A method, device and equipment for determining reservoir quality

Abstract

This specification provides a method, device and equipment for determining reservoir quality. According to the formation density of the target well, the porosity of the target well is corrected to obtain the corrected porosity; based on the corrected porosity and the lateral relaxation time of the target well, the first influencing factor of the matrix porosity of the target well is determined; according to the brittleness index and the horizontal biaxial principal stress difference of the target well, the second influencing factor of the target well on geomechanics is determined; according to the normal stress of the fracture surface, the fracture density and the target angle of the target well, the third influencing factor of the fracture of the target well is determined; according to the preset evaluation rules, these multiple influencing factors are comprehensively used to determine the reservoir quality evaluation result for the area where the target well is located. In this way, the reservoir in the area where the target well is located can be comprehensively and effectively evaluated based on multiple dimensions, overcoming the defects of reservoir evaluation based on single factor and single angle, and improving the accuracy of reservoir evaluation.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration and development technology, and in particular to a method, device and equipment for determining reservoir quality. Background Art

[0002] As oil and gas exploration and development continue to deepen, oil and gas exploration has gradually shifted from medium and shallow layers to deep and ultra-deep layers, from conventional oil and gas reservoirs to unconventional oil and gas reservoirs, and from simple to complex fields. Due to the long historical period, the clastic reservoir geology of deep and ultra-deep layers has undergone complex diagenetic evolution and structural transformation under a strong stress environment, showing the characteristics of complex pore structure, large differences in fracture effectiveness, and strong reservoir heterogeneity. This has led to increased difficulty in reservoir evaluation and prediction, which has seriously restricted the effective exploration and development of natural gas.

[0003] In existing methods, reservoir evaluation of clastic rocks in deep and ultra-deep strong stress environments often adopts a single-factor evaluation method. However, the single-factor evaluation method has limitations and easily leads to the problem of low reservoir evaluation accuracy.

[0004] To address the above problems, no effective solution has been proposed yet. Summary of the invention

[0005] This specification provides a method and device for determining reservoir quality, which comprehensively and effectively evaluates the reservoir in the area where the target well is located based on multiple dimensions, obtains reservoir quality results with high reference value and good accuracy, and overcomes the defects of reservoir evaluation based on single factor and single angle.

[0006] This specification provides a method for determining reservoir quality, comprising:

[0007] According to the formation density of the target well, the porosity of the target well is corrected to obtain the corrected porosity; wherein the formation density of the target well is obtained by performing density logging on the target well, and the porosity of the target well is obtained by performing nuclear magnetic resonance logging on the target well;

[0008] Determining a first influencing factor of the target well on matrix porosity based on the corrected porosity and the transverse relaxation time of the target well;

[0009] Determining a second geomechanical influencing factor of the target well according to the brittleness index and the horizontal biaxial principal stress difference of the target well;

[0010] Determine the third influencing factor of the target well on the fracture according to the fracture surface normal stress, fracture density and target angle of the target well; wherein the target angle is the angle between the maximum horizontal principal stress and the natural fracture trend;

[0011] According to preset evaluation rules, based on the first influencing factor, the second influencing factor and the third influencing factor, a reservoir quality evaluation result for the area where the target well is located is determined.

[0012] In one embodiment, before determining the third influencing factor of the target well according to the fracture normal stress, fracture density and target angle of the target well, the method further includes:

[0013] Obtaining the fracture dip angle, overlying formation pressure, maximum horizontal principal stress and minimum horizontal principal stress of the target well;

[0014] Determining a first direction cosine based on the sine value of the target angle and the sine value of the fracture dip angle;

[0015] Determining a second direction cosine based on the cosine value of the target angle and the sine value of the fracture dip angle;

[0016] Determining a third direction cosine based on the cosine value of the crack inclination angle;

[0017] The fracture surface normal stress is determined using the first direction cosines, the second direction cosines, the first direction cosines, the overlying formation pressure, the maximum horizontal principal stress and the minimum horizontal principal stress.

[0018] In one embodiment, determining the fracture surface normal stress by using the first direction cosines, the second direction cosines, the first direction cosines, the overlying formation pressure, the maximum horizontal principal stress and the minimum horizontal principal stress comprises:

[0019] The normal stress of the crack surface is determined according to the following formula:

[0020] σ n = l 2 ×SH max +m 2 ×Sh min +n 2 ×S v ,

[0021] Among them, SH max is the maximum horizontal principal stress, Sh min is the minimum horizontal principal stress, l is the first direction cosine, m is the second direction cosine, n is the third direction cosine, S v is the overlying formation pressure.

[0022] In one embodiment, the porosity of the target well is corrected according to the formation density of the target well to obtain the corrected porosity, including:

[0023] The corrected porosity is determined according to the following formula:

[0024] Φ corrected =Φ NMR ×(1+k×(ρ m -ρ ref )),

[0025] Among them, Φ corrected is the corrected porosity, Φ NMR is the NMR porosity, k is the correction factor, ρ m is the formation density, ρ ref is the reference density value of the target well.

[0026] In one embodiment, determining the reservoir quality evaluation result for the area where the target well is located based on the first influencing factor, the second influencing factor, and the third influencing factor according to the preset evaluation rule includes:

[0027] According to the preset evaluation rule, determining a first weight corresponding to the first influencing factor, a second weight corresponding to the second influencing factor, and a third weight corresponding to the third influencing factor;

[0028] The first weight, the second weight, the third weight, and the first influencing factor, the second influencing factor, and the third influencing factor are used to determine a reservoir quality evaluation result for the area where the target well is located.

[0029] In one embodiment, the preset evaluation rule is a rule constructed based on a genetic algorithm for determining weights corresponding to influencing factors.

[0030] In one embodiment, determining the reservoir quality evaluation result for the area where the target well is located based on the first influencing factor, the second influencing factor, and the third influencing factor according to the preset evaluation rule includes:

[0031] Obtaining the exploration and development requirements corresponding to the area where the target well is located;

[0032] According to the preset evaluation rule, based on the first influencing factor, the second influencing factor and the third influencing factor, an initial reservoir quality evaluation result and a target influencing factor are determined; wherein the target influencing factor is an influencing factor among the first influencing factor, the second influencing factor and the third influencing factor that does not meet the exploration and development requirements corresponding to the area where the target well is located;

[0033] The reservoir quality evaluation result for the area where the target well is located is determined according to the exploration and development requirements, the initial reservoir quality evaluation result and the target influencing factor.

[0034] This specification provides a reservoir quality determination device, comprising:

[0035] A porosity determination module, used to perform correction processing on the porosity of the target well according to the formation density of the target well to obtain the corrected porosity; wherein the formation density of the target well is obtained by performing density logging on the target well, and the porosity of the target well is obtained by performing nuclear magnetic resonance logging on the target well;

[0036] A first influence determination module, configured to determine a first influence factor of the target well on matrix porosity based on the corrected porosity and the transverse relaxation time of the target well;

[0037] A second impact determination module is used to determine a second impact factor of the target well on geomechanics according to the brittleness index and horizontal bidirectional principal stress difference of the target well;

[0038] A third impact determination module determines a third impact factor of the target well on the fracture according to the fracture surface normal stress, fracture density and target angle of the target well; wherein the target angle is the angle between the maximum horizontal principal stress and the natural fracture direction;

[0039] An evaluation and determination module is used to determine a reservoir quality evaluation result for the area where the target well is located based on the first influencing factor, the second influencing factor and the third influencing factor according to a preset evaluation rule.

[0040] The present specification also provides an electronic device, including a processor and a memory for storing processor-executable instructions, wherein the processor implements a reservoir quality determination method when executing the instructions.

[0041] The present specification also provides a computer-readable storage medium having computer instructions stored thereon, and when the instructions are executed, a reservoir quality determination method is implemented.

[0042] A reservoir quality determination method and device provided in the present specification is provided. The porosity of the target well is corrected according to the formation density of the target well to obtain the corrected porosity; wherein the formation density of the target well is determined by density logging of the target well, and the porosity of the target well is determined by nuclear magnetic resonance logging of the target well. Based on the corrected porosity and the lateral relaxation time of the target well, a first influencing factor of matrix pores of the target well is determined; according to the brittleness index and the horizontal biaxial principal stress difference of the target well, a second influencing factor of geomechanics of the target well is determined; according to the normal stress of the fracture surface of the target well, the fracture density and the target angle, a third influencing factor of the fracture of the target well is determined; wherein the target angle is the angle between the maximum horizontal principal stress and the direction of the natural fracture. According to the preset evaluation rules, based on the first influencing factor, the second influencing factor and the third influencing factor, a reservoir quality evaluation result for the area where the target well is located is determined. In this way, based on the relevant parameters affecting the reservoir quality, the influencing factors of the three dimensions of matrix porosity, geomechanics, and fractures are determined respectively, and then according to the preset evaluation rules, these multiple influencing factors are comprehensively utilized to determine the reservoir quality evaluation results for the area where the target well is located. In this way, the reservoir in the area where the target well is located can be comprehensively and effectively evaluated based on multiple dimensions, and reservoir quality results with high reference value and good accuracy can be obtained, which overcomes the defects of reservoir evaluation based on single factor and single angle and improves the accuracy of reservoir evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of this specification, the drawings required for use in the embodiments will be briefly introduced below. The drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 is a flow chart of a reservoir quality determination method provided by an embodiment of this specification;

[0045] Figure 2 is a schematic diagram of a reservoir quality parameter construction method provided by an embodiment of this specification;

[0046] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of this specification;

[0047] Figure 4 It is a schematic diagram of the structure of a reservoir quality determination device provided by an embodiment of this specification;

[0048] Figure 5This is a flow chart of a method for evaluating the effectiveness of a clastic reservoir in a deep-ultra-deep strong stress environment provided by an embodiment of this specification;

[0049] Figure 6 is a schematic diagram of corrected porosity provided by an embodiment of this specification;

[0050] Figure 7 is a schematic diagram of the matrix porosity factor provided by an embodiment of this specification;

[0051] Figure 8 It is a schematic diagram of core testing and rock mechanics parameter analysis provided by an embodiment of this specification;

[0052] Fig. 9 is a schematic diagram of crack parameters provided by an embodiment of this specification;

[0053] Fig.10 It is a schematic diagram of a single-factor and multi-factor coupling parameter accuracy comparison verification diagram provided by an embodiment of the present specification. DETAILED DESCRIPTION

[0054] 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 this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0055] The clastic reservoir geology of deep and ultra-deep layers has undergone complex diagenetic evolution and structural transformation under strong stress environment, showing the characteristics of complex pore structure, large differences in fracture effectiveness, and strong reservoir heterogeneity. This has increased the difficulty of reservoir evaluation and prediction, seriously restricting the effective exploration and development of natural gas. In the past, the evaluation and prediction of clastic reservoir quality were often studied from a single perspective such as sedimentation, diagenesis, fractures or geomechanics. However, in the deep and ultra-deep strong stress environment, the local stress field and natural fractures caused by sedimentation, diagenesis and structure affect the reservoir quality.

[0056] In response to the root cause of the above problems, this specification considers determining the influencing factors of the three dimensions of matrix porosity, geomechanics, and fractures based on the relevant parameters affecting the reservoir quality, and then comprehensively utilizes these multiple influencing factors according to the preset evaluation rules to determine the reservoir quality evaluation results for the area where the target well is located. In this way, the reservoir in the area where the target well is located can be comprehensively and effectively evaluated based on multiple dimensions, and reservoir quality results with high reference value and good accuracy can be obtained, which overcomes the defects of reservoir evaluation based on single factor and single angle, and improves the accuracy of reservoir evaluation.

[0057] See also Figure 1 As shown, the embodiment of this specification provides a reservoir quality determination method, wherein the method is specifically applied to the server side. When implemented specifically, the method may include the following contents:

[0058] S101: According to the formation density of the target well, the porosity of the target well is corrected to obtain the corrected porosity; wherein the formation density of the target well is obtained by performing density logging on the target well, and the porosity of the target well is obtained by performing nuclear magnetic resonance logging on the target well.

[0059] S102: Determine a first influencing factor of the target well on matrix pores based on the corrected porosity and the transverse relaxation time of the target well.

[0060] S103: Determine a second geomechanical influencing factor of the target well according to the brittleness index and the horizontal biaxial principal stress difference of the target well.

[0061] S104: Determine a third influencing factor of the target well on the fracture according to the fracture surface normal stress, fracture density and target angle of the target well; wherein the target angle is the angle between the maximum horizontal principal stress and the natural fracture direction.

[0062] S105: According to a preset evaluation rule, based on the first influencing factor, the second influencing factor and the third influencing factor, a reservoir quality evaluation result for the area where the target well is located is determined.

[0063] The porosity obtained by the above-mentioned nuclear magnetic resonance logging can be the formation porosity obtained by logging the target well through the nuclear magnetic resonance logging technology, which can be used to reflect the volume of all pore spaces in the formation where the target well is located (including the pore volume of free fluid and bound fluid).

[0064] Specifically, the formation porosity of the formation where the target well is located can be obtained by transmitting radio frequency pulses to the formation where the target well is located and measuring the response of hydrogen nuclei.

[0065] The density logging mentioned above may be a logging method for obtaining physical properties of a formation by measuring the density of the formation.

[0066] In addition, when the target well is processed by NMR logging, the transverse relaxation time can also be obtained, which is closely related to the fluid properties and pore structure in the formation. Therefore, the first influencing factor of the target well on the matrix pore can be determined by the corrected porosity and the transverse relaxation time of the target well.

[0067] Among them, the above-mentioned brittleness index can be an important indicator to measure the brittleness of rock materials, and is often used to evaluate the fracturing ability of shale reservoirs. Rocks with a high brittleness index are more likely to form cracks.

[0068] The above-mentioned horizontal biaxial principal stress difference may refer to the difference between the maximum horizontal principal stress and the minimum horizontal principal stress in the formation. The horizontal biaxial principal stress difference is an important parameter affecting hydraulic fracturing, fault sliding and reservoir rupture behavior.

[0069] The above-mentioned normal stress on the crack surface may be the resultant force of the pressure in the three-dimensional direction of the crack, that is, the force perpendicular to the crack surface.

[0070] The above-mentioned crack density may be the distribution of cracks per unit area or per unit volume in the rock.

[0071] Among them, the above-mentioned natural fracture orientation can be used to describe the main direction or trend of fractures in rocks.

[0072] Based on the above embodiment, based on the relevant parameters affecting the reservoir quality, the influencing factors of the three dimensions of matrix porosity, geomechanics, and fractures are determined respectively, and then according to the preset evaluation rules, these multiple influencing factors are comprehensively utilized to determine the reservoir quality evaluation results for the area where the target well is located. In this way, the reservoir in the area where the target well is located can be comprehensively and effectively evaluated based on multiple dimensions, and reservoir quality results with high reference value and good accuracy can be obtained, which overcomes the defects of reservoir evaluation based on single factor and single angle, and improves the accuracy of reservoir evaluation.

[0073] In some embodiments, the first influencing factor of the target well on the matrix porosity may be determined based on the corrected porosity and the transverse relaxation time of the target well in the following manner, including:

[0074] Acquire a preset rock physics model corresponding to the area where the target well is located;

[0075] A first influencing factor of matrix pores of the target well is determined according to the preset rock physics model, the geometric mean of the lateral relaxation time of the target well, and the corrected porosity.

[0076] The preset rock physics model may be a model constructed for the region where the target well is located and used to determine the first influencing factor on matrix pores.

[0077] In some embodiments, the second geomechanical influencing factor of the target well may be determined according to the brittleness index and the horizontal biaxial principal stress difference of the target well in the following manner, including:

[0078] Determine the brittleness index of the target well according to the formation shear wave time difference, the formation compression wave time difference and the formation density obtained by the density logging;

[0079] Based on the imaging logging data of the target well, picking up the well wall collapse direction and the induced fracture direction of the target well;

[0080] Based on the wellbore collapse direction of the target well, determining the direction of the minimum horizontal principal stress, and based on the direction of the induced fracture, determining the direction of the maximum horizontal principal stress;

[0081] Determining the horizontal bidirectional principal stress difference of the target well according to the difference between the maximum horizontal principal stress and the minimum horizontal principal stress of the target well;

[0082] Determining a second geomechanical influencing factor of the target well according to the brittleness index and the horizontal biaxial principal stress difference of the target well;

[0083] Wherein, the brittleness index of the target well is determined according to the formation shear wave time difference, the formation compression wave time difference and the formation density obtained by the density logging, and the specific implementation includes:

[0084] According to the following formula, the Poisson's ratio of the target well is determined based on the formation shear wave time difference and the formation longitudinal wave time difference of the target well:

[0085]

[0086] Where, υ is the Poisson's ratio of the target well, Δt s is the formation shear wave time difference of the target well, Δt p is the formation compressional wave time difference of the target well.

[0087] According to the following formula, the shear modulus of the target well is determined based on the formation shear wave time difference and the formation density of the target well:

[0088]

[0089] Where G is the shear modulus of the target well, ρ b is the formation density.

[0090] According to the following formula, the Young's modulus of the target well is determined based on the shear modulus of the target well, the formation shear wave time difference of the target well, and the formation compression wave time difference:

[0091]

[0092] Wherein, E is the Young's modulus of the target well.

[0093] According to the Young's modulus of the target well, the mean value of the Young's modulus of the target well is determined according to the following formula:

[0094]

[0095] in, is the mean Young's modulus of the target well, E min is the minimum value among the Young's moduli, E max is the maximum value among the Young's moduli.

[0096] According to the following formula, the mean Poisson's ratio of the target well is determined:

[0097]

[0098] in, is the mean Poisson's ratio of the target well, υ max is the minimum value of the Poisson's ratio, υ min is the maximum value among the Poisson's ratios.

[0099] According to the following formula, the brittleness index of the target well is determined based on the average value of Young's modulus and the average value of Poisson's ratio of the target well:

[0100]

[0101] Wherein, I is the brittleness index of the target well.

[0102] According to the difference between the maximum horizontal principal stress and the minimum horizontal principal stress of the target well, the horizontal bi-directional principal stress difference of the target well is determined. Specifically, the method includes:

[0103] The overlying formation pressure of the target well is determined according to the following formula:

[0104]

[0105] Among them, S v is the overlying formation pressure of the target well, TVD is the vertical depth of the target well, ρ b (z) is the formation density of the target well at a depth of z, and g is the gravitational constant.

[0106] According to the following formula, the pore pressure of the target well is determined:

[0107]

[0108] Among them, P p is the pore pressure of the target well, P pn is the normal compaction pore pressure of the target well, α is the effective stress coefficient of the target well, Δt0 is the mudstone logging acoustic wave time difference of the calculation point of the target well, Δt n is the acoustic time difference of the normal compaction trend line of mudstone corresponding to the calculation point of the target well, and n is the compaction correction coefficient of the target well.

[0109] According to the following formula, the maximum horizontal principal stress of the target well is determined:

[0110]

[0111] Among them, SH max is the maximum horizontal principal stress of the target well, ε H is the maximum horizontal principal stress correlation coefficient, ε h is the minimum horizontal principal stress correlation coefficient.

[0112] According to the following formula, the minimum horizontal principal stress of the target well is determined:

[0113]

[0114] Among them, Sh min is the minimum horizontal principal stress of the target well.

[0115] According to the following formula, the horizontal bidirectional principal stress difference of the target well is determined:

[0116] Δσ=SH max -Sh min ,

[0117] Wherein, Δσ is the horizontal bidirectional principal stress difference of the target well.

[0118] In some embodiments, the third influencing factor of the target well on the fracture may be determined according to the fracture surface normal stress, fracture density and target angle of the target well in the following manner, including:

[0119] The crack density is determined according to the following formula:

[0120]

[0121] Wherein, FVDC is the fracture density of the target well, n is the total number of fractures in the target well, L is the fracture length of the well section of the target well, and Li is the length of the i-th fracture of the target well.

[0122] The target angle is the angle between the maximum horizontal principal stress and the natural fracture direction. Specifically, the Kriging method can be used to calculate the angle between the maximum horizontal principal stress and the natural fracture direction in the target well depth section.

[0123] In some embodiments, before determining the third influencing factor of the target well according to the fracture normal stress, fracture density and target angle of the target well, the method may further include the following when implemented:

[0124] S1: Obtaining the fracture dip angle, overlying formation pressure, maximum horizontal principal stress and minimum horizontal principal stress of the target well.

[0125] S2: Determine a first direction cosine based on the sine value of the target angle and the sine value of the fracture dip angle.

[0126] S3: Determine a second direction cosine based on the cosine value of the target angle and the sine value of the crack dip angle.

[0127] S4: Determine the third direction cosine based on the cosine value of the crack inclination angle.

[0128] S5: Determine the normal stress of the fracture surface by using the first direction cosines, the second direction cosines, the first direction cosines, the overlying formation pressure, the maximum horizontal principal stress and the minimum horizontal principal stress.

[0129] In some embodiments, the method of determining the fracture surface normal stress by using the first direction cosines, the second direction cosines, the first direction cosines, the overlying formation pressure, the maximum horizontal principal stress and the minimum horizontal principal stress may further include the following contents when the method is specifically implemented:

[0130] The first direction cosine is determined according to the following formula:

[0131] l = sinθ × sin(dip),

[0132] Wherein, θ is the angle between the maximum horizontal principal stress and the natural fracture direction, and dip is the fracture dip of the target well. The second direction cosine is determined according to the following formula:

[0133] m=cosθ×sin(dip),

[0134] The third direction cosine is determined according to the following formula:

[0135] n=cos(dip),

[0136] The normal stress of the crack surface is determined according to the following formula:

[0137] σ n = l 2 ×SH max +m 2 ×Sh min +n 2 ×S v ,

[0138] Among them, SH max is the maximum horizontal principal stress, Sh min is the minimum horizontal principal stress, l is the first direction cosine, m is the second direction cosine, n is the third direction cosine, S v is the overlying formation pressure.

[0139] In some embodiments, the porosity of the target well is corrected according to the formation density of the target well to obtain the corrected porosity. When the method is specifically implemented, the following contents may also be included:

[0140] The corrected porosity is determined according to the following formula:

[0141] Φ corrected =Φ NMR ×(1+k×(ρ m -ρ ref )),

[0142] Among them, Φ corrected is the corrected porosity, Φ NMR is the NMR porosity, k is the correction factor, ρ m is the formation density, ρ ref is the reference density value of the target well.

[0143] In some embodiments, the method may further include the following contents when determining the reservoir quality evaluation result for the area where the target well is located based on the first influencing factor, the second influencing factor, and the third influencing factor according to the preset evaluation rule:

[0144] S1: According to the preset evaluation rule, determine a first weight corresponding to the first influencing factor, a second weight corresponding to the second influencing factor, and a third weight corresponding to the third influencing factor.

[0145] S2: Determine a reservoir quality evaluation result for the area where the target well is located by using the first weight, the second weight, the third weight, and the first influencing factor, the second influencing factor, and the third influencing factor.

[0146] In some embodiments, the first weight, the second weight, the third weight, and the first influencing factor, the second influencing factor, and the third influencing factor are used to determine a reservoir quality evaluation result for the area where the target well is located. Specifically, the method includes:

[0147] The reservoir quality evaluation result of the target well area is determined according to the following formula:

[0148] Q 储层 =β1ω1+β2ω2+β3ω3,

[0149] Where: Q 储层 is the reservoir quality evaluation result of the area where the target well is located, β1 is the first weight, ω1 is the first influencing factor, β2 is the second weight, ω2 is the second influencing factor, β3 is the third weight, and ω3 is the third influencing factor.

[0150] In some embodiments, the preset evaluation rule is a rule based on a genetic algorithm for determining the weight corresponding to the influencing factor. When the method is implemented, Figure 2 As shown, the following may also be included:

[0151] Based on the influencing factors corresponding to matrix pores, geomechanics and fractures (i.e., the first influencing factor, the second influencing factor and the third influencing factor), the initial population is generated. After multiple iterative calculations, crossover, mutation and optimization evaluation are performed, and then the convergence of the selected individuals is checked. If the convergence standard is met, the best optimization scheme and the weights of each parameter are output. If the convergence standard is not met, the fitness function is used for adjustment, and crossover, mutation and optimization evaluation are continued until the individuals that meet the convergence standard are screened out, that is, the weights corresponding to each influencing factor are obtained.

[0152] In this way, the above formula constructed according to the weights corresponding to the influencing factors determined by the genetic algorithm can improve the accuracy and reliability of reservoir evaluation.

[0153] Specifically, for example, based on the influencing factors corresponding to matrix porosity, geomechanics, and fractures, multiple weight vectors are randomly generated, such as ([0.1, 0.3, 0.5]), ([0.2, 0.4, 0.1]), etc., and the performance of each weight vector on the evaluation function, such as the accuracy or error of a model, is calculated. According to the fitness, an excellent weight vector is selected to proceed to the next step, and the selected weight vectors are paired and crossed, such as ([0.1, 0.3, 0.5]) and ([0.2, 0.4, 0.1]) are crossed to obtain new weight vectors ([0.1, 0.4, 0.1]) and ([0.2, 0.3, 0.5]), and some weight vectors are randomly adjusted, such as the first element of ([0.1, 0.4, 0.1]) is mutated to 0.15 to obtain ([0.15, 0.4, 0.1]), and the above steps are repeated until the best weight vector is found or the maximum number of iterations is reached. Through such an evolutionary process, the genetic algorithm can gradually optimize the weights so that it can achieve the best performance on the objective evaluation function.

[0154] In some embodiments, the method may further include the following contents when determining the reservoir quality evaluation result for the area where the target well is located based on the first influencing factor, the second influencing factor, and the third influencing factor according to the preset evaluation rule:

[0155] S1: Obtaining the exploration and development requirements corresponding to the area where the target well is located.

[0156] S2: According to the preset evaluation rules, based on the first influencing factor, the second influencing factor and the third influencing factor, determine the initial reservoir quality evaluation result and the target influencing factor; wherein the target influencing factor is the influencing factor among the first influencing factor, the second influencing factor and the third influencing factor that does not meet the exploration and development needs corresponding to the area where the target well is located.

[0157] S3: Determine the reservoir quality evaluation result for the area where the target well is located according to the exploration and development requirements, the initial reservoir quality evaluation result and the target influencing factor.

[0158] In some embodiments, according to the preset evaluation rule, based on the first influencing factor, the second influencing factor and the third influencing factor, an initial reservoir quality evaluation result and a target influencing factor are determined, and when specifically implemented, the method includes:

[0159] According to the preset evaluation rules, based on the first influencing factor, the second influencing factor and the third influencing factor, a population containing several initial reservoir evaluation results and target influencing factor combinations is first generated, and the evaluation quality of each combination is evaluated by the fitness function. Then, according to the fitness value, a combination with excellent performance is selected for crossover and mutation operations to generate a new combination. This process is continuously iterated, and the reservoir evaluation results and target influencing factors are optimized through multiple generations of evolution, and finally the combination with the highest fitness is output as the optimal solution.

[0160] In some embodiments, the reservoir quality evaluation result for the area where the target well is located is determined according to the exploration and development requirements, the initial reservoir quality evaluation result and the target influencing factor. When implemented specifically, the method includes:

[0161] Determine the reservoir quality category of the area where the target well is located based on a preset classification rule and the initial reservoir quality evaluation result;

[0162] The reservoir quality evaluation result for the area where the target well is located is determined according to the exploration and development requirements, the reservoir quality category of the area where the target well is located, and the target influencing factor.

[0163] Specifically, for example, the initial reservoir quality evaluation result may include a reservoir quality score, and the reservoir quality score may be greater than 0 and less than or equal to 1, that is, the larger the reservoir quality score of the area where the target well is located, the better the reservoir quality of the area where the target well is located.

[0164] The preset classification rule can determine the reservoir quality category of the area where the target well is located according to the score range of the reservoir quality score. For example, when the initial reservoir quality evaluation result is in the score range 1 (such as [0.7-1]), the reservoir quality category of the area where the target well is located can be Class I reservoir quality; when the initial reservoir quality evaluation result is in the score range 2 (such as [0.5-0.7), the reservoir quality category of the area where the target well is located can be Class II reservoir quality; when the initial reservoir quality evaluation result is in the score range 3 (such as [0.3-0.5)), the reservoir quality category of the area where the target well is located can be Class III reservoir quality; when the initial reservoir quality evaluation result is in the score range 4 (such as (0-0.3)), the reservoir quality category of the area where the target well is located can be Class IV reservoir quality.

[0165] In addition, since different regions may have different exploration and development requirements for matrix porosity, geomechanics and fractures, the initial reservoir quality evaluation results may not meet the actual exploration and development requirements of the target well region. Therefore, based on the initial reservoir quality evaluation results, the reservoir quality evaluation results of the target well region can be determined by combining the first influencing factor, the second influencing factor and the target influencing factor among the third influencing factors that do not meet the exploration and development requirements corresponding to the target well region.

[0166] Specifically, for example: assuming that the initial reservoir quality evaluation result corresponding to the area where the target well is located is Class III reservoir quality, and the first influencing factor and the third influencing factor corresponding to the area where the target well is located both meet the exploration and development requirements corresponding to the area where the target well is located, and only the second influencing factor does not meet the exploration and development requirements corresponding to the area where the target well is located. When the area where the target well is located has high requirements for the exploration and development of matrix pores and fractures and low requirements for the exploration and development of geomechanics, it can be determined that the reservoir quality evaluation result of the area where the target well is located is good. When the area where the target well is located has high requirements for the exploration and development of geomechanics, it can be determined that the reservoir quality evaluation result of the area where the target well is located is poor.

[0167] In this way, based on the initial reservoir quality evaluation results and combined with the target influencing factors, the reservoir quality evaluation results of the target well area can be determined in a targeted manner, which can improve the accuracy of determining the reservoir quality evaluation results of the target well area.

[0168] As can be seen from the above, a reservoir quality determination method provided in an embodiment of the present specification obtains a corrected porosity by correcting the porosity of the target well according to the formation density of the target well; wherein the formation density of the target well is obtained by performing density logging on the target well, and the porosity of the target well is obtained by performing nuclear magnetic resonance logging on the target well. Based on the corrected porosity and the lateral relaxation time of the target well, a first influencing factor of the matrix pores of the target well is determined; based on the brittleness index and the horizontal biaxial principal stress difference of the target well, a second influencing factor of the geomechanics of the target well is determined; based on the normal stress of the fracture surface of the target well, the fracture density and the target angle, a third influencing factor of the fracture of the target well is determined; wherein the target angle is the angle between the maximum horizontal principal stress and the direction of the natural fracture; based on the preset evaluation rules, based on the first influencing factor, the second influencing factor and the third influencing factor, a reservoir quality evaluation result for the area where the target well is located is determined. In this way, based on the relevant parameters affecting the reservoir quality, the influencing factors of the three dimensions of matrix porosity, geomechanics, and fractures are determined respectively, and then according to the preset evaluation rules, these multiple influencing factors are comprehensively utilized to determine the reservoir quality evaluation results for the area where the target well is located. In this way, the reservoir in the area where the target well is located can be comprehensively and effectively evaluated based on multiple dimensions, and reservoir quality results with high reference value and good accuracy can be obtained, which overcomes the defects of reservoir evaluation based on single factor and single angle and improves the accuracy of reservoir evaluation.

[0169] See also Figure 3 As shown, the embodiment of this specification also provides a specific electronic device, wherein the electronic device includes a network communication port 301, a processor 302 and a memory 303, and the above structures are connected through internal cables so that each structure can perform specific data interaction.

[0170] Among them, the network communication port 301 can be specifically used to correct the porosity of the target well according to the formation density of the target well to obtain the corrected porosity; wherein the formation density of the target well is determined by density logging of the target well, and the porosity of the target well is determined by nuclear magnetic resonance logging of the target well.

[0171] The processor 302 can be specifically used to determine a first influencing factor of the target well regarding matrix porosity based on the corrected porosity and the lateral relaxation time of the target well; determine a second influencing factor of the target well regarding geomechanics based on the brittleness index and the horizontal biaxial principal stress difference of the target well; determine a third influencing factor regarding fractures of the target well based on the fracture surface normal stress, fracture density and target angle of the target well; wherein the target angle is the angle between the maximum horizontal principal stress and the direction of the natural fracture; and determine a reservoir quality evaluation result for the area where the target well is located based on the first influencing factor, the second influencing factor and the third influencing factor according to preset evaluation rules.

[0172] The memory 303 may be specifically used to store corresponding instruction programs.

[0173] Based on the above method, the relevant structural performance of the electronic equipment can be effectively utilized, the data processing speed of the electronic equipment can be improved, and the reservoir quality determination method can be efficiently implemented.

[0174] In this embodiment, the network communication port 301 can be a virtual port that is bound to different communication protocols so that different data can be sent or received. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. In addition, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM, CDMA, etc.; it can also be a Wifi chip; it can also be a Bluetooth chip.

[0175] In this embodiment, the processor 302 may be implemented in any appropriate manner. For example, the processor may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) executable by the (micro)processor, a logic gate, a switch, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, etc. This specification does not limit this.

[0176] In this embodiment, the memory 303 may include multiple levels. In a digital system, anything that can store binary data can be a memory; in an integrated circuit, a circuit with a storage function but no physical form is also called a memory, such as RAM, FIFO, etc.; in a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0177] The embodiment of the present specification also provides a computer-readable storage medium based on the above-mentioned reservoir quality determination method, wherein the computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed, the following are achieved: according to the formation density of the target well, the porosity of the target well is corrected to obtain the corrected porosity; wherein the formation density of the target well is obtained by performing density logging on the target well, and the porosity of the target well is obtained by performing nuclear magnetic resonance logging on the target well; based on the corrected porosity and the transverse relaxation time of the target well, the target well is determined. The first influencing factor of the target well on matrix porosity is determined; according to the brittleness index and the horizontal biaxial principal stress difference of the target well, the second influencing factor of the target well on geomechanics is determined; according to the normal stress of the fracture surface, the fracture density and the target angle of the target well, the third influencing factor of the target well on fracture is determined; wherein the target angle is the angle between the maximum horizontal principal stress and the direction of the natural fracture; according to the preset evaluation rules, based on the first influencing factor, the second influencing factor and the third influencing factor, the reservoir quality evaluation result for the area where the target well is located is determined.

[0178] In this embodiment, the storage medium includes, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a cache, a hard disk (HDD), or a memory card. The memory may be used to store computer program instructions. The network communication unit may be an interface for network connection communication set in accordance with the standard specified by the communication protocol.

[0179] In this embodiment, the functions and effects specifically implemented by the program instructions stored in the computer-readable storage medium can be explained in comparison with other implementations and will not be described in detail here.

[0180] See also Figure 4 At the software level, the embodiments of this specification also provide a reservoir quality determination device, which may specifically include the following structural modules:

[0181] The porosity determination module 401 is used to perform correction processing on the porosity of the target well according to the formation density of the target well to obtain the corrected porosity; wherein the formation density of the target well is obtained by performing density logging on the target well, and the porosity of the target well is obtained by performing nuclear magnetic resonance logging on the target well;

[0182] A first impact determination module 402, configured to determine a first impact factor of the target well on matrix porosity based on the corrected porosity and the transverse relaxation time of the target well;

[0183] A second impact determination module 403 is used to determine a second impact factor of the target well on geomechanics according to the brittleness index and horizontal biaxial principal stress difference of the target well;

[0184] The third impact determination module 404 determines a third impact factor on the fracture of the target well according to the fracture surface normal stress, fracture density and target angle of the target well; wherein the target angle is the angle between the maximum horizontal principal stress and the natural fracture trend;

[0185] The evaluation determination module 405 is used to determine the reservoir quality evaluation result for the area where the target well is located based on the first influencing factor, the second influencing factor and the third influencing factor according to a preset evaluation rule.

[0186] In some embodiments, before the third impact determination module 404, during specific implementation, the fracture dip, overlying formation pressure, maximum horizontal principal stress and minimum horizontal principal stress of the target well are obtained; the first direction cosine is determined based on the sine value of the target angle and the sine value of the fracture dip; the second direction cosine is determined based on the cosine value of the target angle and the sine value of the fracture dip; the third direction cosine is determined based on the cosine value of the fracture dip; a normal stress determination module is used to determine the fracture surface normal stress using the first direction cosine, the second direction cosine, the first direction cosine, the overlying formation pressure, the maximum horizontal principal stress and the minimum horizontal principal stress.

[0187] In some embodiments, when the normal stress determination module is implemented, the normal stress of the crack surface is determined according to the following formula:

[0188] σ n = l 2 ×SH max +m 2 ×Sh min +n 2 ×S v ,

[0189] Among them, SH max is the maximum horizontal principal stress, Sh min is the minimum horizontal principal stress, l is the first direction cosine, m is the second direction cosine, n is the third direction cosine, S v is the overlying formation pressure.

[0190] In some embodiments, when the porosity determination module 401 is implemented, the corrected porosity is determined according to the following formula:

[0191] Φ corrected =Φ NMR ×(1+k×(ρ m -ρ ref )),

[0192] Among them, Φ corrected is the corrected porosity, Φ NMR is the NMR porosity, k is the correction factor, ρ m is the formation density, ρ ref is the reference density value of the target well.

[0193] In some embodiments, when the evaluation determination module 405 is specifically implemented, according to the preset evaluation rule, a first weight corresponding to the first influencing factor, a second weight corresponding to the second influencing factor, and a third weight corresponding to the third influencing factor are determined; the first weight, the second weight, the third weight, and the first influencing factor, the second influencing factor, and the third influencing factor are used to determine the reservoir quality evaluation result for the area where the target well is located;

[0194] In some embodiments, the preset evaluation rule is a rule constructed based on a genetic algorithm for determining the weight corresponding to the influencing factor.

[0195] In some embodiments, when the above-mentioned evaluation and determination module 405 is implemented, according to the preset evaluation rules, based on the first influencing factor, the second influencing factor and the third influencing factor, an initial reservoir quality evaluation result is determined, as well as a target influencing factor among the first influencing factor, the second influencing factor and the third influencing factor that does not meet the exploration and development needs corresponding to the area where the target well is located; the exploration and development needs corresponding to the area where the target well is located are obtained, and based on the exploration and development needs, the initial reservoir quality evaluation result and the target influencing factor, the reservoir quality evaluation result for the area where the target well is located is determined.

[0196] It should be noted that the units, devices or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described separately by functions divided into various modules. Of course, when implementing this specification, the functions of each module can be implemented in the same or more software and / or hardware, or the modules that implement the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0197] As can be seen from the above, a reservoir quality determination device provided in an embodiment of the present specification determines the influencing factors of the three dimensions of matrix porosity, geomechanics, and fractures based on the relevant parameters affecting the reservoir quality, and then determines the reservoir quality evaluation result for the area where the target well is located based on the preset evaluation rules and comprehensively utilizes these multiple influencing factors. In this way, the reservoir in the area where the target well is located can be comprehensively and effectively evaluated based on multiple dimensions, and a reservoir quality result with high reference value and good accuracy can be obtained, which overcomes the defects of reservoir evaluation based on single factor and single angle, and improves the accuracy of reservoir evaluation.

[0198] In a specific scenario example, a reservoir quality determination method and device provided in this specification can be applied to comprehensively and effectively evaluate the reservoir in the area where the target well is located based on multiple dimensions, and obtain a reservoir quality result with high reference value and good accuracy, thus overcoming the defects of reservoir evaluation based on single factor and single angle. The specific implementation process may include the following contents, see Figure 5 shown.

[0199] Step 1: Due to the influence of fluid density and hydrogen index, the porosity calculated by the density theory formula is greater than the actual porosity, while the porosity calculated by nuclear magnetic resonance is less than the actual porosity. Therefore, the porosity obtained by nuclear magnetic resonance logging can be corrected by using the formation density obtained by density logging to obtain the corrected porosity; refer to Figure 6 As shown in the figure, the calculation accuracy of the corrected porosity is significantly better than the formation density obtained by density logging and the porosity obtained by nuclear magnetic resonance logging.

[0200] Step 2: To associate discrete geological data with continuous logging data, the pore structure parameters of core samples can be obtained through high-pressure mercury injection experiments to construct a matrix pore factor model. The matrix pore factor model can then be calibrated with the logging data to evaluate the accuracy of the matrix pore factor model. If the matrix pore factor model is inaccurate, it can be adjusted and optimized through data resampling, model correction, multivariate regression or machine learning, cross-validation, and joint inversion to ultimately ensure the reliability and practicality of the matrix pore factor model.

[0201] See also Figure 7 As shown in the figure, the calculated first influencing factor of matrix porosity is consistent with the experimental parameters. It can be seen from the figure that the matrix porosity factor of the gas layer is greater than that of the poor gas layer; the wellbore expansion makes the porosity measured by nuclear magnetic resonance calculated to be too large, but the first influencing factor of matrix porosity is consistent with the actual situation in distinguishing the reservoir, indicating that the constructed first influencing factor of matrix porosity has a good practical application effect.

[0202] Step 3: Obtain the rock mechanics parameters and geostress parameters of the target well, wherein the rock mechanics parameters of the target well include Young's modulus, Poisson's ratio, and shear modulus, and the geostress parameters of the target well include pore pressure, overburden formation pressure, maximum horizontal principal stress, and minimum horizontal principal stress. Figure 8 As shown in the figure, the results of core testing and rock mechanics parameter analysis show the lithology of the mudstone section and the coring section in layers, and the comparison curve on the right shows the changes in parameters such as gamma rays, resistivity, porosity, acoustic time difference and horizontal stress of the formation. These data can be used to evaluate formation stability and reservoir characteristics.

[0203] Based on the rock mechanics parameters and ground stress parameters of the target well, the horizontal biaxial principal stress difference and the brittleness index can be determined, and based on the horizontal biaxial principal stress difference and the brittleness index, the second influencing factor on geomechanics can be constructed.

[0204] Step 4: Based on the imaging logging data of the target well, pick up the wellbore collapse direction and the induced fracture direction of the target well; based on the wellbore collapse direction of the target well, determine the direction of the minimum horizontal principal stress, and based on the direction of the induced fracture, determine the direction of the maximum horizontal principal stress. Fig. 9 shown.

[0205] Step 5: Determine the normal stress of the fracture surface, the target angle and the fracture density in combination with the ground stress, so as to construct the third influencing factor about the fracture according to the normal stress of the fracture surface, the target angle and the fracture density. The target angle can be the angle between the maximum horizontal principal stress of the target well depth section calculated by the Kriging method and the natural fracture direction, see Fig. 9As shown in Figure 2, the larger the normal stress of the crack, the worse the corresponding crack effectiveness.

[0206] Step 6: The first influencing factor of matrix porosity, the second influencing factor of geomechanics, and the third influencing factor of fractures are normalized and converted into a unified proportional range to eliminate the dimensional differences between different variables. Then, based on the genetic algorithm, an initialization population is generated, and after multiple iterations, crossovers, mutations, and optimization evaluations, a convergence check is finally achieved, and the weights corresponding to the optimal influencing factors are output. Based on the first influencing factor, the second influencing factor, the third influencing factor, and the weights corresponding to each influencing factor, the reservoir quality evaluation result for the target well area is determined.

[0207] For details, see Fig.10 As shown in the figure, through the actual precision effect comparison chart of single factor and multi-factor coupling in a certain section of the test oil layer, it can be seen that based on multiple dimensions, the reservoir in the area where the target well is located can be comprehensively and effectively evaluated, and the reservoir quality results with high reference value and good accuracy can be obtained.

[0208] Although the present specification provides method operation steps as described in the embodiments or flow charts, more or less operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way of executing the order of many steps, and does not represent a unique execution order. When the device or client product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiments or the drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such a process, method, product or device. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. The first, second, etc. words are used to represent the name, and do not represent any particular order.

[0209] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0210] Through the description of the above embodiments, it can be known that those skilled in the art can clearly understand that the present specification can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present specification can essentially be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in each embodiment of the present specification or some parts of the embodiments.

[0211] Although the present specification is described through embodiments, those skilled in the art will appreciate that there are many modifications and changes to the present specification without departing from the spirit of the present specification, and it is intended that the appended claims include these modifications and changes without departing from the spirit of the present specification.

Claims

1. A method for determining reservoir quality, characterized in that: include: According to the formation density of the target well, the porosity of the target well is corrected to obtain the corrected porosity; wherein the formation density of the target well is obtained by performing density logging on the target well, and the porosity of the target well is obtained by performing nuclear magnetic resonance logging on the target well; Determining a first influencing factor of the target well on matrix porosity based on the corrected porosity and the transverse relaxation time of the target well; Determining a second geomechanical influencing factor of the target well according to the brittleness index and the horizontal biaxial principal stress difference of the target well; According to the fracture surface normal stress, fracture density and target angle of the target well, the third influencing factor of the fracture of the target well is determined; wherein the target angle is the angle between the maximum horizontal principal stress and the natural fracture direction; the fracture surface normal stress is determined as follows: Obtaining the fracture dip angle, overlying formation pressure, maximum horizontal principal stress and minimum horizontal principal stress of the target well; Determining a first direction cosine based on the sine value of the target angle and the sine value of the fracture dip angle; Determining a second direction cosine based on the cosine value of the target angle and the sine value of the fracture dip angle; Determining a third direction cosine based on the cosine value of the crack inclination angle; Determine the fracture surface normal stress by using the first direction cosine, the second direction cosine, the third direction cosine, the overlying formation pressure, the maximum horizontal principal stress and the minimum horizontal principal stress; According to the rule for determining the weights corresponding to the influencing factors constructed by the genetic algorithm, the reservoir quality evaluation result for the area where the target well is located is determined based on the first influencing factor, the second influencing factor and the third influencing factor.

2. The method according to claim 1, characterized in that The method of determining the fracture surface normal stress by using the first direction cosine, the second direction cosine, the third direction cosine, the overlying formation pressure, the maximum horizontal principal stress and the minimum horizontal principal stress comprises: The normal stress of the crack surface is determined according to the following formula: s n =l 2 ×SH max +m 2 ×Sh min +n 2 ×S v , Among them, SH max is the maximum horizontal principal stress, Sh min is the minimum horizontal principal stress, l is the first direction cosine, m is the second direction cosine, n is the third direction cosine, S v is the overlying formation pressure.

3. The method according to claim 1, characterized in that The step of correcting the porosity of the target well according to the formation density of the target well to obtain the corrected porosity includes: The corrected porosity is determined according to the following formula: F corrected =Φ NMR ×(1+k×(ρ m -r ref )), Among them, Φ corrected is the corrected porosity, Φ NMR is the NMR porosity, k is the correction factor, ρ m is the formation density, ρ ref is the reference density value of the target well.

4. The method according to claim 1, characterized in that The rule for determining the weights corresponding to the influencing factors constructed according to the genetic algorithm determines the reservoir quality evaluation result for the area where the target well is located based on the first influencing factor, the second influencing factor and the third influencing factor, including: Determine, according to the rule for determining weights corresponding to influencing factors constructed by the genetic algorithm, a first weight corresponding to the first influencing factor, a second weight corresponding to the second influencing factor, and a third weight corresponding to the third influencing factor; The first weight, the second weight, the third weight, and the first influencing factor, the second influencing factor, and the third influencing factor are used to determine a reservoir quality evaluation result for the area where the target well is located.

5. The method according to claim 1, characterized in that The rule for determining the weights corresponding to the influencing factors constructed according to the genetic algorithm determines the reservoir quality evaluation result for the area where the target well is located based on the first influencing factor, the second influencing factor and the third influencing factor, including: Obtaining the exploration and development requirements corresponding to the area where the target well is located; According to the rule for determining the weights corresponding to the influencing factors constructed by the genetic algorithm, an initial reservoir quality evaluation result and a target influencing factor are determined based on the first influencing factor, the second influencing factor and the third influencing factor; wherein the target influencing factor is an influencing factor among the first influencing factor, the second influencing factor and the third influencing factor that does not meet the exploration and development requirements corresponding to the area where the target well is located; The reservoir quality evaluation result for the area where the target well is located is determined according to the exploration and development requirements, the initial reservoir quality evaluation result and the target influencing factor.

6. A reservoir quality determination device, characterized in that: include: A porosity determination module, used to perform correction processing on the porosity of the target well according to the formation density of the target well to obtain the corrected porosity; wherein the formation density of the target well is obtained by performing density logging on the target well, and the porosity of the target well is obtained by performing nuclear magnetic resonance logging on the target well; A first influence determination module, configured to determine a first influence factor of the target well on matrix porosity based on the corrected porosity and the transverse relaxation time of the target well; A second impact determination module is used to determine a second impact factor of the target well on geomechanics according to the brittleness index and horizontal bidirectional principal stress difference of the target well; The third impact determination module determines the third impact factor of the target well on the fracture according to the fracture surface normal stress, fracture density and target angle of the target well; wherein the target angle is the angle between the maximum horizontal principal stress and the natural fracture direction; the fracture surface normal stress is determined as follows: A data acquisition module, used to acquire the fracture dip, overlying formation pressure, maximum horizontal principal stress and minimum horizontal principal stress of the target well; A first cosine determination module, configured to determine a first direction cosine based on a sine value of a target angle and a sine value of a crack dip angle; A second cosine determination module, configured to determine a second direction cosine based on a cosine value of the target angle and a sine value of the crack dip; A third cosine determination module, configured to determine a third direction cosine based on the cosine value of the crack inclination; A normal stress determination module is used to determine the normal stress of the fracture surface by using the first direction cosine, the second direction cosine, the third direction cosine, the overlying formation pressure, the maximum horizontal principal stress and the minimum horizontal principal stress; an evaluation determination module is used to determine the reservoir quality evaluation result for the area where the target well is located based on the first influencing factor, the second influencing factor and the third influencing factor according to the rules for determining the weights corresponding to the influencing factors constructed by the genetic algorithm.

7. An electronic device, characterized in that: The invention comprises a processor and a memory for storing instructions executable by the processor, wherein the steps of the reservoir quality determination method according to any one of claims 1 to 5 are implemented when the processor executes the instructions.

8. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the instructions are executed by a processor, the steps of the reservoir quality determination method according to any one of claims 1 to 5 are implemented.

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