CN116738672A - Method for establishing complex biological reef bottom water and gas reservoir numerical simulation model - Google Patents
Method for establishing complex biological reef bottom water and gas reservoir numerical simulation model Download PDFInfo
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Abstract
The application discloses a method for establishing a complex biological reef bottom water and gas reservoir numerical simulation model, which comprises the following steps: s1, establishing a reservoir attribute model based on geological modeling achievements; s2, establishing a seepage physical model by using the gas-water seepage curve, the capillary pressure curve, the J function and the rock compression coefficient; establishing a gas reservoir fluid saturation model by adopting a balance initialization method; s3, establishing a gas reservoir numerical simulation model by combining the fluid attribute model, the well model and the dynamic model, and operating the model; s4, comparing the model calculation result with the geological reserves, the fluid saturation and the production dynamic data, S5, modifying the variables, and repeating the steps S2-S5 until the fitting is completed. The application fully considers the range of the adjustable parameters and the uncertainty thereof, effectively shortens a large number of repeated fitting processes caused by the cross influence between multiple parameters and multiple indexes, reduces the multiple solutions of model adjustment, improves the fitting efficiency, and improves the prediction precision and the reliability.
Description
Technical Field
The application relates to the technical field of petroleum and natural gas exploitation, in particular to a method for establishing a complex biological reef bottom water and gas reservoir numerical simulation model.
Background
The biological reef gas reservoir in China is rich in resources and huge in reserves, and the biological reef gas reservoir is strong in heterogeneity and can develop in different degrees in side bottom water. The problems of large prediction difficulty, complex gas-water distribution, non-uniform gas-water interface in well logging interpretation and non-realization of stratum water spreading range can occur in the water-gas reservoir at the bottom of the biological reef; in particular to a complex reef beach phase bottom water and gas reservoir of a reef body, and the development degree, range, communication degree and the like of a reservoir and stratum water have larger uncertainty.
The conventional numerical simulation flow is a single-index one-way fitting process, namely, a numerical simulation model is established, initialization is completed, reserve fitting is carried out, and finally production history fitting and production prediction are carried out; for a conventional heterogeneous and weak oil and gas reservoir, uncertain parameters are relatively few, and the conventional flow can well complete numerical simulation. For complex biological reef bottom water and gas reservoirs, because of the fact that uncertain parameters are more, fitting efficiency can be reduced due to mutual correlation and mutual influence among the steps, and fitting multi-solution property can influence the rationality of model adjustment and the reliability of prediction, so that gas reservoir prediction indexes and gas reservoir development benefits are influenced.
Therefore, in order to reduce the polynomials of model adjustment, improve the fitting efficiency, and obtain a fine numerical simulation model more consistent with the actual gas reservoir, a new fitting method and process are required to be provided, so as to reduce a large number of repeated fitting processes caused by the cross influence between multiple parameters and multiple indexes, and achieve the purpose of improving the prediction precision and the reliability.
Disclosure of Invention
The application aims to provide a method for establishing a complex biological reef bottom water and gas reservoir numerical simulation model, which is used for shortening a large number of repeated fitting processes caused by the cross influence between multiple parameters and multiple indexes, achieving the purposes of reducing the multiresolution of model adjustment, improving fitting efficiency, improving prediction precision and reliability degree, and obtaining a fine numerical simulation model more conforming to an actual gas reservoir.
In order to achieve the above object, the present application provides the following technical solutions:
a method for establishing a complex biological reef bottom water and gas reservoir numerical simulation model comprises the following steps:
s1, establishing a reservoir attribute model based on geological modeling achievements;
the reservoir property model includes a build model, a porosity model, and a permeability model;
s2, on the basis of the reservoir property model established in the step S1, establishing a seepage physical model by using a gas-water seepage curve, a capillary pressure curve, a J function and a rock compression coefficient;
combining the fluid model, temperature and pressure system data and the depth of the air-water interface, and establishing a gas reservoir fluid saturation model by adopting a balance initialization method to finish model initialization; the method comprises the steps of carrying out a first treatment on the surface of the
The fluid attribute model comprises a seepage physical model, a fluid saturation model and a natural gas stratum water attribute model;
s3, establishing an initial gas reservoir numerical simulation model by combining the well model and the dynamic model, and operating the initial gas reservoir numerical simulation model to calculate pre-geological reserves, pre-fluid saturation and pre-production dynamic data;
s4, comparing the pre-geological reserves calculated in the step S3 with the actual geological reserves, the pre-fluid saturation and the actual saturation, and the pre-production dynamic data with the actual production dynamic data one by one at the same time, and completing one-round fitting;
and S5, if the difference of the comparison results of the three groups of data in the step S4 does not meet the requirement, taking at least one of a gas-water permeability curve, a capillary pressure curve, a J function, a gas-water interface depth and conductivity as a modification variable, simultaneously taking fluid saturation, geological reserves and production dynamic data as fitting targets, repeating the steps S2-S4, and carrying out multi-round fitting until the difference of the comparison results of the three groups of data in the step S4 meets the requirement, namely, fitting is completed, so as to obtain a final gas reservoir numerical simulation model.
The application discloses a method for establishing a complex biological reef bottom water and gas reservoir numerical simulation model, which mainly comprises the following steps: s1, establishing a reservoir attribute model based on geological modeling achievements; the reservoir property model includes a build model, a porosity model, and a permeability model; s2, on the basis of the reservoir property model established in the step S1, establishing a seepage physical model by using a gas-water seepage curve, a capillary pressure curve, a J function and a rock compression coefficient; combining the fluid model, temperature and pressure system data and the depth of the air-water interface, and establishing a gas reservoir fluid saturation model by adopting a balance initialization method to finish model initialization; the fluid attribute model comprises a seepage physical model, a fluid saturation model and a natural gas stratum water attribute model; s3, establishing an initial gas reservoir numerical simulation model by combining the well model and the dynamic model, and operating the initial gas reservoir numerical simulation model to calculate pre-geological reserves, pre-fluid saturation and pre-production dynamic data; s4, comparing the pre-geological reserves calculated in the step S3 with the actual geological reserves, the pre-fluid saturation and the actual saturation, and the pre-production dynamic data with the actual production dynamic data one by one at the same time, and completing one-round fitting; and S5, if the difference of the comparison results of the three groups of data in the step S4 does not meet the requirement, taking at least one of a gas-water permeability curve, a capillary pressure curve, a J function, a gas-water interface depth and conductivity as a modification variable, simultaneously taking fluid saturation, geological reserves and production dynamic data as fitting targets, repeating the steps S2-S4, and carrying out multi-round fitting until the difference of the comparison results of the three groups of data in the step S4 meets the requirement, namely, fitting is completed, so as to obtain a final gas reservoir numerical simulation model. According to the complexity of the water and gas reservoir at the bottom of the biological reef, taking the depth of a gas-water interface, an permeability curve and a capillary pressure curve as uncertain parameters, and synchronously carrying out model establishment, model initialization and production history fitting; considering the simultaneous influence of a single parameter on a plurality of indexes on the whole, and simultaneously adjusting a plurality of influence parameters such as gas-water interface depth, capillary pressure, permeability, an permeability curve and the like, and taking reservoir fitting, gas reservoir pressure fitting, single well pressure fitting and water production fitting into consideration while establishing a fluid saturation distribution field and a water body model; the specificity of different fitting targets, pertinence of parameter adjustment and overall consistency are considered, repeated cross influence between multiple parameters and multiple indexes is avoided, and polynomials can be effectively reduced, and fitting efficiency is improved.
Meanwhile, the method for establishing the complex biological reef bottom water and gas reservoir numerical simulation model fully considers the range of adjustable parameters and uncertainty thereof, can effectively shorten a large number of repeated fitting processes caused by the cross influence between multiple parameters and multiple indexes, reduces the multiresolution of model adjustment, improves fitting efficiency, improves prediction precision and reliability, and obtains a fine numerical simulation model more conforming to the actual gas reservoir such as logging interpretation saturation, production dynamics and the like.
Further, in the step S2, the gas-water permeability curve is a classification normalization curve, that is, according to the reservoir classification standard, the gas-water permeability experiment of the gas reservoir core is classified, and the permeability curve of each type of core sample is normalized, so as to obtain the gas-water permeability curve capable of representing various reservoirs;
the specific steps for acquiring the gas-water phase curve are as follows:
step a, normalizing the infiltration end point of the core sample according to the formula (2) to obtainAnd->Obtaining +.>Curve sum->A curve;
step b, pairingCurve sum->Respectively carrying out regression fitting on the curves to respectively obtain a and b;
step c, a, b and S of each type of core sample wi And S is gr Averaging to obtain an average valueGiven S wD Obtaining an average relative permeability curve of each type of standardized core sample according to a formula (3);
step d, the average relative permeability curve of each type of core sample obtained in the step c is subjected to de-standardization according to the formula (4) to obtain the average relative permeability curve of each type of core sample, namely, the gas-water phase curve;
wherein K is rg Is the relative permeability of gas phase, S w Is water saturation, K rw A, b are relative permeability of water phaseIs a regression constant, S wi For initial water saturation, S gr Is the residual gas saturation, S wD The value range is 0-1 for the constant water saturation.
Further, in the step S2, the capillary pressure curve is a normalized classification curve, i.e. classifying the gas reservoir core mercury injection experiment according to the reservoir classification standard, and classifying and normalizing the capillary pressure curve by J function processing for each type of core sample, i.e. establishing J-S of the core sample wn Obtaining a capillary pressure curve which can represent various reservoirs according to the relation;
the specific steps for obtaining the capillary pressure curve are as follows:
step A, establishing the movable water saturation S of each core sample wn J-S of (2) wn The relation is shown in the formula (5) and the formula (6):
wherein S is wn For movable water saturation, P c Is capillary pressure value, sigma is fluid interfacial tension, theta is wetting angle; phi is the porosity of the core, S wc Is critical water saturation, A, B is constant;
step B, pair J-S wn The relation regression fit yields a J function,
step C, averaging constants A and B of each type of core sample to obtain each type of core sample with representativenessThe function of the function is that,i.e. capillaryA pressure curve.
Further, in the step S2, the depth of the air-water interface is the result of logging interpretation, dynamic monitoring and comprehensive analysis of ecological dynamic quantity obtained in the actual monitoring process. The method is influenced by factors such as reservoir development heterogeneity, data recording and the like, and has stronger uncertainty and larger adjustable range; the reference depth and reference pressure are formation pressures within the formation of interest as measured or converted by the gas well.
Further, in step S2, the specific steps of obtaining the fluid saturation model are as follows: s is obtained according to the formula (7) wn Then the water saturation S is obtained by de-normalization according to the formula (6) w Obtaining a fluid saturation model;
wherein P is c,res For reservoir actual capillary pressure value, sigma res For reservoir actual fluid interfacial tension, θ res Reservoir actual wetting angle.
Further, in the step S3,
determining a natural gas formation water attribute model according to high-pressure physical property experimental data of natural gas and formation water;
establishing a well model based on a seepage theory and a vertical pipe flow theory according to a gas well test result and a well test interpretation result;
and establishing a dynamic model according to the dynamic data of gas well production and the dynamic monitoring data.
Further, the model water saturation and geological reserves are determined based on the following principles:
according to the gravity difference balance principle of capillary pressure and gas-water in the gas reservoir, the height of stratum water which can rise under the capillary pressure imbibition action, namely the height of a gas-water transition zone, is determined by the following formula (8):
due to reservoir physical property heterogeneity, capillary pressure is different in all parts in the same depth plane, and the water saturation determined by the capillary pressure is also different; for example, for a height h above the air-water interface GWC j The average water saturation of which can be obtained by the formula (9).
The water saturation distribution (air-water interface, capillary pressure and permeability endpoint value), air-containing area, effective thickness, porosity and pressure of the whole air reservoir jointly determine the reserves of the air reservoir, namely a volumetric reserve formula (10), and reserve fitting is carried out based on the principle:
further, in the step S3, the pre-production dynamic data includes pre-daily water yield and pre-pressure fitting.
Further, the pre-daily water yield is determined by the following method:
the gas-water interface, capillary pressure curve and the end point value of the permeability curve are used as main adjusting parameters for simultaneously influencing saturation fitting and reserve fitting, and the influence on water production fitting should be considered simultaneously during adjustment; in addition, the directional permeability and the relative permeability of the aqueous phase need to be considered.
A certain height is h j According to k rg (S wj ) And k rw (S wj ) The volume average water saturation S of the water phase can be obtained w The relative permeability is a pseudo-function of the following, equation (11):
for the gas-water two-phase flow, the pre-daily water yield is calculated by a yield equation (12):
wherein l=g, w; for steady state flow, f=0.5; for quasi-steady-state flow, f=0.75.
Further, the precompression fit is determined by the following method:
for a gas well without actually measured bottom hole flow pressure, when single well pressure is fitted, wellhead oil pressure is used as a fitting target, and gas reservoir data outside the well bore are required to be adjusted on the basis of ensuring well bore loss prediction accuracy, so that the multiple solutions of the well bore pressure loss part are not discussed.
According to the pressure transient theory adopted by Mattax and Dalton, under single-phase, transient conditions, the solution of the transient flow problem can be expressed as a form (13) of an exponential integral function, namely:
equation (13) is derived for a single phase flow, but similarly for a multiphase flow, the relevant conclusion can be drawn, i.e., the magnitude of the pressure drop value p i -p and (qμB)/(4πβ) c k H h) And (4α) c β c k H /(φc t μr 2 ) Forming a positive correlation;
in pressure fitting, the pressure change at a given wellbore location can be changed and corrected by adjusting the reservoir related parameters, which have been fully considered in saturation and reserve fitting and production fitting, which is an overall adjustment, simultaneous fitting process.
Further, in the step S5, the requirement that the three groups of occurrence comparison results are different is that the reserve error is less than or equal to 5%, and the single well fitting rate of the production history index fitting is more than or equal to 85%.
Compared with the prior art, the application has the following beneficial effects:
1. the application discloses a method for establishing a complex biological reef bottom water and gas reservoir numerical simulation model, which mainly comprises the following steps: s1, establishing a reservoir attribute model based on geological modeling achievements; the reservoir property model includes a build model, a porosity model, and a permeability model; s2, on the basis of the reservoir property model established in the step S1, establishing a seepage physical model by using a gas-water seepage curve, a capillary pressure curve, a J function and a rock compression coefficient; combining the fluid model, temperature and pressure system data and the depth of the air-water interface, and establishing a gas reservoir fluid saturation model by adopting a balance initialization method to finish model initialization; the fluid attribute model comprises a seepage physical model, a fluid saturation model and a natural gas stratum water attribute model; s3, establishing an initial gas reservoir numerical simulation model by combining the well model and the dynamic model, and operating the initial gas reservoir numerical simulation model to calculate pre-geological reserves, pre-fluid saturation and pre-production dynamic data; s4, comparing the pre-geological reserves calculated in the step S3 with the actual geological reserves, the pre-fluid saturation and the actual saturation, and the pre-production dynamic data with the actual production dynamic data one by one at the same time, and completing one-round fitting; and S5, if the difference of the comparison results of the three groups of data in the step S4 does not meet the requirement, taking at least one of a gas-water permeability curve, a capillary pressure curve, a J function, a gas-water interface depth and conductivity as a modification variable, simultaneously taking fluid saturation, geological reserves and production dynamic data as fitting targets, repeating the steps S2-S4, and carrying out multi-round fitting until the difference of the comparison results of the three groups of data in the step S4 meets the requirement, namely, fitting is completed, so as to obtain a final gas reservoir numerical simulation model. According to the complexity of the water and gas reservoir at the bottom of the biological reef, taking the depth of a gas-water interface, an permeability curve and a capillary pressure curve as uncertain parameters, and synchronously carrying out model establishment, model initialization and production history fitting; considering the simultaneous influence of a single parameter on a plurality of indexes on the whole, and simultaneously adjusting a plurality of influence parameters such as gas-water interface depth, capillary pressure, permeability, an permeability curve and the like, and taking reservoir fitting, gas reservoir pressure fitting, single well pressure fitting and water production fitting into consideration while establishing a fluid saturation distribution field and a water body model; the specificity of different fitting targets, pertinence of parameter adjustment and overall consistency are considered, repeated cross influence between multiple parameters and multiple indexes is avoided, and polynomials can be effectively reduced, and fitting efficiency is improved.
2. The method for establishing the complex biological reef bottom water and gas reservoir numerical simulation model fully considers the range of adjustable parameters and uncertainty thereof, can effectively shorten a large number of repeated fitting processes caused by the cross influence between multiple parameters and multiple indexes, reduces the multiresolution of model adjustment, improves fitting efficiency, improves prediction precision and reliability, and obtains a fine numerical simulation model more conforming to the actual gas reservoir such as logging interpretation saturation, production dynamics and the like.
Drawings
FIG. 1 is a flow chart of a complex biological reef bottom water and gas reservoir numerical simulation synchronous fitting method and application;
FIG. 2 is a three-dimensional view of a complex reef-bottom water and gas reservoir configuration and porosity model in an embodiment;
FIG. 3 is a three-dimensional view of a permeability model of a complex reef-bottom water reservoir in an embodiment;
FIG. 4 is a diagram of a complex biological reef bottom water and gas reservoir A reef cluster core sample phase permeation curve classification normalization result in an embodiment;
FIG. 5 is a schematic diagram of a complex biological reef bottom water and gas reservoir A reef cluster core sample J-S in an embodiment wn A relational curve;
FIG. 6 is a simplified flow chart of a history fit of a complex reef bottom water and gas reservoir numerical simulation synchronization fitting method in an embodiment;
FIG. 7 is a cross-sectional view of a complex biological reef bottom water and gas reservoir A reef cluster gas saturation model in an embodiment.
FIG. 8 is a fitted graph of the complex reef bottom water and gas reservoir X1 well bottom (formation) pressure and wellhead pressure in an example;
FIG. 9 is a fitted graph of daily water production of an X1 well of a complex reef-bottom water and gas reservoir in an embodiment.
Detailed Description
The present application will be described in further detail with reference to test examples and specific embodiments. It should not be construed that the scope of the above subject matter of the present application is limited to the following embodiments, and all techniques realized based on the present application are within the scope of the present application.
A complex biological reef bottom water and gas reservoir numerical simulation synchronous fitting method and application are shown in figure 1, and comprise the following steps:
s1, establishing a reservoir attribute model based on geological modeling achievements;
the reservoir property model includes a build model, a porosity model, and a permeability model;
s2, on the basis of the reservoir property model established in the step S1, establishing a seepage physical model by using a gas-water seepage curve, a capillary pressure curve, a J function and a rock compression coefficient;
combining the fluid model, temperature and pressure system data and the depth of the air-water interface, and establishing a gas reservoir fluid saturation model by adopting a balance initialization method to finish model initialization;
the fluid attribute model comprises a seepage physical model, a fluid saturation model and a natural gas stratum water attribute model;
s3, establishing an initial gas reservoir numerical simulation model by combining the well model and the dynamic model, and operating the initial gas reservoir numerical simulation model to calculate pre-geological reserves, pre-fluid saturation and pre-production dynamic data;
s4, comparing the pre-geological reserves calculated in the step S3 with the actual geological reserves, the pre-fluid saturation and the actual saturation, and the pre-production dynamic data with the actual production dynamic data one by one at the same time, and completing one-round fitting;
and S5, if the difference of the comparison results of the three groups of data in the step S4 does not meet the requirement, taking at least one of a gas-water permeability curve, a capillary pressure curve, a J function, a gas-water interface depth and conductivity as a modification variable, simultaneously taking fluid saturation, geological reserves and production dynamic data as fitting targets, repeating the steps S2-S4, and carrying out multi-round fitting until the difference of the comparison results of the three groups of data in the step S4 meets the requirement, namely, fitting is completed, so as to obtain a final gas reservoir numerical simulation model.
In the embodiment, taking a complex biological reef gas reservoir in Sichuan as an example, the biological reef gas reservoir belongs to the biological reef deposition at the edge of a bench, develops 4 reef belts, 21 reef clusters and 90 single reefs, has small scale, longitudinally and synchronously develops a plurality of reefs in multiple stages and horizontally overlaps the reefs, has the geological characteristics of 'one super, three high and five complex', and is a lithologic gas reservoir which is a structure with high sulfur content, locally existing edge (bottom) water and controlled by the reef beach. The gas reservoir is put into production and developed in 2014 in 12 months, and at present, 13 production wells are filled with stratum water, and the water-gas ratio is 0.5 square/square-15 square/square. Only 8 wells of the 13 producing wells develop an impure gas layer, and 6 wells do not develop an impure gas layer. The gas-water interface is not uniform in well logging interpretation, and the formation water spreading range is not realized; the development degree, range, communication degree and the like of the reservoir and the stratum water have large uncertainty.
The complex biological reef bottom water gas reservoir numerical simulation synchronous fitting method disclosed by the application is described by combining the gas reservoir, and the method is specifically as follows:
s1, establishing a reservoir attribute model based on geological modeling achievements, wherein the reservoir attribute model comprises a construction model, a porosity model, a permeability model and the like;
in step S1, according to reservoir development, fluid distribution, development dynamic characteristics and the like of the gas reservoir, the gas reservoir structural model, the porosity model and the permeability model are established by using seismic data, geological data, logging data, core gas-water permeability experimental data, core mercury-pressing experimental data and the like, as shown in fig. 2 and 3.
S2, establishing a seepage physical model and a fluid saturation model of the gas reservoir by using a gas-water seepage curve, capillary pressure, J function, gas-water interface depth, reference depth and reference pressure and adopting a balance method.
The gas reservoir has complex gas-water relationship, and the depth of a gas-water interface is the result of comprehensive analysis according to logging interpretation, dynamic monitoring, production dynamic and other data, is influenced by factors such as reservoir development heterogeneity, data logging and the like, has stronger uncertainty and larger adjustable range and is about-6280 meters to-6350 meters; the reference depth and the reference pressure are 66.69MPa of the stratum pressure measured at the position of well27 well-5599.66 m, and 13 balance areas are set according to the data of 37 production wells of the gas reservoir.
In step S2, 74 mercury-pressing experimental core samples and 25 gas-water infiltration experimental core samples of the gas reservoir are selected, and the experimental results are compared and analyzed by combining logging interpretation, core analysis, production dynamics and other data, so that the samples can be considered to represent most reservoir types of the reef cluster. The permeability curve and capillary pressure curve of each core sample were obtained after the experiment, as shown in fig. 4 and 5.
S21, the gas-water permeability curve is a classification normalization curve, namely, the gas-water permeability experiment of the gas reservoir core is classified and normalized according to the reservoir classification standard, so that the gas-water permeability curve which can represent various reservoirs is obtained.
As shown in fig. 4, the endpoint values of the phase permeation curve of the class I sample obtained after normalization are respectively: the saturation of the irreducible water is 38.49%, the permeability of the irreducible underwater gas phase is 0.560, the saturation of the isotonic point gas is 23.72%, and the relative permeability of the isotonic point is 0.109; class II samples had a irreducible water saturation of 41.34%, an irreducible underwater gas phase permeability of 0.334, an isoosmotic point gas saturation of 21.91%, and an isoosmotic point relative permeability of 0.073; class III sample has irreducible water saturation of 53.52%, irreducible underwater gas phase permeability of 0.061, isoosmotic point gas saturation of 13.15%, and relative isoosmotic point of 0.021.
S22, classifying and normalizing capillary pressure curves of each type of core samples by utilizing J function processing to establish a J-Swn relation of the core samples;
the contact angle of mercury with air in a laboratory is known to be 140 degrees, and the interfacial tension is known to be 480mN/m; the contact angle of the gas-water interface under the stratum condition is 0 DEG, and the interfacial tension is obtained by calculating the difference value of the gas-water interface tension under different temperature and pressure conditions due to the influence of the temperature and the pressure. The temperature of the gas reservoir of the Yuanba Changxing group is generally about 152 ℃ and the formation pressure is about 68MPa, so that the gas-water two-phase interfacial tension is 35.68mN/m under the condition of the gas reservoir.
Normalizing various samples according to the classification result of the core mercury-pressing experiment (figure 4) to obtain the J-Swn relation after the average of the I type samplesJ-Swn relation after averaging of class II samplesJ-Swn relation after class III sample averaging +.>As shown in particular in fig. 5.
S3, determining a natural gas attribute model according to the high-pressure physical property experimental data of the natural gas of the gas reservoir; and establishing a well model comprising information such as well bore pressure loss, near zone reservoir layer transformation degree and the like according to a well test result and a well test interpretation result aiming at 37 production wells based on a seepage theory and a vertical pipe flow theory. Establishing a dynamic model according to the production dynamic data and the dynamic monitoring data of 37 production wells; setting a gas-water interface to be-6290 m, classifying and using a normalized phase permeability curve and capillary pressure curve, combining a fluid attribute model, a well model and a dynamic model to build a gas reservoir numerical simulation model, and operating the model.
S4, comparing the model calculation result with the geological reserves, the fluid saturation and the production dynamic data, finding out indexes which do not accord with the actual data, and analyzing the reasons for the differences, wherein the reasons are shown in FIG. 3.
Because of the complex geological conditions of the biological reef gas reservoir, the reservoir continuity is poor, and the parameters are determined only by comparison at well points; the well spacing is larger (about 2000 m), the number of vertical wells is smaller, the degree of knowledge among the wells is lower, and the accuracy of transverse prediction is difficult to control and judge; in the development process of the gas reservoir, the formation pressure data is less, the productivity well test data is less, the production well has no production logging and actual bottom hole flow pressure measurement, and the influence range and the productivity change of the gas well are difficult to master; part of high-yield wells are closed for a long time, production data are few, and production dynamics of the high-yield wells are difficult to know. Therefore, parameters such as the net thickness of a reservoir layer, capillary force, stratum water distribution, permeability and the like between the complex biological reef gas reservoirs of the meta-dam are difficult to predict, and the uncertainty is strong; these uncertainty parameters can have varying degrees of impact on reserve fitting, pressure fitting, and water production fitting.
Because the uncertain parameters are more, and the fitting targets are influenced by the plurality of uncertain parameters, different numbers or different types of parameter combinations are selected when the model is adjusted, and a satisfactory fitting effect can be obtained; obtaining an adjustment result which is more consistent with the practice of the gas reservoir, and relying more on experience and knowledge of the gas reservoir; if the degree of geologic awareness is low or the physical meaning of the existence of certain static and dynamic phenomena is erroneously understood, the geologic model is likely to be erroneously modified.
S5, the model is readjusted by taking the seepage, capillary pressure, J function, gas-water interface, conductivity and the like as main modification variables, and the steps S2 to S5 are repeated until the fitting is completed, and the method is particularly shown in FIG. 6.
According to the method for establishing the complex biological reef bottom water and gas reservoir numerical simulation model, on the basis of the first round of fitting, the depth of a gas-water interface, an phase-permeability curve and a capillary pressure curve are taken as uncertain parameters, and the next round of fitting is carried out again, namely, the establishment of the model, the initialization of the model and the production history fitting are synchronously carried out; considering the simultaneous influence of a single parameter on a plurality of indexes on the whole, and simultaneously adjusting a plurality of influence parameters such as gas-water interface depth, capillary pressure, permeability, an permeability curve and the like, and taking reservoir fitting, gas reservoir pressure fitting, single well pressure fitting and water production fitting into consideration while establishing a fluid saturation distribution field and a water body model; the specificity of different fitting targets, pertinence of parameter adjustment and overall consistency are considered, repeated cross influence between multiple parameters and multiple indexes is avoided, multiple solutions are effectively reduced, and fitting efficiency is improved.
Repeating the steps S2-S5 to obtain the final numerical simulation model of the gas reservoir, wherein the error between the model reserve of 1090.05 hundred million square and the geological reserve of 1046.11 hundred million square is 4.2 percent, which is smaller than the reserve error required in the technical Specification SY_T 6744-2019 for the numerical simulation of the gas reservoir, which is smaller than 5 percent. Meanwhile, the production history indexes of 37 production wells, such as daily gas production, daily water production, water breakthrough time, wellhead oil pressure and the like, are consistent with the actual production conditions of the production wells. The wellhead oil pressure fitting rate is up to 95%, the daily water yield fitting rate is affected by the recognition degree and the like and is 87%, the single well fitting rate is more than 85%, and the fitting precision in the later fitting stage is higher than that in the initial and middle fitting stages.
Taking an independent reef cluster of the A well region as an example, the porosity, the permeability and the initial fluid saturation field of the model are compared with the well logging interpretation result of the A well inclined pilot hole, and the overall anastomosis is good, as shown in figure 7.
Logging to explain that the A well inclined guide hole (-6248.68) to (-6257.75) meters is a class II gas layer, and the water saturation is 3.2-23.7%; the water saturation is 3.2-28% for layers 4 and 5 in the model. The (-6257.75) to (-6260.78) meters of the inclined guide eye is the same layer of air and water, the water saturation is 38.9% -87%, the 6 th layer is in the model, and the water saturation is 34% -42%. The (-6260.78) to (-6298.22) meter position of the inclined guide eye is a water layer, the water saturation is 15.2% -100%, the 7 th-15 th layer is in the model, and the water saturation is 29% -100%.
The elevation depth of a free water interface at the inclined guide eye track of the A well in the model is-6289 m; -6258 meters of water saturation from 34% to 41% and being the top of the gas-water transition zone, which is consistent with the same layer of logging solution gas-water; the natural gas geological reserve of the reef cluster model is 27.89 hundred million square, and the volume of the bottom water body is 2857 trillion square; calculating the error of the geological reserve (29.24 hundred million square) of the natural gas by a volumetric method, and the error of the water volume (3000 trillion square) by 4.6 percent; about 3.7 times the volume of water.
As seen from the wellhead oil pressure fitting graph of the a well (fig. 8), the synchronous fitting method is closer to the actual wellhead oil pressure than the conventional fitting method; from the daily water yield fitting graph of well a (fig. 9), it is seen that the synchronous fitting method is closer to the actual daily water yield than the conventional fitting method.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.
Claims (10)
1. The method for establishing the complex biological reef bottom water and gas reservoir numerical simulation model is characterized by comprising the following steps of:
s1, establishing a reservoir attribute model based on geological modeling achievements;
the reservoir property model includes a build model, a porosity model, and a permeability model;
s2, on the basis of the reservoir property model established in the step S1, establishing a seepage physical model by using a gas-water seepage curve, a capillary pressure curve, a J function and a rock compression coefficient;
combining the fluid model, temperature and pressure system data and the depth of the air-water interface, and establishing a gas reservoir fluid saturation model by adopting a balance initialization method to finish model initialization;
the fluid attribute model comprises a seepage physical model, a fluid saturation model and a natural gas stratum water attribute model;
s3, establishing an initial gas reservoir numerical simulation model by combining the well model and the dynamic model, and operating the initial gas reservoir numerical simulation model to calculate pre-geological reserves, pre-fluid saturation and pre-production dynamic data;
s4, comparing the pre-geological reserves obtained by the calculation in the step S3 with the actual geological reserves, the pre-fluid saturation and the actual saturation, and the pre-production dynamic data with the actual production dynamic data one by one at the same time, and completing one round of fitting;
and S5, if the difference of comparison results of the three groups of data in the step S4 does not meet the requirement, taking at least one of a gas-water permeability curve, a capillary pressure curve, a J function, a gas-water interface depth and conductivity as a modification variable, simultaneously taking fluid saturation, geological reserves and production dynamic data as fitting targets, repeating the steps S2-S4, and performing multi-round fitting until the difference of the comparison results of the three groups of data in the step S4 meets the requirement, namely, the fitting is completed, so as to obtain the final gas reservoir numerical simulation model.
2. The method for establishing a complex biological reef bottom water-gas reservoir numerical simulation model according to claim 1, wherein in the step S2, the gas-water permeability curve is a classification normalization curve, that is, according to reservoir classification standards, a gas reservoir core gas-water permeability experiment is classified and the permeability curve of each type of core sample is normalized to obtain a gas-water permeability curve capable of representing various reservoirs;
the specific steps for acquiring the gas-water phase curve are as follows:
step a, normalizing the infiltration end point of the core sample according to the formula (2) to obtainAnd->Obtaining +.>Curve sum->A curve;
step b, pairingCurve sum->Respectively carrying out regression fitting on the curves to respectively obtain a and b; step c, a, b and S of each type of core sample wi And S is gr Averaging to obtain average->Given S wD Obtaining an average relative permeability curve of each type of standardized core sample according to a formula (3);
step d, the average relative permeability curve of each type of core sample obtained in the step c is subjected to de-standardization according to the formula (4) to obtain the average relative permeability curve of each type of core sample, namely, the gas-water phase curve;
wherein K is rg Is the relative permeability of gas phase, S w Is water saturation, K rw For the relative permeability of the water phase, a and b are regression constants, S wi For initial water saturation, S gr Is the residual gas saturation, S wD The value range is 0-1 for the constant water saturation.
3. The method for building a complex biological reef bottom water and gas reservoir numerical simulation model according to claim 1, wherein in the step S2, the capillary pressure curve is a classification normalization curve, namely, according to reservoir classification standards, classifying a gas reservoir core mercury injection experiment and classifying and normalizing the capillary pressure curve by J function processing for each type of core sample, namely, building a J-S of the core sample wn Obtaining a capillary pressure curve which can represent various reservoirs according to the relation;
the specific steps for obtaining the capillary pressure curve are as follows:
step A, building each rockMovable water saturation S of heart sample wn J-S of (2) wn The relation is shown in the formula (5) and the formula (6):
wherein S is wn For movable water saturation, P c Is capillary pressure value, sigma is fluid interfacial tension, theta is wetting angle; phi is the porosity of the core, S wc Is critical water saturation, A, B is constant;
step B, pair J-S wn The relation regression fit yields a J function,
step C, averaging constants A and B of each type of core sample to obtain a representative J function of each type,namely, the capillary pressure curve.
4. The method for building a complex biological reef-bottom water-gas reservoir numerical simulation model according to claim 1, wherein in the step S2, the gas-water interface depth is a result of comprehensive analysis of well logging interpretation, dynamic monitoring and ecological dynamic quantity obtained in the actual monitoring process.
5. The method for building a complex biological reef bottom water and gas reservoir numerical simulation model according to claim 1, wherein in step S2, the specific steps of obtaining the fluid saturation model are as follows: s is obtained according to the formula (7) wn Then the water saturation S is obtained by de-normalization according to the formula (6) w Obtaining the fluidA saturation model;
wherein P is c,res For reservoir actual capillary pressure value, sigma res For reservoir actual fluid interfacial tension, θ res Reservoir actual wetting angle.
6. A method for establishing a complex biological reef bottom water and gas reservoir numerical simulation model according to claim 1, wherein in the step S3,
determining a natural gas formation water attribute model according to high-pressure physical property experimental data of natural gas and formation water;
establishing a well model based on a seepage theory and a vertical pipe flow theory according to a gas well test result and a well test interpretation result;
and establishing a dynamic model according to the dynamic data of gas well production and the dynamic monitoring data.
7. A method for establishing a complex biological reef bottom water and gas reservoir numerical simulation model according to claim 1, wherein in the step S3, the pre-production dynamic data includes pre-daily water yield and pre-pressure fitting.
8. The method for establishing the complex biological reef bottom water and gas reservoir numerical simulation model according to claim 7, wherein the pre-daily water yield is determined by the following method:
a certain height is h j According to k rg (S wj ) And k rw (S wj ) The volume average water saturation of the water phase can be obtainedThe relative permeability is a pseudo-function of the following, equation (11):
for the gas-water two-phase flow, the pre-daily water yield is calculated by a yield equation (12):
wherein l=g, w; for steady state flow, f=0.5; for quasi-steady-state flow, f=0.75.
9. A method of establishing a complex biological reef-bottom water and gas reservoir numerical simulation model according to claim 7 wherein the pre-pressure fit is determined by:
according to the pressure transient theory, under single-phase, transient conditions, the solution of the transient flow problem can be expressed as a form (13) of an exponential integral function, namely:
i.e. the magnitude p of the pressure drop value i P and (qμB) (4πβ) c k H h) And (4α) c β c k H (φc t μr 2 ) In positive correlation.
10. The method for building a complex biological reef bottom water and gas reservoir numerical simulation model according to any one of claims 1 to 9, wherein in step S5, the requirement that the three sets of comparison results are different is that the reserve error is less than or equal to 5% and the production history index fitting single well fitting rate is more than or equal to 85%.
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| CN118095140A (en) * | 2024-04-17 | 2024-05-28 | 成都理工大学 | Method for diagnosing effective reservoir capacity of side water and gas reservoir type gas reservoir |
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