A method for evaluating and determining the boundaries of three-dimensional engineering sweet spots in differentiated tight reservoirs

Abstract

The present invention relates to a method for evaluating and determining the boundaries of a three-dimensional engineering sweet spot for a differentiated tight reservoir, which belongs to the technical field of oil and gas field development, and includes: calculating the porosity, permeability, and water saturation parameters of rocks; using numerical simulation methods to establish energy storage parameters for evaluating geological sweet spots; calculating a comprehensive evaluation index based on rock brittleness and a comprehensive evaluation index of ground stress based on the minimum horizontal principal stress; determining a comprehensive rupture rate based on the ratio of tensile fracture cracks and shear fracture cracks, and calculating a comprehensive evaluation index of natural fractures; combining a comprehensive evaluation index based on rock brittleness, a comprehensive evaluation index of ground stress, and a comprehensive evaluation index of natural fractures to form an engineering sweet spot model; and using field collected data to perform post-engineering sweet spot evaluation. The present invention can directly obtain and predict the unimpeded flow characteristics under the differentiated engineering sweet spot mode through the characteristics of geological sweet spots, achieve more accurate well location and layer deployment, and improve the construction effect of the entire fracturing well.

Description

Technical Field

[0001] The invention relates to a method for evaluating and determining a boundary of a differentiated tight reservoir three-dimensional engineering sweet spot, and belongs to the technical field of oil and gas field development. Background Art

[0002] Tight oil and gas resources have become an effective alternative to my country's energy. However, my country's oil and gas reservoirs are usually buried deep and have complex ground stress conditions, so the transformation is extremely difficult. In order to more specifically improve the efficiency of hydraulic fracturing and completion construction in tight reservoirs and realize the efficient commercial exploitation of tight oil and gas resources, it is extremely important to conduct a comprehensive engineering sweet spot evaluation of tight reservoirs. However, since the main objects of the existing compressibility evaluation work in the oil and gas field are shale gas reservoirs or directly derived from the brittleness evaluation experience in the development of unconventional reservoirs in North America, the results are sometimes not ideal when conducting brittleness evaluation of tight oil and gas resources. Secondly, compressibility assessment involves multiple factors such as brittleness, ground stress, and natural fractures. Therefore, the construction of a comprehensive compressibility model requires consideration of the coupling of multiple factors, which are usually divided into primary and secondary.

[0003] In the past, the compressibility evaluation methods mainly relied on core or logging data, so the evaluation results obtained were mainly the interpretation results of the core layer or single well, and it was not possible to effectively carry out the inter-well compressibility evaluation. Secondly, the post-evaluation of the compressibility of tight reservoirs has always been an issue that needs to be considered in the construction of compressibility models. Generally speaking, most monitoring methods such as microseismic and digital optical fiber are used to directly invert the morphology of hydraulic fractures, thereby evaluating the accuracy of reservoir compressibility. However, due to economic cost constraints, the above-mentioned fracture monitoring activities are likely to be carried out only in a few wells on site, and the compressibility evaluation lacks first-hand data. The open flow parameter is the first-hand data on production on site, but the open flow parameter is affected by both geological and engineering factors, and is more susceptible to the influence of geological parameters to a large extent, so it is usually difficult to directly establish the relationship between the open flow and the engineering sweet spot. Summary of the invention

[0004] In order to solve the problem that the current engineering sweet spot prediction and evaluation results cannot be fully verified by effective data, the present invention provides a differentiated tight reservoir three-dimensional engineering sweet spot evaluation and boundary determination method, which firstly constructs geological parameters to model the geological sweet spot, then determines the classification boundary of the geological sweet spot through the unobstructed flow data, and then conducts a comprehensive classification of the geological sweet spots. On this basis, a geomechanical model is further constructed, and the engineering sweet spot is modeled through the brittleness, ground stress, natural fractures and other parameters of the work area. After eliminating the influence of geological parameters, the relationship between the engineering sweet spot and the unobstructed flow is further established. This method can directly obtain and predict the unobstructed flow characteristics under the differentiated engineering sweet spot mode through the geological sweet spot characteristics, achieve more accurate well location and layer deployment, and improve the construction effect of the entire fracturing well.

[0005] The present invention adopts the following technical solutions:

[0006] A method for evaluating and determining the boundaries of three-dimensional engineering sweet spots in differentiated tight reservoirs comprises the following steps:

[0007] S1: Calculate rock parameters such as porosity, permeability, and water saturation through core description and logging curves;

[0008] S2: Use numerical simulation methods to form the distribution field of rock porosity, permeability, and gas saturation, and combine reservoir porosity and other parameters to establish energy storage parameters for geological sweet spot evaluation;

[0009] S3: Collect open flow parameters on site, classify the open flow on site by clustering method, and divide differentiated geological sweet spots;

[0010] S4: Construct a three-dimensional geomechanical model of the target reservoir, and calculate the comprehensive evaluation index based on rock brittleness and the comprehensive evaluation index of geostress based on the minimum horizontal principal stress;

[0011] S5: Construct a natural fracture model for the study area and determine the comprehensive fracture rate I based on the ratio of tensile fractures to shear fractures. c , calculate the comprehensive evaluation index of natural fractures;

[0012] S6: Based on the analysis of differentiated geological sweet spots, the engineering sweet spot model is constructed by combining the comprehensive evaluation index based on rock brittleness, the comprehensive evaluation index of ground stress and the comprehensive evaluation index of natural fractures;

[0013] S7: Use the data collected on site to conduct post-evaluation of the engineering sweet spot, realize the weight correction of differentiated engineering sweet spots, and determine the boundary map of the on-site engineering sweet spot;

[0014] The weight correction process is as follows: select 80% of the data as sample data to regress and obtain the relevant weight coefficients of Class I, Class II, and Class III engineering sweet spots, and use the remaining 20% ​​of the data as test data. If the regression coefficient meets the conditions, stop the calculation and determine the final weight coefficient.

[0015] Preferably, in step S1, lithology description is performed based on downhole thin section information and core observation data, and lithology identification is performed in combination with logging data. On the basis of lithology identification, the porosity parameters of the rock are calculated through gamma, density and other logging curves (acoustic time difference, density and resistivity). The porosity parameter model uses the principal component multiple regression method to obtain a multiple regression equation of density, acoustic time difference, deep resistivity and porosity. The porosity parameter is as shown in formula (1):

[0016] Φ=m1·DEN+m2·DT-m3·RD+m4 (1)

[0017] Among them, Φ represents the porosity of rock, DEN represents density data, DT represents acoustic time difference data, RD represents deep resistivity data, and m1, m2, m3, and m4 represent the empirical coefficients of multiple regression respectively; among them, density data, acoustic time difference data, deep resistivity data, etc. need to be obtained through well logging.

[0018] Preferably, in step S1, considering that the tight sandstone reservoir has strong heterogeneity and large permeability variation within the layer, only the sand layer group or single sand layer is used as the interpretation unit, ignoring the differences in physical properties and seepage characteristics inside the sand layer, resulting in low accuracy of permeability calculated by logging. Therefore, the permeability is calculated based on the flow characteristics, wherein the compensation density, natural gamma and acoustic wave time difference curves are selected as variables through sensitivity analysis and multivariate regression statistical analysis of the logging response, and a multi-parameter fitting model of the flow unit index is established; first, the flow unit index FZI value is calculated by formula (2), and the flow unit is defined as a rock reservoir with the same flow characteristics, and then the correlation between porosity and permeability suitable for different flow units is established, such as formula (3) to formula (5):

[0019] logFZI=7.087-0.017*GR-2.026*DEN+0.02*DT (2)

[0020] Class I FZI>3.31, K=0.2798e^(0.4548Φ) (3)

[0021] Class II 0.98 <FZI<3.31,K=0.0294e^(0.4542Φ) (4)

[0022] Class III FZI>0.27, K=0.0068e^(0.434Φ) (5)

[0023] Among them, FZI represents flow unit, GR represents natural gamma value, DT represents acoustic time difference, K represents rock permeability, and Φ represents rock porosity.

[0024] Preferably, in step S1, to determine the gas saturation, it is necessary to first establish a nonlinear Archie water saturation model applicable to different diagenetic phases to calculate the water saturation, and then inversely calculate the gas saturation based on the water saturation;

[0025] where the nonlinear Archie water saturation is given by:

[0026]

[0027] S w +S q =1 (9)

[0028] Among them, S w Indicates water saturation, S q represents gas saturation, m is the pore cementation index, a4 is the lithology coefficient, which is 1, xb and xn are the coefficients of the nonlinear Archie water saturation model, and the values ​​of xb and xn need to be determined by inversion of the stratified position, which are generally related to the reservoir properties and are different from the b and n values ​​in the Archie formula. t Represents the formation resistivity, R w Indicates the resistivity of formation water;

[0029] F represents the formation factor, dimensionless, R o Indicates the resistivity of 100% water-bearing rock, represents the effective porosity;

[0030] I represents the resistivity index, dimensionless;

[0031] Preferably, in step S2, according to the porosity, permeability and gas saturation values ​​of the rock, a three-dimensional distribution field of the porosity, permeability and gas saturation values ​​of the rock is obtained by a random modeling method, and the modeling process mainly includes a structural model, and a lithofacies and attribute parameter model, wherein the structural model mainly reflects the spatial stereoscopic framework and microstructural characteristics of the reservoir, and uses the single well geological stratification data, combined with the top surface structure of each layer group and each small layer in the study area, to achieve the construction of a three-dimensional structural model; on the basis of the three-dimensional structural model, according to the lake delta deposition model, a three-dimensional lithofacies model is established by comprehensively utilizing core, well logging and other data, and then the porosity, permeability, gas saturation and other attribute parameter models are established by using the phase-controlled modeling idea, and the entire three-dimensional modeling process is a conventional modeling process in this field;

[0032] The energy storage coefficient Q is calculated using the calculated rock porosity, permeability, and gas saturation. p, used to construct a comprehensive geological sweet spot model, the relevant formula is as follows:

[0033] Q P =Φ×S q ×K (10)

[0034] In the formula, Q p represents the reservoir coefficient, Φ is the porosity, K is the permeability, S q is the gas saturation value.

[0035] The energy storage coefficient calculation formula is constructed by weighted product of key geological reservoir parameters, and is used to evaluate the geology and thus classify the basic geological conditions of tight reservoirs.

[0036] Preferably, in step S3, after collecting the open flow parameters on site, the actual production of different fracturing layers is determined by production splitting, the production data is statistically analyzed using the K-means clustering method, and a map of open flow and energy storage coefficient is constructed. The boundaries of differentiated geological sweet spots are determined according to the inflection points of the nonlinear curve, and the boundaries of differentiated geological sweet spots are used to divide the geological sweet spots, and the relevant classification of differentiated sweet spots is determined, which can be specifically divided into three levels, namely, Class I geological sweet spots, Class II geological sweet spots, and Class III geological sweet spots;

[0037] The present invention collects the open flow parameters on site and further divides the interval flowback of the entire production according to the calculated reservoir coefficient to determine the relevant range of differentiated geological sweet spots.

[0038] Preferably, in step S4, a one-dimensional geomechanical model consisting of rock strength characteristics and stress distribution is constructed in combination with all neighboring well data, wherein all neighboring well data include open hole logging data, core geomechanical test experiments, ground drain tests, pore pressure tests, and drilling logs, etc., and inter-well parameters are obtained by using seismic data through lithology control constraints, and three-dimensional rock mechanics field calculations are carried out using an interpolation method, and a three-dimensional geomechanical model is obtained at the same time;

[0039] First, the logging data is used to calculate the rock mechanical parameters of a single well, including Young's modulus, Poisson's ratio, rock tensile strength, fracture toughness and other parameters, thereby obtaining a comprehensive evaluation index F based on rock brittleness. I1 :

[0040] F I1 =aE G +bv G +cS G +dK G (11)

[0041] Among them, E G , ν G , S G , KG Can be expressed as:

[0042]

[0043]

[0044] In the formula, E G is the normalized Young's modulus, ν G is the normalized Poisson's ratio, S G is the normalized rock tensile strength, K G is the normalized rock fracture toughness value, a, b, c, d are weight coefficients affecting the brittleness index value, E is the actual rock Young's modulus, E max is the maximum rock Young's modulus, E min is the minimum rock Young's modulus, dimensionless; ν is the actual Poisson's ratio, ν max is the maximum Poisson's ratio, ν min is the minimum Poisson's ratio, dimensionless; S is the actual tensile strength, S max is the maximum tensile strength, S min is the minimum tensile strength, dimensionless; K is the actual rock fracture toughness value, K max is the maximum rock fracture toughness value, K min is the minimum rock fracture toughness value, in GPa.

[0045] Preferably, in step S4, one-dimensional geostress field data is calculated using well logging data, wherein pore pressure is estimated based on well logging curves, formation fluid invasion and column gas observations, and using methods such as Eaton or Bowers effective stress is the most common method for predicting formation pore pressure;

[0046] The direction of the horizontal principal stress is predicted by the borehole collapse and drilling-induced tensile fractures observed in the imaging logging (the direction of the minimum horizontal principal stress can be obtained by using the statistical rose diagram of the direction of the borehole collapse fractures, and the direction of the maximum horizontal principal stress can be obtained by using the statistical rose diagram of the direction of the drilling-induced tensile fractures). The Shmin effective stress ratio can be calibrated by Minifrac and XLOT, and the SHmax effective stress ratio can be calibrated by the observed value of the borehole damage. Furthermore, the three-dimensional reservoir geostress calculation is carried out, and the three-dimensional elastic modulus model, the three-dimensional Poisson's ratio model, etc. are combined to perform the three-dimensional geostress field inversion by the finite element method, and the rock mechanics module is estimated from the empirical equation using the logging data, and calibrated with the core geostress acoustic emission or differential strain experimental data;

[0047] Select the combined spring model to calculate the horizontal stress, as shown in the following formula:

[0048] The combined spring model takes into account the influence of overburden pressure, tectonic stress and pore pressure on the ground stress, and assumes the formation to be a homogeneous and isotropic body. The calculation formula is:

[0049]

[0050] Among them, σ H represents the maximum horizontal principal stress, σ h represents the minimum horizontal principal stress, v is the actual Poisson's ratio, σ v is the overburden pressure, α is the Biot coefficient, P P is the formation pore pressure, E S is the static rock Young's modulus, ε H is the tectonic stress coefficient in the direction of the maximum principal stress, ε h is the tectonic stress coefficient in the direction of minimum principal stress;

[0051] Calculation of the comprehensive evaluation index F of ground stress based on the minimum horizontal principal stress I3 :

[0052]

[0053] Among them, F I3 represents the comprehensive evaluation index of geostress, σ hmax Indicates the highest value of the minimum horizontal principal stress in the evaluation layer, σ hmin Indicates the lowest value of the minimum horizontal principal stress in the evaluation layer segment.

[0054] It should be noted that it is necessary to determine the upper and lower limits of the differentiated engineering sweet spots by the type of geological sweet spots. In order to further constrain the characteristics of the sweet spots, the upper and lower limits in the differentiated engineering sweet spots should be the upper and lower limits in the space containing the main fracturing layers.

[0055] Preferably, in step S5, firstly, large-scale natural fractures are constrained by using the ant body interpretation results, and small and medium-scale natural fractures are constructed by random modeling methods, and at the same time, they need to be constrained by combining imaging data to construct a natural fracture model of the research area; the present invention identifies the distribution characteristics of natural fractures by core observation or imaging logging, and the method adopted is a multi-source natural fracture modeling method; since some interpretation intervals do not have the development characteristics of natural fractures, a three-dimensional finite element method is used for natural fracture modeling and analysis; firstly, the proportion of tensile fracture and shear fracture is determined by combining downhole thin sections and core data (generally obtained by conducting experiments), and the weight of the proportion of the two is obtained, and the spatial development intensity of natural fractures is further calculated by the comprehensive fracture rate, specifically:

[0056] According to the ratio of tensile fracture cracks and shear fracture cracks, the weight of the two is obtained to determine the comprehensive fracture rate Ic :

[0057]

[0058] In the formula, I c is the comprehensive rupture rate, d1 and d2 are the proportions of observed tensile rupture cracks and shear rupture cracks, respectively, and I t is the tensile rupture coefficient, dimensionless, I n is the shear rupture coefficient, dimensionless;

[0059] The comprehensive rupture rate I c The method of assigning values ​​to layers is used to import them into the three-dimensional geomechanical model as a constraint condition for controlling the spatial distribution of fractures. At the same time, different rock fracture criteria are used for different natural fracture types. For tension fractures, the Griffith fracture criterion is used, and for shear fractures, the Mohr-Coulomb criterion is used. Based on the comprehensive fracture index, the fracture density on a single well (this parameter is mainly obtained through imaging logging interpretation) is used as hard data, and the comprehensive fracture rate is used as a spatial constraint. The sequential indication method is used to randomly simulate the fracture density in layers, delineate and divide the fracture development grade area in the reservoir, and calculate the comprehensive evaluation index of natural fractures:

[0060]

[0061] Among them, F I2 Represents the comprehensive evaluation index of natural fractures, I cmax It represents the highest comprehensive rupture rate within the evaluation layer interval, I c It represents the comprehensive rupture rate of natural fractures in the evaluation layer interval, I cmin Indicates the minimum comprehensive rupture rate within the evaluation layer interval.

[0062] Preferably, in step S6, the comprehensive calculation formula of the Class I engineering sweet spot, Class II engineering sweet spot, and Class III engineering sweet spot in the engineering sweet spot model is as follows:

[0063] F1=a1F I1 +b1F I2 +c1F I3 (20)

[0064] F2=a2F I1 +b2F I2 +c2F I3 (twenty one)

[0065] F3=a3F I1 +b3F I2 +c3F I3 (twenty two)

[0066] Among them, a1, b1, and c1 are the relevant weight coefficients of class I engineering sweet spots, a2, b2, and c2 are the relevant weight coefficients of class II engineering sweet spots, and a3, b3, and c3 are the relevant weight coefficients of class III engineering sweet spots. The weight coefficients can be determined by methods such as fuzzy hierarchical analysis.

[0067] For any details not provided in the present invention, please refer to the prior art.

[0068] The beneficial effects of the present invention are:

[0069] The present invention establishes a method for evaluating engineering sweet spots in tight reservoirs under differentiated geological conditions, which fully eliminates the influence of geological sweet spot characteristics on compressibility factors (the main steps are firstly to determine the geological sweet spot through the energy storage coefficient, and then select areas with consistent geological sweet spots to carry out engineering sweet spot evaluation), making it convenient for on-site operators to evaluate engineering sweet spots at the same physical property scale.

[0070] The method for evaluating engineering sweet spots in tight reservoirs under differentiated geological conditions of the present invention takes into account the brittleness, biaxial stress difference and development characteristics of natural fractures, and can realize the prediction of three-dimensional engineering sweet spots, while establishing the relationship between engineering sweet spots and unimpeded flow.

[0071] The method for evaluating engineering sweet spots of tight reservoirs under differentiated geological conditions of the present invention further sets upper and lower limits of dimensionless parameter regression according to differentiated geological sweet spots during dimensionless parameter processing, thereby more accurately optimizing engineering sweet spots. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The drawings in the specification, which constitute a part of the present application, are used to provide further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0073] Figure 1 is a flow chart of the present invention;

[0074] Figure 2 It is a schematic diagram of the energy storage coefficient division result;

[0075] Figure 3 This is the interpretation result of natural fracture distribution in tight reservoirs;

[0076] Figure 4 Interpretation results map for tight reservoir engineering sweet spot;

[0077] Figure 5 Figure 1 is the interpretation result diagram of differentiated tight reservoir engineering sweet spots, where (a) is Class I engineering sweet spot, (b) is Class II engineering sweet spot, and (c) is Class III engineering sweet spot. DETAILED DESCRIPTION

[0078] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the drawings in the implementation of this specification, but are not limited to this. Anything not fully described in the present invention shall be based on the conventional technology in the art.

[0079] Example 1

[0080] A method for evaluating and determining the boundaries of three-dimensional engineering sweet spots in differentiated tight reservoirs, such as Figure 1 As shown, the following steps are included:

[0081] S1: Calculate rock parameters such as porosity, permeability, and water saturation through core description and logging curves;

[0082] Lithology description is carried out based on downhole thin section information and core observation data, and lithology identification is carried out in combination with logging data. On the basis of lithology identification, the porosity parameters of rocks are calculated through gamma, density and other logging curves (acoustic time difference, density and resistivity). The porosity parameter model uses the principal component multiple regression method to obtain the multiple regression equation of density, acoustic time difference, deep resistivity and porosity. The porosity parameter is as shown in formula (1):

[0083] Φ=m1·DEN+m2·DT-m3·RD+m4 (1)

[0084] Among them, Φ represents the porosity of rock, DEN represents density data, DT represents acoustic time difference data, RD represents deep resistivity data, m1, m2, m3, and m4 represent the empirical coefficients of multiple regression respectively, and the values ​​of m1, m2, m3, and m4 can be 46.285, 0.85, -0.04, and 118.467 respectively; among them, density data, acoustic time difference data, deep resistivity data, etc. need to be obtained through well logging.

[0085] Preferably, in step S1, considering that the tight sandstone reservoir has strong heterogeneity and large permeability variation within the layer, only the sand layer group or single sand layer is used as the interpretation unit, ignoring the differences in physical properties and seepage characteristics inside the sand layer, resulting in low accuracy of permeability calculated by logging. Therefore, the permeability is calculated based on the flow characteristics, wherein the compensation density, natural gamma and acoustic wave time difference curves are selected as variables through sensitivity analysis and multivariate regression statistical analysis of the logging response, and a multi-parameter fitting model of the flow unit index is established; first, the flow unit index FZI value is calculated by formula (2), and the flow unit is defined as a rock reservoir with the same flow characteristics, and then the correlation between porosity and permeability suitable for different flow units is established, such as formula (3) to formula (5):

[0086] logFZI=7.087-0.017*GR-2.026*DEN+0.02*DT (2)

[0087] Class I FZI>3.31, K=0.2798e^(0.4548Φ) (3)

[0088] Class II 0.98 <FZI<3.31,K=0.0294e^(0.4542Φ) (4)

[0089] Class III FZI>0.27, K=0.0068e^(0.434Φ) (5)

[0090] Among them, FZI represents flow unit, GR represents natural gamma value, DT represents acoustic time difference, K represents rock permeability, and Φ represents rock porosity.

[0091] Preferably, in step S1, a nonlinear Archie water saturation model applicable to different diagenetic phases is established to calculate water saturation. The nonlinear Archie water saturation is as follows:

[0092]

[0093] S w +S q =1 (9)

[0094] Among them, S w Indicates water saturation, S q represents gas saturation, m is the pore cementation index, with a value range of about 0.9-1.1, a4 is the lithology coefficient, with a value of 1, xb and xn are the coefficients of the nonlinear Archie water saturation model, and the values ​​of xb and xn need to be determined by inversion through stratification, which are generally related to reservoir properties. In this embodiment, the specific values ​​of xb and xn are 65 and 4, respectively, which are different from the b and n values ​​in the Archie formula. t Represents the formation resistivity, R w Indicates the resistivity of formation water;

[0095] F represents the formation factor, dimensionless, R o Indicates the resistivity of 100% water-bearing rock, represents the effective porosity;

[0096] I represents the resistivity index, dimensionless;

[0097] Formula (7) needs to be obtained through rock electrical experiments. The resistivity values ​​Ro of some samples after saturation with water are measured, and then F is related to porosity to obtain a and m. In formula (8), xb and xn also need to be obtained through rock electrical experiments. According to the rock resistivity values ​​at different water saturations, the resistance increase rate I is obtained. By relating I to Sw, xb and xn can be fitted. Based on the above experiments, a, m, xb and xn values, i.e. known quantities, can be obtained. Using formula (6), Sw can be calculated according to the formation resistivity Rt and the calculated porosity value.

[0098] S2: Use numerical simulation methods to form the distribution field of rock porosity, permeability, and gas saturation, and combine reservoir porosity and other parameters to establish energy storage parameters for geological sweet spot evaluation;

[0099] According to the porosity, permeability and gas saturation values ​​of the rock, the three-dimensional distribution field of the porosity, permeability and gas saturation values ​​of the rock is obtained through the random modeling method. The modeling process mainly includes the structural model, as well as the lithofacies and attribute parameter models. The structural model mainly reflects the spatial stereoscopic framework and microstructural characteristics of the reservoir. The three-dimensional structural model is constructed by using the geological stratification data of a single well and combining the top surface structure of each layer group and each small layer in the study area. On the basis of the three-dimensional structural model, according to the lake delta deposition model, the three-dimensional lithofacies model is established by comprehensively utilizing the core, well logging and other data, and then the attribute parameter models such as porosity, permeability and gas saturation are established by using the phase-controlled modeling idea. The entire three-dimensional modeling process is a conventional modeling process in this field.

[0100] The energy storage coefficient Q is calculated using the calculated rock porosity, permeability, and gas saturation. p , used to construct a comprehensive geological sweet spot model, the relevant formula is as follows:

[0101] Q P =Φ×S q ×K (10)

[0102] In the formula, Q p represents the reservoir coefficient, Φ is the porosity, K is the permeability, S q is the gas saturation value.

[0103] The energy storage coefficient calculation formula is constructed by weighted product of key geological reservoir parameters, and is used to evaluate the geology and thus classify the basic geological conditions of tight reservoirs.

[0104] S3: Collect open flow parameters on site, classify the open flow on site by clustering method, and divide differentiated geological sweet spots;

[0105] After collecting the open flow parameters on site, the actual production of different fracturing layers is determined by production splitting. The K-means clustering method is used to count the production data, and the open flow and energy storage coefficient map is constructed. The boundaries of differentiated geological sweet spots are determined according to the inflection points of the nonlinear curve. The boundaries of differentiated geological sweet spots are used to divide the geological sweet spots and determine the relevant classification of differentiated sweet spots. Specifically, they can be divided into three levels, namely, Class I geological sweet spots, Class II geological sweet spots, and Class III geological sweet spots. Figure 2 Three types of atmosphere.

[0106] The present invention collects the open flow parameters on site and further divides the interval flowback of the entire production according to the calculated reservoir coefficient to determine the relevant range of differentiated geological sweet spots.

[0107] S4: Construct a three-dimensional geomechanical model of the target reservoir, and calculate the comprehensive evaluation index based on rock brittleness and the comprehensive evaluation index of geostress based on the minimum horizontal principal stress;

[0108] A one-dimensional geomechanical model consisting of rock strength characteristics and stress distribution is constructed by combining all neighboring well data, including open hole logging data, core geomechanical test experiments, ground drain tests, pore pressure tests, and drilling logs. Through lithology control constraints, seismic data is used to obtain inter-well parameters, and interpolation methods are used to perform three-dimensional rock mechanics field calculations, and a three-dimensional geomechanical model is obtained at the same time;

[0109] First, the logging data is used to calculate the rock mechanical parameters of a single well, including Young's modulus, Poisson's ratio, rock tensile strength, fracture toughness and other parameters, thereby obtaining a comprehensive evaluation index F based on rock brittleness. I1 :

[0110] F I1 =aE G +bv G +cS G +dK G (11)

[0111] Among them, E G , ν G , S G , K G Can be expressed as:

[0112]

[0113]

[0114] In the formula, E G is the normalized Young's modulus, ν G is the normalized Poisson's ratio, S G is the normalized rock tensile strength, KG is the normalized rock fracture toughness value, a, b, c, d are weight coefficients affecting the brittleness index value. In this embodiment, the specific values ​​of a, b, c, d are 0.35, 0.2, 0.2, 0.25, respectively, E is the actual rock Young's modulus, E max is the maximum rock Young's modulus, E min is the minimum rock Young's modulus, dimensionless; ν is the actual Poisson's ratio, ν max is the maximum Poisson's ratio, ν min is the minimum Poisson's ratio, dimensionless; S is the actual tensile strength, S max is the maximum tensile strength, S min is the minimum tensile strength, dimensionless; K is the actual rock fracture toughness value, K max is the maximum rock fracture toughness value, K min is the minimum rock fracture toughness value, in GPa.

[0115] In step S4, one-dimensional geostress field data is calculated using well logging data, wherein pore pressure is estimated based on well logging curves, formation fluid invasion and column gas observations. The most common method for predicting formation pore pressure is to use methods such as Eation or Bowers effective stress;

[0116] The direction of the horizontal principal stress is predicted by the borehole collapse and drilling-induced tensile fractures observed in the imaging logging (the direction of the minimum horizontal principal stress can be obtained by using the statistical rose diagram of the direction of the borehole collapse fractures, and the direction of the maximum horizontal principal stress can be obtained by using the statistical rose diagram of the direction of the drilling-induced tensile fractures). The Shmin effective stress ratio can be calibrated by Minifrac and XLOT, and the SHmax effective stress ratio can be calibrated by the observed value of the borehole damage. Furthermore, the three-dimensional reservoir geostress calculation is carried out, and the three-dimensional elastic modulus model, the three-dimensional Poisson's ratio model, etc. are combined to perform the three-dimensional geostress field inversion by the finite element method, and the rock mechanics module is estimated from the empirical equation using the logging data, and calibrated with the core geostress acoustic emission or differential strain experimental data;

[0117] Select the combined spring model to calculate the horizontal stress, as shown in the following formula:

[0118] The combined spring model takes into account the influence of overburden pressure, tectonic stress and pore pressure on the ground stress, and assumes the formation to be a homogeneous and isotropic body. The calculation formula is:

[0119]

[0120] Among them, σ H represents the maximum horizontal principal stress, σ h represents the minimum horizontal principal stress, v is the actual Poisson's ratio, σv is the overburden pressure, α is the Biot coefficient, P P is the formation pore pressure, E S is the static rock Young's modulus, ε H is the tectonic stress coefficient in the direction of the maximum principal stress, ε h is the tectonic stress coefficient in the direction of minimum principal stress;

[0121] Calculation of the comprehensive evaluation index F of ground stress based on the minimum horizontal principal stress I3 :

[0122]

[0123] Among them, F I3 represents the comprehensive evaluation index of geostress, σ hmax Indicates the highest value of the minimum horizontal principal stress in the evaluation layer, σ hmin Indicates the lowest value of the minimum horizontal principal stress in the evaluation layer segment.

[0124] It should be noted that it is necessary to determine the upper and lower limits of the differentiated engineering sweet spots by the type of geological sweet spots. In order to further constrain the characteristics of the sweet spots, the upper and lower limits in the differentiated engineering sweet spots should be the upper and lower limits in the space containing the main fracturing layers.

[0125] S5: Construct a natural fracture model for the study area and determine the comprehensive fracture rate I based on the ratio of tensile fractures to shear fractures. c , calculate the comprehensive evaluation index of natural fractures;

[0126] First, large-scale natural fractures are constrained by using the results of ant body interpretation, and small and medium-scale natural fractures are constructed by random modeling methods. At the same time, they need to be constrained by combining imaging data to construct a natural fracture model for the research area. The present invention identifies the distribution characteristics of natural fractures by core observation or imaging logging, and adopts a multi-source natural fracture modeling method. Since some interpretation intervals do not have the development characteristics of natural fractures, a three-dimensional finite element method is used for natural fracture modeling and analysis. First, the proportion of tensile fractures and shear fractures is determined by combining downhole thin sections and core data (generally obtained by experiments), and the weight of the two is obtained. The spatial development intensity of natural fractures is further calculated by the comprehensive fracture rate. Specifically:

[0127] According to the ratio of tensile fracture cracks and shear fracture cracks, the weight of the two is obtained to determine the comprehensive fracture rate I c :

[0128]

[0129] In the formula, I cis the comprehensive rupture rate, d1 and d2 are the proportions of observed tensile rupture cracks and shear rupture cracks, respectively, and I t is the tensile rupture coefficient, dimensionless, I n is the shear rupture coefficient, dimensionless; in this embodiment, when I t ≥1, rock shear fracture occurs; when I n ≥1, rock tensile fracture occurs. The types of microcracks can be identified through core, thin section, electron microscope observation, CT and other technologies. The weight of tensile fracture and inter-fracture ratio is further obtained through data statistics. The comprehensive fracture rate of the study area is 1.18, which means that natural fractures are weakly developed.

[0130] The comprehensive rupture rate I c The method of assigning values ​​to layers is used to import them into the three-dimensional geomechanical model as a constraint condition for controlling the spatial distribution of fractures. At the same time, different rock fracture criteria are used for different natural fracture types. For tension fractures, the Griffith fracture criterion is used, and for shear fractures, the Mohr-Coulomb criterion is used. Based on the comprehensive fracture index, the fracture density on a single well (this parameter is mainly obtained through imaging logging interpretation) is used as hard data, and the comprehensive fracture rate is used as a spatial constraint. The sequential indication method is used to randomly simulate the fracture density in layers, delineate and divide the fracture development grade area in the reservoir, and calculate the comprehensive evaluation index of natural fractures:

[0131]

[0132] Among them, F I2 Represents the comprehensive evaluation index of natural fractures, I cmax It represents the highest comprehensive rupture rate within the evaluation layer interval, I c It represents the comprehensive rupture rate of natural fractures in the evaluation layer interval, I cmin Indicates the minimum comprehensive rupture rate within the evaluation layer interval.

[0133] S6: Based on the analysis of differentiated geological sweet spots, the engineering sweet spot model is constructed by combining the comprehensive evaluation index based on rock brittleness, the comprehensive evaluation index of ground stress and the comprehensive evaluation index of natural fractures;

[0134] The comprehensive calculation formulas for Class I engineering sweet spots, Class II engineering sweet spots, and Class III engineering sweet spots in the engineering sweet spot model are as follows:

[0135] F1=a1F I1 +b1F I2 +c1F I3 (20)

[0136] F2=a2F I1 +b2F I2 +c2F I3 (twenty one)

[0137] F3=a3F I1 +b3F I2 +c3F I3 (twenty two)

[0138] Among them, a1, b1, and c1 are relevant weight coefficients of Class I engineering sweet spots, a2, b2, and c2 are relevant weight coefficients of Class II engineering sweet spots, and a3, b3, and c3 are relevant weight coefficients of Class III engineering sweet spots. The weight coefficients can be determined by methods such as fuzzy hierarchical analysis. In this embodiment, a1, b1, and c1 are 0.52, 0.26, and 0.12, respectively; a2, b2, and c2 are 0.46, 0.32, and 0.22, respectively; and a3, b3, and c3 are 0.31, 0.46, and 0.23, respectively.

[0139] S7: Use the data collected on site to conduct post-evaluation of the engineering sweet spot, realize the weight correction of differentiated engineering sweet spots, and determine the boundary map of the on-site engineering sweet spot;

[0140] The weight correction process is as follows: select 80% of the data as sample data to regress and obtain the relevant weight coefficients of Class I, Class II, and Class III engineering sweet spots, and use the remaining 20% ​​of the data as test data. If the regression coefficient meets the conditions, stop the calculation and determine the final weight coefficient.

[0141] Figure 2 This is a schematic diagram of the energy storage coefficient classification. The energy storage coefficient is calculated using formula (10). The main purpose of each period is to determine the type of geological sweet spot, namely the three types of gas layers in the figure.

[0142] Figure 3 This is the interpretation result of natural fracture distribution in tight reservoirs;

[0143] The natural fracture modeling is mainly completed through the following steps: identifying the distribution characteristics of natural fractures through core observation or imaging logging, obtaining the location of fracture development zone through finite element inversion method, and calculating the comprehensive evaluation index of natural fractures, which is mainly completed through formula (19);

[0144] Figure 4 This is the result diagram of the interpretation of the engineering sweet spot of the tight reservoir. This figure is the distribution result of the planar engineering sweet spot calculated by formulas (20)-(22). The advantageous area for fracturing can be selected by combining this figure.

[0145] Figure 5In order to differentiate the interpretation results of engineering sweet spots in tight reservoirs, after eliminating geological factors, three types of sweet spots are further differentiated and classified into engineering sweet spots, where (a) is Class I engineering sweet spot, (b) is Class II engineering sweet spot, and (c) is Class III engineering sweet spot. The compressibility index refers to the compressibility evaluation results corresponding to Class I, II, and III sweet spots calculated according to formulas (20) to (22).

[0146] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for evaluating and determining the boundaries of three-dimensional engineering sweet spots in differentiated tight reservoirs, characterized in that: The steps include: S1: Calculate rock porosity, permeability and water saturation parameters through core description and well logging curves; S2: Use numerical simulation methods to form the distribution field of rock porosity, permeability, and gas saturation, and establish energy storage parameters for geological sweet spot evaluation; S3: Collect open flow parameters on site, classify the open flow on site by clustering method, and divide differentiated geological sweet spots; S4: Construct a three-dimensional geomechanical model of the target reservoir, and calculate the comprehensive evaluation index based on rock brittleness and the comprehensive evaluation index of geostress based on the minimum horizontal principal stress; S5: Construct a natural fracture model for the study area and determine the comprehensive fracture rate I based on the ratio of tensile fractures to shear fractures. c , calculate the comprehensive evaluation index of natural fractures; S6: The engineering sweet spot model is constructed by combining the comprehensive evaluation index based on rock brittleness, the comprehensive evaluation index of ground stress and the comprehensive evaluation index of natural fractures; S7: Use the data collected on site to conduct post-engineering sweet spot evaluation and achieve differentiated engineering sweet spot weight correction; In step S2, according to the porosity, permeability and gas saturation values ​​of the rock, a three-dimensional distribution field of the porosity, permeability and gas saturation values ​​of the rock is obtained by a random modeling method, and the energy storage coefficient Q is calculated using the calculated porosity, permeability and gas saturation values ​​of the rock. p , the formula is as follows: Q P =Φ×S q ×K(10) In the formula, Q p represents the reservoir coefficient, Φ is the porosity, K is the permeability, S q is the gas saturation value; In step S3, after collecting the open flow parameters on site, the actual production of different fracturing layers is determined by production splitting, the production data is statistically analyzed using the K-means clustering method, and a map of open flow and energy storage coefficient is constructed. The boundaries of differentiated geological sweet spots are determined according to the inflection points of the nonlinear curve, and the boundaries of differentiated geological sweet spots are used to divide the geological sweet spots, and the classification of differentiated sweet spots is determined, which is specifically divided into three levels, namely Class I geological sweet spots, Class II geological sweet spots, and Class III geological sweet spots.

2. The differentiated tight reservoir three-dimensional engineering sweet spot evaluation and boundary determination method according to claim 1, characterized in that: In step S1, lithology description is performed based on downhole thin section information and core observation data, and lithology identification is performed in combination with logging data. On the basis of lithology identification, the porosity parameters of the rock are calculated through the logging curve. The porosity parameters are as shown in formula (1): Φ=m1·DEN+m2·DT-m3·RD+m4(1) Among them, Φ represents the porosity of rock, DEN represents density data, DT represents acoustic time difference data, RD represents deep resistivity data, and m1, m2, m3, and m4 represent the empirical coefficients of multiple regression respectively.

3. The method for evaluating and determining the boundary of a three-dimensional engineering sweet spot in a differentiated tight reservoir according to claim 2, characterized in that: In step S1, the permeability is calculated according to the flow characteristics. First, the flow unit index FZI value is calculated by formula (2). The flow unit is defined as a rock reservoir with the same flow characteristics. Then, the correlation between porosity and permeability suitable for different flow units is established, such as formula (3) to formula (5): logFZI=7.087-0.017*GR-2.026*DEN+0.02*DT(2) Class I FZI>3.31, K=0.2798e^(0.4548Φ)(3) Class II 0.98 <FZI<3.31,K=0.0294e^(0.4542Φ)(4) Class III FZI>0.27, K=0.0068e^(0.434Φ)(5) Among them, FZI represents flow unit, GR represents natural gamma value, DT represents acoustic time difference, K represents rock permeability, and Φ represents rock porosity.

4. The method for evaluating and determining the boundary of a three-dimensional engineering sweet spot in a differentiated tight reservoir according to claim 3, characterized in that: In step S1, to determine the gas saturation, it is necessary to first establish a nonlinear Archie water saturation model applicable to different diagenetic phases to calculate the water saturation, and then inversely calculate the gas saturation based on the water saturation; where the nonlinear Archie water saturation is given by: S w +S q =1(9) Among them, S w Indicates water saturation, S q represents gas saturation, m is the pore cementation index, a4 is the lithology coefficient, which is 1, xb and xn are the coefficients of the nonlinear Archie water saturation model, R t Represents the formation resistivity, R w Indicates the resistivity of formation water; F represents the formation factor, dimensionless, R o Indicates the resistivity of 100% water-bearing rock, represents the effective porosity; I represents the resistivity index, which is dimensionless.

5. The method for evaluating and determining the boundary of a three-dimensional engineering sweet spot in a differentiated tight reservoir according to claim 4, characterized in that: In step S4, a one-dimensional geomechanical model consisting of rock strength characteristics and stress distribution is constructed in combination with data from all adjacent wells. Inter-well parameters are obtained using seismic data through lithology control constraints, and three-dimensional rock mechanics field calculations are performed using interpolation methods to obtain a three-dimensional geomechanical model. First, the rock mechanical parameters of a single well are calculated, including Young's modulus, Poisson's ratio, rock tensile strength, and fracture toughness, thereby obtaining a comprehensive evaluation index F based on rock brittleness. I1 : F I1 =aE G +bv G +cS G +dK G (11) Among them, E G , ν G , S G , K G Respectively expressed as: In the formula, E G is the normalized Young's modulus, ν G is the normalized Poisson's ratio, S G is the normalized rock tensile strength, K G is the normalized rock fracture toughness value, a, b, c, d are weight coefficients affecting the brittleness index value, E is the actual rock Young's modulus, E max is the maximum rock Young's modulus, E min is the minimum rock Young's modulus, dimensionless; ν is the actual Poisson's ratio, ν max is the maximum Poisson's ratio, ν min is the minimum Poisson's ratio, dimensionless; S is the actual tensile strength, S max is the maximum tensile strength, S min is the minimum tensile strength, dimensionless; K is the actual rock fracture toughness value, K max is the maximum rock fracture toughness value, K min is the minimum rock fracture toughness value, in GPa.

6. The method for evaluating and determining the boundary of a three-dimensional engineering sweet spot in a differentiated tight reservoir according to claim 5, characterized in that: In step S4, the direction of the horizontal principal stress is predicted by the borehole collapse and drilling-induced tension fractures observed in the imaging logging; Select the combined spring model to calculate the horizontal stress, as shown in the following formula: The combined spring model takes into account the influence of overburden pressure, tectonic stress and pore pressure on the ground stress, and assumes the formation to be a homogeneous and isotropic body. The calculation formula is: Among them, σ H represents the maximum horizontal principal stress, σ h represents the minimum horizontal principal stress, v is the actual Poisson's ratio, σ v is the overburden pressure, α is the Biot coefficient, P P is the formation pore pressure, E S is the static rock Young's modulus, ε H is the tectonic stress coefficient in the direction of the maximum principal stress, ε h is the tectonic stress coefficient in the direction of minimum principal stress; Calculation of the comprehensive evaluation index F of ground stress based on the minimum horizontal principal stress I3 : Among them, F I3 represents the comprehensive evaluation index of geostress, σ hmax Indicates the highest value of the minimum horizontal principal stress in the evaluation layer, σ hmin Indicates the lowest value of the minimum horizontal principal stress in the evaluation layer segment.

7. The method for evaluating and determining the boundary of a three-dimensional engineering sweet spot in a differentiated tight reservoir according to claim 6, characterized in that: In step S5, the comprehensive rupture rate I is determined according to the ratio of tensile rupture cracks to shear rupture cracks. c : In the formula, I c is the comprehensive rupture rate, d1 and d2 are the proportions of observed tensile rupture cracks and shear rupture cracks, respectively, and I t is the tensile rupture coefficient, dimensionless, I n is the shear rupture coefficient, dimensionless; The comprehensive rupture rate I c The method of assigning values ​​to layers is used to import them into the three-dimensional geomechanical model as a constraint condition for controlling the spatial distribution of fractures. Based on the comprehensive fracture index, the fracture density on a single well is used as hard data, and the comprehensive fracture rate is used as a spatial constraint. The sequential indication method is used to randomly simulate the fracture density in layers, delineate and divide the fracture development level area in the reservoir, and calculate the comprehensive evaluation index of natural fractures: Among them, F I2 Represents the comprehensive evaluation index of natural fractures, I cmax It represents the highest comprehensive rupture rate within the evaluation layer interval, I c It represents the comprehensive rupture rate of natural fractures in the evaluation layer interval, I cmin Indicates the minimum comprehensive rupture rate within the evaluation layer interval.

8. The method for evaluating and determining the boundary of a three-dimensional engineering sweet spot in a differentiated tight reservoir according to claim 7, characterized in that: In step S6, the comprehensive calculation formula of the Class I engineering sweet spot, Class II engineering sweet spot, and Class III engineering sweet spot in the engineering sweet spot model is as follows: F1=a1F I1 +b1F I2 +c1F I3 (20) <h2 style=";text-align:left;direction:ltr">F2=a2F<h2 style=";text-align:left;direction:ltr"> I1 <h2 style=";text-align:left;direction:ltr"> +b2F<h2 style=";text-align:left;direction:ltr"> I2 <h2 style=";text-align:left;direction:ltr"> +c2F<h2 style=";text-align:left;direction:ltr"> I3 <h2 style=";text-align:left;direction:ltr"> (21) <h2 style=";text-align:left;direction:ltr">F3=a3F<h2 style=";text-align:left;direction:ltr"> I1 <h2 style=";text-align:left;direction:ltr"> +b3F<h2 style=";text-align:left;direction:ltr"> I2 <h2 style=";text-align:left;direction:ltr"> +c3F<h2 style=";text-align:left;direction:ltr"> I3 <h2 style=";text-align:left;direction:ltr"> (22) Among them, a1, b1, and c1 are the relevant weight coefficients of class I engineering sweet spots, a2, b2, and c2 are the relevant weight coefficients of class II engineering sweet spots, and a3, b3, and c3 are the relevant weight coefficients of class III engineering sweet spots.

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