution of a tight sandstone rich gas reservoir is provided. As shown in Figure 11 , in the exemplary embodiment, the determination system includes: a reservoir classification lithofacies module 10, a porosity parameter module 20, an irreducible water saturation logging interpretation module 30, an irreducible water saturation module 40, a movable water saturation module 50, and a total water saturation module 60.

[0064] The reservoir classification lithofacies module 10 is configured to establish a reservoir classification lithofacies model. The construction method of the reservoir classification lithofacies model can refer to S400 in the above exemplary embodiment and will not be elaborated here.

[0065] The porosity parameter module 20 is configured to establish a porosity parameter model based on the coarsened data of logging curves under the phase control of the established reservoir classification lithofacies model. The establishment steps of the porosity parameter model can refer to step S500 in the above exemplary embodiment and will not be elaborated here.

[0066] The irreducible water saturation logging interpretation module 30 is configured to establish an irreducible water saturation logging interpretation model according to the relative permeability test and nuclear magnetic resonance logging data. Specifically, according to the core relative permeability test and nuclear magnetic resonance logging data, a functional relationship between porosity and irreducible water saturation is established to interpret the irreducible water saturation; based on the rock electricity experiment, the single-well water saturation is interpreted through the Archie formula; the movable water saturation is calculated by using the difference between the water saturation and the irreducible water saturation.

[0067] The irreducible water saturation module 40 is configured to establish an irreducible water saturation model based on the porosity parameter model according to the functional relationship between irreducible water saturation and porosity, and the irreducible water saturation model mainly reflects the control effects of lithology and physical properties.

[0068] The movable water saturation module 50 is configured to be guided by the hierarchical control mode of gas-water distribution, based on the coarse data of well-point movable water saturation, and under multi-level phase control, through the analysis of saturation data of different types of reservoirs and collaborative constraint means, the control effects of hydrocarbon source rocks, transport systems, and structures are gradually reflected in the model during the modeling process, and a movable water saturation model is established.

[0069] The water saturation module 60 is configured to establish a water saturation model based on the water saturation data interpreted by logging, with the combined model obtained by adding the irreducible water saturation model and the movable water saturation model as the trend constraint.

[0070] Exemplary Embodiment 3

[0071] This exemplary embodiment provides a computer device, which may include a processor and a memory. The memory is used to store a computer program, and when the computer program is executed by the processor, the method for determining the gas-water distribution of a tight sandstone gas reservoir as described in Exemplary Embodiment 1 can be implemented.

[0072] Exemplary Embodiment 4

[0073] This exemplary embodiment provides a computer-readable storage medium storing a computer program, which can implement the method for determining the gas-water distribution of a tight sandstone gas reservoir as described in Exemplary Embodiment 1 when the computer program is executed by a processor. The computer-readable recording medium is any data storage device that can store data read by a computer system. Examples of computer-readable recording media include: read-only memory, random access memory, compact disc read-only memory, magnetic tape, floppy disk, optical data storage device, and carrier wave (such as data transmission through the Internet via a wired or wireless transmission path).

[0074] To further understand the above Exemplary Embodiment 1, the following further illustrates it with specific examples.

[0075] Example 1

[0076] As Figure 1 and Figure 2A and 2B shown in, taking the gas-water distribution characterization of a tight sandstone rich gas reservoir in the fluvial facies of a certain block as an example:

[0077] Step 1: Analyze the control effects of factors such as source rocks, transport systems, structures, and reservoirs in the block on gas-water distribution, clarify the control effects and characteristics of different factors on gas-water distribution, classify different control factors affecting gas-water distribution hierarchically, establish a multi-factor hierarchical control model for gas-water distribution in tight sandstone rich gas reservoirs. On this basis, establish the control methods of different control factors on gas-water distribution according to the control characteristics of different control factors: namely, lithology and physical properties control the occurrence and saturation of bound water, showing a phase control form; source rocks control the gas-bearing differences in different horizons, showing a layer control form; the transport system controls the regional distribution of mobile water, showing a regional control form; micro-folds control local gas-water differentiation, showing a local point control form (attached Figure 1 , attached Figure 2A and Figure 2B ).

[0078] Step 2: According to field outcrops, satellite image measurements, core and well log facies identification, construct a geological knowledge base of different configuration parameters, and formulate a 3-6 level reservoir configuration division plan for the fluvial facies of the block (refer to attached Figure 3 ). Reservoir configuration characterization mainly focuses on the 4th and 5th level configurations. Among them, the 5th level configuration mainly consists of single channel belt and overbank deposit configurations. In the division of 4th level configuration units, a new method for dividing high-energy channel and low-energy waterway configuration units is proposed. High-energy channels in the 4th level configuration represent channels with strong hydrodynamic energy and large scales, mainly composed of point bar and braided channel configuration units. Low-energy waterways represent channels with low hydrodynamic energy and small scales. Small waterways, abandoned waterways, crevasse channels, floodplains, crevasse fans, and natural levees are all deposits in low-energy environments. Single wells are divided into hierarchical configuration units according to the configuration division plan.

[0079] Step 3: Based on the 4th level configuration units in Step 2, establish a reservoir classification standard, divide the reservoirs into 3 categories. Class I and Class II reservoirs correspond to high-energy waterway configuration units. Among them, Class I reservoirs correspond to point bar configurations. Class III reservoirs are tight sandstones deposited in low-energy waterway, crevasse fan, and natural levee configuration units. Mudstone corresponds to floodplain and abandoned waterway configurations. On this basis, single wells are divided into reservoir types.

[0080] Step 4: On the basis of Step 2, adopt the multiple-point geostatistical simulation method to establish 5th level configuration models of channel sandstones (i.e., channel belt sandstones) and floodplain mudstones (refer to Figure 4A and Figure 4B ).

[0081] Step 5: Under the facies control of the channel sandstone model established in Step 4, based on the single-well reservoir classification data, use the multiple-point geostatistics method to establish an I+II reservoir model representing the high-energy channel configuration and a Class III reservoir model representing the low-energy channel, natural levee, and crevasse splay configurations (refer to Figure 5A and Figure 5B ).

[0082] Step 6: Based on Step 2, determine the geometric morphology and parameters of the point bar configuration. Using the I+II reservoir model established in Step 5 as the facies control and the coarsened data of the single-well reservoir classification as the basis, use the object-based simulation method to establish a Class I reservoir model representing the point bar or lateral bar configuration and a Class II reservoir model representing the braided channel. Establish a reservoir classification lithofacies model under the control of the reservoir configuration (refer to Figure 6A and Figure 6B ).

[0083] Step 7: Based on the coarsened data of the logging curves, coarsen the logging porosity curve to the model grid. Under the facies control of Step 6, use the sequential Gaussian simulation algorithm to perform porosity parameter interpolation and establish a porosity parameter model (refer to Figure 7A and Figure 7B ).

[0084] Step 8: Based on the joint calibration of the core capillary pressure curve and nuclear magnetic logging, establish the functional relationship between porosity and irreducible water saturation, and interpret the irreducible water saturation; based on the rock electricity experiment, interpret the single-well water saturation through the Archie formula, and calculate the movable water saturation using the difference between the water saturation and the irreducible water saturation.

[0085] Step 9: Based on Step 7, establish an irreducible water saturation model according to the functional relationship between the irreducible water saturation and porosity in Step 8. Since the irreducible water saturation and the reservoir porosity are in a functional relationship, the established irreducible water model is mainly controlled by physical properties.

[0086] Step 10: Guided by the multi-factor hierarchical control mode of gas-water distribution established in Step 1, based on the coarsened data of the well-point movable water saturation, under the control of the reservoir classification lithofacies model in Step 8, through the analysis of the water saturation data of different types of reservoirs and the means of collaborative constraint control, reflect the control effects of different control factors step by step in the model during the modeling process (refer to Figure 8A and Figure 8B ).

[0087] First, carry out data analysis under the zonal control of the water-rich area and gas-rich area (controlled by the transport system). Through zonal data analysis and statistics, achieve the zonal control of the movable water distribution by the transport system. In different regions, carry out the data analysis of the movable water saturation under the facies control of different types of reservoirs to achieve the phase-controlled distribution of the movable water by the lithology and physical properties of the reservoir. During the process of zonal and phase-controlled movable water modeling, take the micro-structure removing the tectonic trend as the collaborative constraint condition to achieve the local control of the micro-structure on the movable water saturation distribution. Under the superposition control and constraint of various factors, establish the movable water saturation model (refer to Figure 9A and Figure 9B ). The movable water saturation model is jointly controlled by various factors such as lithology and physical properties, source rock, transport system, and micro-structure.

[0088] Step 11: Based on the water saturation data interpreted by well logging on the basis of Steps 9 and 10, add the irreducible water saturation model and the movable water saturation model as the spatial constraint to establish the water saturation model (refer to Figure 10A and Figure 10B ).

[0089] Based on the multi-factor hierarchical control mode of gas-water distribution, the present invention splits the water saturation into irreducible water and movable water for separate modeling, and then adds and combines them for trend constraint modeling. Through data analysis and collaborative constraint control means in the multi-level facies-controlled modeling process, the control effects of various factors such as source conditions, tectonic conditions, reservoir conditions, and transport conditions on gas-water distribution are reflected step by step in the modeling, realizing the quantitative characterization of gas-water distribution under multi-factor control. The present invention solves the problem that multi-factor simultaneous constraints cannot be achieved in gas-water modeling, and realizes the transformation of the description of complex gas-water distribution under multi-factor control from qualitative analysis to quantitative characterization. According to the method of the present invention, the prediction accuracy of gas-water distribution is greatly improved.

[0090] Although the present invention has been described above in conjunction with exemplary embodiments and the accompanying drawings, those of ordinary skill in the art should understand that various modifications can be made to the above embodiments without departing from the spirit and scope of the claims.

Claims

1. A method for determining the gas-water distribution in a tight sandstone gas reservoir, characterized in that The method includes: establishing a multi-factor hierarchical control model for gas-water distribution in tight sandstone gas reservoirs, and based on the multi-factor hierarchical control model of gas-water distribution, stepwise embedding control factors during the modeling process of irreducible water and mobile water, and establishing a total water saturation model with the combined model of irreducible water and mobile water as the spatial constraint, so as to realize the quantitative characterization of gas-water distribution controlled by multiple factors.

2. The method for determining the gas-water distribution of a tight sandstone gas reservoir according to claim 1, characterized in that Establishing a multi-factor hierarchical control model for gas-water distribution in tight sandstone gas reservoirs includes: Conducting an analysis of the control effects of different control factors on gas-water distribution to clarify the control effects and characteristics of different control factors on gas-water distribution; and Dividing different control factors affecting gas-water distribution into levels and establishing a multi-factor hierarchical control model for gas-water distribution in tight sandstone gas reservoirs; Among them, the control factors include source rocks, transport systems, tectonic controls, and reservoirs.

3. The method for determining the gas-water distribution of a tight sandstone gas reservoir according to claim 2, wherein Establishing the control methods of different control factors on gas-water distribution according to the control characteristics of different control factors includes: The lithology and physical properties of the reservoir control the occurrence and saturation of irreducible water, and the control method is facies control; Source rocks control the gas-bearing differences in different horizons, and the control method is horizon control; The transport system controls the regional distribution of mobile water, and the control method is regional control; Tectonics control the local gas-water differentiation, and the control method is local point control.

4. The method for determining the gas-water distribution of a tight sandstone gas reservoir according to claim 3, characterized in that, The process of irreducible water modeling includes: Establishing a classified reservoir lithofacies model; Based on the coarsened data of well logging curves, under the facies control of the established classified reservoir lithofacies model, establishing a porosity parameter model; According to core phase permeability experiments and nuclear magnetic resonance logging data, establishing a functional relationship between porosity and irreducible water saturation, and interpreting the irreducible water saturation; based on petrophysical experiments, interpreting the water saturation of a single well through Archie's formula; calculating the mobile water saturation using the difference between the water saturation and the irreducible water saturation; and Based on the porosity parameter model, establishing an irreducible water saturation model according to the functional relationship between irreducible water saturation and porosity.

5. The method for determining the gas-water distribution of a tight sandstone gas reservoir according to claim 4, wherein Using the sequential Gaussian simulation algorithm to perform porosity parameter interpolation and establish the porosity parameter model.

6. The method for determining the gas-water distribution of a tight sandstone gas reservoir according to claim 4, wherein The process of mobile water modeling includes: guided by the hierarchical control model of gas-water distribution, based on the coarsened data of well-point mobile water saturation, under multi-level facies control, through data analysis and collaborative constraint means, embodying the control effects of different control factors such as source rocks, transport systems, and tectonic controls in the model step by step during the modeling process, and establishing a mobile water saturation model.

7. The method for determining the gas-water distribution in a tight sandstone gas reservoir according to claim 6, characterized in that Conducting data analysis under the regional control of the transport system, and realizing the regional control of the transport system on the distribution of mobile water through zonal data analysis and statistics; Conducting data analysis of mobile water saturation under the facies control of different types of reservoirs in different regions to realize the phase control of reservoir lithology and physical properties on the distribution of mobile water; During the process of zonal and phase-based mobile water modeling, taking the micro-structure after removing the tectonic trend as a collaborative constraint condition to realize the local control of the micro-structure on the distribution of mobile water saturation; And Under the superposition control and constraint of multiple factors, establishing a mobile water saturation model, and the multiple factors include lithology and physical properties, source rocks, transport systems, and micro-structures.

8. The method for determining the gas-water distribution of a tight sandstone gas reservoir according to claim 6, characterized in that, Building the total water saturation model includes: Adding the irreducible water saturation model and the mobile water saturation model to obtain a total water saturation constraint model; Based on the total water saturation interpreted from well logging and using the total water saturation constraint model obtained by addition as a spatial constraint, a sequential Gaussian simulation algorithm is adopted to build the total water saturation model.

9. The method for determining the gas-water distribution of a tight sandstone gas reservoir according to claim 4, characterized in that, The steps for building the reservoir classification lithofacies model include: According to outcrop, satellite image measurement, core and well logging facies identification, formulate a reservoir hierarchical configuration division plan, construct a geological knowledge base of configuration parameters, and divide the single well into hierarchical configuration units; Establish a reservoir classification standard based on configuration control and divide the single well into reservoir types; Taking the multiple-point geostatistical simulation algorithm as the core, jointly using deterministic simulation, sequential indicator simulation, object-based simulation and sequential Gaussian simulation algorithms, through hierarchical facies control and collaborative constraints, build a reservoir classification lithofacies model that simultaneously includes multiple levels of configuration units in a hierarchical manner.

10. The method for determining the gas-water distribution of a tight sandstone gas reservoir according to claim 9, characterized in that, The hierarchical building of a reservoir classification lithofacies model that simultaneously includes multiple levels of configuration units includes: Building a 5-level configuration model for channel sandstone and overbank deposits; Under the facies control of the channel sandstone model, based on the single well reservoir classification data, using the pattern model as the training image and the probability volume model as the distribution constraint, adopt the multiple-point geostatistical method to build a Class I+II reservoir model representing high-energy channel configuration and a Class III reservoir model representing low-energy channels; and According to the established geological knowledge base of fluvial facies reservoir configuration, determine the geometric shape and parameters of point bar and lateral bar configurations. Using the Class I+II reservoir model as the facies control and based on the coarsened data of single well reservoir classification, adopt the object-based simulation method to build a Class I reservoir model representing point bar configuration and a Class II reservoir model representing braided channels.

11. A system for determining the gas-water distribution in a tight sandstone gas reservoir, characterized in that, The determination system includes: A reservoir classification lithofacies module configured to build a reservoir classification lithofacies model; A porosity parameter module configured to build a porosity parameter model based on the coarsened data of well logging curves under the facies control of the built reservoir classification lithofacies model; An irreducible water saturation well logging interpretation module configured to establish a functional relationship between porosity and irreducible water saturation according to core capillary pressure test and nuclear magnetic resonance well logging data, and interpret the irreducible water saturation; interpret the single well water saturation through the Archie formula based on rock-electricity experiments; calculate the mobile water saturation using the difference between the water saturation and the irreducible water saturation; An irreducible water saturation module configured to build an irreducible water saturation model based on the porosity parameter model according to the functional relationship between the irreducible water saturation and porosity; A mobile water saturation module configured to be guided by a hierarchical control mode of gas-water distribution, based on the well point mobile water saturation data, under multi-level facies control, through data analysis and collaborative constraint means, incorporate the control effects of hydrocarbon source rocks, transport systems and tectonic controls into the model step by step during the modeling process, and build a mobile water saturation model; and The total water saturation module is configured to add the irreducible water saturation model and the mobile water saturation model to obtain a total water saturation constraint model. Based on the total water saturation obtained from log interpretation and using the total water saturation constraint model obtained by addition as a spatial constraint, a sequential Gaussian simulation algorithm is adopted to establish a total water saturation model.

12. A computer device, characterized in that, The computer device includes: a processor; and a memory storing a computer program, which when executed by the processor, implements the method for determining the gas-water distribution in a tight sandstone gas reservoir according to any one of claims 1-10.

13. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for determining the gas-water distribution in a tight sandstone gas reservoir according to any one of claims 1-10.

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