A quantitative evaluation method for trap-destructive faults in oil and gas reservoirs

Abstract

The present invention relates to a quantitative evaluation method for oil and gas reservoir closure damage faults, and belongs to the technical field of oil and gas exploration. The present invention first determines the relationship between the activity time of the fault and the oil and gas accumulation period. When the fault activity time is earlier than the oil and gas accumulation period, the displacement pressure at each sampling point of the fault is determined by the core data of the drilled trap damage fault, and the displacement pressure, mud content and fault burial depth at each sampling point of the drilled trap damage fault are used to establish a nonlinear relationship between the displacement pressure and the mud content and burial depth, and the nonlinear relationship is used to determine the corresponding displacement pressure at each sampling point of the fault to be evaluated according to the burial depth and mud content of the fault to be evaluated. The present invention fully considers the early and late relationship between the fault activity time and the oil and gas accumulation period, and at the same time considers the nonlinear relationship between the fault rock displacement pressure and the fault burial depth and the fault mud content, thereby improving the accuracy of the evaluation of oil and gas reservoir closure damage faults.

Description

Technical Field

[0001] The invention relates to a quantitative evaluation method for oil and gas reservoir trap damage faults, belonging to the technical field of oil and gas exploration. Background Art

[0002] The fault that destroys the oil and gas reservoir trap refers to the fault that has a destructive effect on the already formed oil and gas reservoir. Oil and gas are all stored in underground traps. After years of oil exploration, traps of different types and sizes have been identified by means of drilling, seismic and other means. Most of the traps that remain intact and not destroyed by faults have been drilled, and a considerable number of the remaining traps overlap with faults. For these traps overlapping with faults, the effectiveness of the trap is a prerequisite for the success of oil and gas exploration. Among the factors that determine the effectiveness of the trap, the quality of the cap rock conditions is particularly important. In addition to being affected by its own original sedimentary characteristics, the quality of the cap rock conditions is also restricted by the degree of fault damage. The cap rock with a large original sedimentary thickness, high displacement pressure, and strong sealing ability for oil and gas, due to the subsequent fault damage, its sealing quality has deteriorated, and it may even lose its ability to seal oil and gas. Therefore, for traps overlapping with faults, the impact of fault activity on its sealing ability is greater than that of original sedimentation on its sealing ability.

[0003] Many scholars have discussed the evaluation methods of the sealing capacity of the caprock, but they mainly evaluate the sealing capacity of the original sedimentation of the caprock. Even if the destructive effect of the fault on the caprock is taken into account, there is a lack of quantitative research and evaluation methods. In the article "Evaluation Method of the Sealing Capacity of the Caprock after Fault Destruction", Zhang Liguo proposed a method of using the thickness of the overlying strata and the stratum density to calculate the diagenetic pressure of the fault rock and then use this as a basis to evaluate the sealing capacity of the fault. This method ignores the matching relationship between the reservoir formation period and the fault activity period, as well as the differences in the fault distance and the thickness of the mudstone caprock at different locations in space, and the evaluation results are different from the actual situation. Fu Guang and others from the School of Earth Sciences of Northeast Petroleum University proposed a method of determining the sealing capacity of the fault by comparing the relative size of the displacement pressure of the fault rock and the displacement pressure of the underlying reservoir rock in the article "Method and Application of Using the Mud Content of Fault Rock to Determine the Vertical Sealing Capacity of the Fault". This method takes into account the closure of the fault, but the destructiveness of the fault is related to the relationship between the fault activity time and the oil and gas accumulation period, and the fault rock displacement pressure in the actual formation is not linearly correlated with the fault burial depth and mud content. Therefore, the evaluation of the oil and gas reservoir closure destruction fault obtained by the above method is different from the actual fault situation and has low accuracy. Summary of the invention

[0004] The purpose of the present invention is to provide a quantitative evaluation method for oil and gas reservoir trap damage faults, so as to solve the problem of low accuracy in the current evaluation of oil and gas reservoir trap damage faults.

[0005] In order to solve the above technical problems, the present invention provides a quantitative evaluation method for oil and gas reservoir trap failure faults, which method comprises the following steps:

[0006] 1) Obtain relevant seismic and geological data of the fault to be evaluated, and use the above data to determine the activity time of the fault to be evaluated and the oil and gas accumulation period;

[0007] 2) When the fault activity time is earlier than the oil and gas accumulation period, calculate the shale content of the fault rock at different sampling points inside the fault to be evaluated;

[0008] 3) Using the fault core data of the drilled trap-damaged fault, the displacement pressure at each sampling point of the fault is determined. Based on the displacement pressure, shale content and fault burial depth at each sampling point of the drilled trap-damaged fault, a machine learning algorithm is used to establish a nonlinear relationship between displacement pressure, shale content and burial depth;

[0009] 4) Inputting the mud content and fault burial depth at different sampling points of the fault to be evaluated into the above model to calculate the displacement pressure at different sampling points of the fault to be evaluated;

[0010] 5) Compare the displacement pressure at each sampling point of the fault to be evaluated with the displacement pressure F0 of the reservoir of the same layer under the fault to be evaluated. If the displacement pressure at each sampling point of the fault is not less than F0, it indicates that the fault is not a destructive fault. Otherwise, the fault is considered to be a destructive fault.

[0011] The present invention first determines the relationship between the activity time of the fault and the oil and gas accumulation period. When the activity time of the fault is earlier than the oil and gas accumulation period, the displacement pressure at each sampling point of the fault with the same three-dimensional coordinate is determined by using the core data of the drilled trap-damaged fault, and the displacement pressure at each sampling point of the drilled trap-damaged fault and the mud content data and fault burial depth data corresponding to the three-dimensional coordinate are used to establish a nonlinear relationship between the displacement pressure and the mud content and the burial depth. The nonlinear relationship is used to determine the corresponding displacement pressure at each sampling point of the fault to be evaluated according to the burial depth and mud content of the fault to be evaluated. If the displacement pressure at the sampling point of the fault to be evaluated is not less than the displacement pressure F0 of the reservoir of the same layer system of the reservoir underlying the fault, it is considered that the fault does not belong to a destructive fault. The present invention fully considers the early and late relationship between the fault activity time and the oil and gas accumulation period, and also considers the nonlinear relationship between the fault rock displacement pressure and the fault burial depth and the fault mud content, thereby improving the accuracy of the evaluation of the oil and gas reservoir closure damage fault.

[0012] Furthermore, in order to accurately describe the relationship between the displacement pressure of fault rocks and the shale content of fault rocks and the fault burial depth, the nonlinear relationship between the displacement pressure of fault rocks and the shale content of fault rocks and the fault burial depth is obtained by training a neural network model.

[0013] Furthermore, in order to accurately obtain the shale content, the process of determining the shale content at different sampling points of the fault to be evaluated in step 4) is as follows:

[0014] a. Based on the seismic and geological data of the fault to be evaluated, conduct joint well-seismic inversion to determine the volume fraction of mudstone on both sides of the fault to be evaluated;

[0015] b. The intersection of the seismic survey line and the fault to be evaluated is used as the sampling point, the interval between seismic traces is used as the interval sampling, and the three-dimensional coordinate system composed of geodetic coordinates and altitude depth is used as the reference system;

[0016] c. Calculate the fault rock shale content at each sampling point within the fault to be evaluated based on the obtained shale volume fraction.

[0017] Furthermore, in step c, the SGR method is used to calculate the shale content of the fault rock at each sampling point.

[0018] Furthermore, when the activity time of the fault to be evaluated is later than the oil and gas accumulation period, the fault to be evaluated is directly judged as a destructive fault.

[0019] Furthermore, the learning algorithm is a neural network. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of the quantitative evaluation method of oil and gas reservoir trap damage fault of the present invention;

[0021] Figure 2 It is a schematic diagram of the distribution of traps and faults in the Y basin in the embodiment of the present invention. DETAILED DESCRIPTION

[0022] The specific implementation of the present invention will be further described below in conjunction with the accompanying drawings.

[0023] Method Embodiment

[0024] The present invention first obtains relevant seismic data and geological data of the fault to be evaluated, and uses the above data to determine the activity time and oil and gas accumulation period of the fault to be evaluated; when the fault activity time is earlier than the oil and gas accumulation period, calculate the shale content of the fault rock at different sampling points inside the fault to be evaluated; use the fault core data of the drilled closed and damaged fault to determine the displacement pressure along the fault, and use a neural network to establish the relationship between the fault rock displacement pressure and the fault rock shale content and the fault burial depth; input the shale content and fault burial depth at different sampling points of the fault to be evaluated into the above model to calculate the displacement pressure at different sampling points of the fault to be evaluated; compare the displacement pressure at each sampling point of the fault to be evaluated with the displacement pressure F0 of the reservoir of the same formation of the underlying reservoir of the fault to be evaluated. If the displacement pressure at each sampling point of the fault is not less than F0, it indicates that the fault is not destructive and does not belong to a destructive fault. Its implementation process is as follows: Figure 1 As shown, a detailed description is given below taking a basin Y as an example.

[0025] 1. Obtain seismic and geological data related to the fault to be evaluated.

[0026] The data obtained by the present invention include relevant data on the damaged faults in the drilled traps and the faults that cut into the cap layers in the undrilled traps in the area to be evaluated, fault burial depth data, regional geological data, research results on the reservoir formation period in the area to be evaluated, fault rock core sampling data of the damaged faults in the drilled traps, and three-dimensional and two-dimensional seismic data and seismic interpretation results in the area to be evaluated.

[0027] For this embodiment, geological data of the area where the drilled trap B (trap B1, trap B2, and trap B3 all have developed faults) and the undrilled trap A (trap A1, trap A2, and trap A3 all have developed faults) in the Y basin are collected, such as Figure 2 As shown in the figure, it specifically includes: relevant data on the faults to be evaluated in traps A1, A2, and A3 and the known damaged faults in traps B1, B2, and B3, fault burial depth data, regional geological data, research results on the reservoir formation period of the Y basin, fault rock coring data of the drilled damaged faults in the traps, and 3D and 2D seismic data and seismic interpretation results of the area to be evaluated. The coring data uses the geodetic coordinates and altitude depth as the 3D coordinate system.

[0028] 2. Determine the activity time of the fault to be evaluated, and determine whether the fault to be evaluated is a destructive fault based on the fault activity time.

[0029] The fault throws of different layers of the faults to be evaluated are determined by the collected seismic interpretation results. The activity period of the faults to be evaluated is determined based on the change of the fault throws and the fault penetration of the formation. The regional reservoir formation period is compared with the fault activity time. If the fault to be evaluated is later than the oil and gas accumulation period, it is determined to be a destructive fault. If the fault to be evaluated is earlier than the oil and gas accumulation period, further evaluation is required.

[0030] In this embodiment, according to the seismic data, there are 6 faults to be evaluated in traps A1, A2, and A3. A1-1 、f A1-2 、f A1-3 、f A2-1 、f A3-1 、f A3-2 ,like Figure 2 As shown in the figure, according to the collected seismic interpretation results, the fault throws of different layers of the faults to be evaluated in traps A1, A2 and A3 and the fault penetration of the strata are statistically analyzed. Compared with the main reservoir formation period in the Y basin, which is the late Cretaceous, it is clear that there are two faults in the traps to be evaluated. A1-3 、f A3-2 The active period is later than the Cretaceous period, and it is a destructive fault. The other four faults areA1-1 、f A1-2 、f A2-1 、f A3-1 The period of activity is earlier than the Cretaceous period and needs further judgment.

[0031] 3. Calculate the shale content of the fault rock at different sampling points within the fault to be evaluated.

[0032] Based on regional geological data and seismic data, the volume fraction of mudstone on both sides of the fault is obtained by the joint well-seismic inversion method. The intersection of the seismic line and the fault to be evaluated is used as the sampling point, and the interval between seismic traces is used as the interval sampling. The SGR method is used to calculate the shale content of the fault rock at different sampling points inside the fault to be evaluated.

[0033] For this example, the interval between seismic lines in the study area is 12.5 m, and 12.5 m is taken as the fault f. A1-1 、f A1-2 、f A2-1 、f A3-1 、f B1 、f B2 、f B3 The sampling interval is based on the seismic lines and seven faults. A1-1 、f A1-2 、f A2-1 、f A3-1 、f B1 、f B2 、f B3 The intersection point is used as the sampling point, and the mudstone volume fraction data set of different sampling points is read in combination with the three-dimensional data body of the mudstone volume fraction: DATA(r A1-1 )、DATA(r A1-2 )、DATA(r A2-1 )、DATA(r A3-1 )、DATA(r B1 )、DATA(r B2 )、DATA(r B3 ). The shale content data of the fault rocks at different sampling points of the seven faults were obtained by the SGR calculation formula, and the shale content data set of the seven faults was obtained: DATA(R A1-1 )、DATA(R A1-2 )、DATA(R A2-1 )、DATA(R A3-1 )、DATA(R B1 )、DATA(R B2 )、DATA(R B3 ).

[0034] 4. Establish a calculation model for fault rock displacement pressure.

[0035] Using the fault rock core data of the drilled trap-damaged fault and the core data of the reservoir in the same layer system as the underlying reservoir of the fault to be evaluated, the fault rock displacement pressure and the underlying reservoir displacement pressure F0 are measured in the laboratory by the direct displacement method, and the corresponding geodetic coordinates and altitude depths are selected. The calculated value of the shale content of the fault rock and the fault burial depth are used to establish a model, and a nonlinear relationship between the fault rock displacement pressure and the calculated value of the shale content of the fault rock and the fault burial depth is established by a machine learning algorithm, wherein the machine learning algorithm uses a neural network. In addition to the neural network, other machine learning algorithms can also be used. The present invention uses the rock displacement pressure of the drilled trap-damaged fault, the calculated value of the shale content of the fault rock and the fault burial depth to train the neural network, and uses the trained neural network model as a fault rock displacement pressure calculation model.

[0036] For this example, the displacement pressure of fault rocks in three known traps was measured in the laboratory using the direct displacement method using coring data:

[0037] F B1-1 =3.12MPa, F B1-2 =3.54MPa, F B1-3 =4.12MPa, F B1-4 =2.98MPa, F B1-5 =3.22MPa,

[0038] F B1-6 =4.65MPa, F B1-7 =3.82MPa, F B1-8 =2.83MPa, F B2-1 =4.22MPa, F B2-2 =4.74MPa,

[0039] F B2-3 =4.01MPa, F B2-4 =3.77MPa, F B2-5 =4.23MPa, F B3-1 =3.83MPa, F B3-2 =3.68MPa,

[0040] F B3-3 =3.57MPa, F B3-4 =2.99MPa, F B3-5 =3.23MPa, F B3-6 =406MPa, F B3-7 =4.13MPa,

[0041] Note: The displacement pressures at different sampling points within the same fault rock are different. The above measured data correspond to the measured displacement pressure values ​​of fault rocks at different fault sampling points. At the same time, the displacement pressure of the reservoir in the same layer system as the unknown trap reservoir in the Y basin to be evaluated was measured to be F0 = 3.57MPa.

[0042] Combining the collected fault data, the average burial depth of the seven faults is obtained: D A1-1 =1023m, D A1-2 =913m, D A2-1 =984m, D A3-1 =925m, D B1 =1066m, D B2 =745m, D B3 = 918m, and the mud content R of the sampling point corresponding to the displacement pressure of the fault is calculated using the calculation results of step 3 B1-1 =51.63%, R B1-2 =47.54%, R B1-3 =39.86%, R B1-4 =62.13%, R B1-5 =71.11%, R B1-6 =61.23%, R B1-7 =42.37%, R B1-8 =46.85%, R B2-1 =61.25%, R B2-2 =56.73%, R B2-3 =62.17%, R B2-4 =50.07%, R B2-5 =53.48%, R B3-1 =46.77%, R B3-2 =47.98%, R B3-3 =43.23%, R B3-4 =39.25%, R B3-5 =41.54%, R B3-6 =45.22%, R B3-7 =50.13%.

[0043] The calculation model of the present invention adopts a neural network, and the displacement pressure of each sampling point of the drilled closed fault, the mud content at the corresponding sampling point, and the fault burial depth are input into the neural network for training, so as to establish the relationship between the fault displacement pressure and the mud content and the fault burial depth, which is the calculation model of the displacement pressure.

[0044] 5. Calculate the displacement pressure at different sampling points of the fault to be evaluated, and determine whether the fault is a destructive fault based on the difference between the displacement pressure at different sampling points and F0.

[0045] The displacement pressure calculation model is used to calculate the displacement pressure of fault rocks at different sampling points of the fault to be evaluated, and the fault rock displacement pressure dataset F of the fault to be evaluated is obtained. a(a＝1、2、3、4…) , calculate F a If the difference with F0 is all positive or 0, the fault to be evaluated is not a destructive fault. If there is a negative value, the fault to be evaluated is a destructive fault.

[0046] In this embodiment, the displacement pressure calculation model established in step 4 is used to calculate the displacement pressure of the four faults to be evaluated. A1-1 、f A1-2 、f A2-1 、f A3-1 The displacement pressure of the four faults is calculated and the displacement pressure data set of the four faults is obtained: DATA(F A1-1 )、DATA(F A1-2 )、DATA(F A2-1 )、DATA(F A3-1 ). Statistical data set F n The difference between DATA(F0) and F0 shows that A1-1 -F0)、DATA(F A2-1 -F0) are all positive values, DATA(F A1-2 -F0)、DATA(F A3-1 -F0) has a negative value, so the fault to be evaluated f A1-1 、f A2-1 The closed fault is not a destructive fault, f A1-2 、f A3-1 It is a destructive fault.

[0047] Combined with the results of the second step, it can be seen that the oil and gas reservoir traps A1 and A3 are destroyed by fault f A1-3 、f A1-2 、f A3-1 、f A3-2 The fault in trap A2 is closed and the damaged fault does not exist, so it has exploration potential.

[0048] To further demonstrate the effect of the present invention, the present invention is compared with the method used by Fu Guang in the article "Method and application of using fault rock mud content to judge the vertical closure of faults" in the study of fault closure in Nanpu Sag, Bohai Bay Basin. The present invention not only considers the activity period of the fault, but also considers the spatial heterogeneity of the displacement pressure of the fault rock. In the study of comparing the displacement pressure, Fu Guang focused on the influence of the original rock mud content, the compaction time and the compaction force angle during the diagenesis process on the fault rock. By replacing formulas ①, ③, and ④, the displacement pressure comparison relationship is replaced by the relationship between the mud content of the fault rock and the mud content of the underlying reservoir rock, the diagenesis time of the fault rock and the diagenesis time of the underlying reservoir, and the fault dip angle, and formula ⑤ is obtained, based on which the fault closure is evaluated.

[0049]

[0050] P c is the displacement pressure of sedimentary rocks; Z c is the burial depth of compaction of sedimentary style; Rc is the mud content of sedimentary rock; a and b are regional related parameters.

[0051]

[0052] Z f T is the burial depth of fault rock compaction; f T is the compaction and diagenesis time of fault rock; c is the compaction and diagenesis time of the cap rock with the same burial depth as the fault rock; Z c The compaction diagenesis burial depth of the cap rock is the same as that of the fault rock.

[0053] P df ≥P dr .........................................④

[0054] P df is the displacement pressure of fault rock; P dr is the displacement pressure of the underlying reservoir rock.

[0055]

[0056] R fmin R is the minimum mud content required for fault sealing; r is the mud content of the underlying reservoir rock; T f is the diagenesis time of fault rock; Tc is the formation time of the trap cap rock; θ is the fault dip angle.

[0057] And from the calculation example in Nanpu Sag, it can be seen that during the calculation process, the fault dip and the shale content of the fault rock are taken as single values, and it is speculated that they are represented by the average value or the value of a single position. The actual shale content of the fault rock is also calculated by the SGR method.

[0058] The method was applied to the six faults to be evaluated in traps A1, A2 and A3. A1-1 、f A1-2 、f A1-3 、f A2-1 、f A3-1 、f A3-2 The calculation results are shown in Table 1:

[0059] Table 1

[0060]

[0061] You can see f A1-3 、f A3-2 The activity period is later than the accumulation period (the compaction time is short), which should be a destructive fault. However, the calculation results according to the method in this article show that it is a closed fault, which is a misjudgment due to lack of consideration. A1-2 The actual shale content of the fault rock (average value) is 43.1%>41.1%, which belongs to a closed fault. However, the calculated data set (a total of 12 sampling points: sampling point 1, 45.6%; 2, 47.8%; 3, 35.6%; 4, 37.9%; 5, 49.1%; 6, 45.3%; 7, 39.8%; 8, 43.2%; 9, 44.1%;

[0062] 10, 42.7%; 11, 46.3%; 12, 40.1%), there are 4 data less than 41.1%, indicating that it is locally damaged, and oil and gas can flow up along the local damage position (sampling points 3, 4, 7, 12). A1-1 、f A1-2 、f A1-3 It is a closed fault, and the trap A1 has not been destroyed and has exploration value, which is somewhat misleading for exploration deployment.

[0063] Since the present invention fully considers the early and late relationship between the fault activity time and the oil and gas accumulation period, as well as the nonlinear relationship between the fault rock displacement pressure and the fault burial depth and the fault mud content, the accuracy of the evaluation of the oil and gas reservoir closure damage fault is guaranteed.

Claims

1. A quantitative evaluation method for oil and gas reservoir trap failure faults, characterized in that: The method comprises the following steps: 1) Obtain relevant seismic and geological data of the fault to be evaluated, and use the above data to determine the activity time of the fault to be evaluated and the oil and gas accumulation period; 2) When the fault activity time is earlier than the oil and gas accumulation period, calculate the shale content of the fault rock at different sampling points inside the fault to be evaluated; 3) Using the fault core data of the drilled trap-damaged fault, the displacement pressure at each sampling point of the fault is determined. Based on the displacement pressure, shale content and fault burial depth at each sampling point of the drilled trap-damaged fault, a machine learning algorithm is used to establish a nonlinear relationship between displacement pressure, shale content and burial depth; 4) Inputting the mud content and fault burial depth at different sampling points of the fault to be evaluated into the above model to calculate the displacement pressure at different sampling points of the fault to be evaluated; 5) Compare the displacement pressure at each sampling point of the fault to be evaluated with the displacement pressure F0 of the reservoir of the same layer under the fault to be evaluated. If the displacement pressure at each sampling point of the fault is not less than F0, it indicates that the fault is not a destructive fault. Otherwise, the fault is considered to be a destructive fault.

2. The quantitative evaluation method for oil and gas reservoir trap failure faults according to claim 1, characterized in that: The relationship between fault rock displacement pressure, fault rock shale content and fault burial depth is obtained by training a neural network model.

3. The quantitative evaluation method for oil and gas reservoir trap failure faults according to claim 1, characterized in that: The process of determining the shale content at different sampling points of the fault to be evaluated in step 4) is as follows: a. Based on the seismic and geological data of the fault to be evaluated, conduct joint well-seismic inversion to determine the volume fraction of mudstone on both sides of the fault to be evaluated; b. The intersection of the seismic survey line and the fault to be evaluated is used as the sampling point, the interval between seismic traces is used as the interval sampling, and the three-dimensional coordinate system composed of geodetic coordinates and altitude depth is used as the reference system; c. Calculate the fault rock shale content at each sampling point within the fault to be evaluated based on the obtained shale volume fraction.

4. The quantitative evaluation method for oil and gas reservoir trap failure faults according to claim 3 is characterized in that: In step c, the SGR method is used to calculate the shale content of the fault rock at each sampling point.

5. The quantitative evaluation method for oil and gas reservoir trap failure faults according to claim 1, characterized in that: When the activity time of the fault to be evaluated is later than the oil and gas accumulation period, the fault to be evaluated is directly judged as a destructive fault.

6. The quantitative evaluation method of oil and gas reservoir trap failure fault according to claim 1, characterized in that: The learning algorithm is a neural network.

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