Method for qualitatively evaluating scale of fault control fracture-cavity type reservoir and application of method

Abstract

The invention provides a method for qualitatively evaluating the scale of a fault control fracture-cavity type reservoir and application of the method, and relates to the field of oil and gas field exploration evaluation. The method comprises the following steps: collecting reservoir transformation data of a to-be-evaluated fault control fracture-cavity reservoir; according to the collected reservoir transformation data, a fitting pumping pressure curve is obtained; and comparing the actual pumping pressure curve of the to-be-evaluated fault control fracture-vug type reservoir with the fitting pumping pressure curve, and qualitatively evaluating the development and scale of the to-be-evaluated fault control fracture-vug type reservoir according to the significant degree of the curve amplitude difference and the difference value of the curve amplitude difference. The invention further provides application of the method in production of reservoir reforming equipment. Compared with the prior art, the method has the advantages that by calculating and fitting the displacement-pump pressure curve in the existing reservoir transformation data, the reservoir transformation data can be directly applied to the evaluation of the reservoir body scale, and the method is simpler and more visual.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas field exploration and evaluation, and particularly relates to a method for qualitatively evaluating the scale of fault-controlled fracture-vug reservoirs and its application. Background Art

[0002] The description of reservoir bodies is one of the core tasks in oil and gas exploration and development, and plays an important role in understanding the oil or gas storage potential of reservoir bodies, guiding drilling and production, geological research, resource evaluation, development planning, and environmental protection.

[0003] Fault-controlled fracture-vug oil and gas reservoirs are a special type of oil and gas reservoirs, and their formation is mainly controlled by fault zones and karstification. The reservoir bodies of such oil and gas reservoirs are mainly composed of bedrock, fracture zones, and cave zones, forming a set of multi-group fracture-vug aggregates. Among them, the cave zone and the fracture zone are the main storage spaces, and the seismic identification characteristics are faults - beads - chaotic strong reflections; such oil and gas reservoirs are not controlled by structural burial depth, and the effective reservoir burial depth extends to below 8500m. The description of their reservoir bodies currently faces two major problems: ① During the drilling process, when encountering a fault-controlled fracture-vug reservoir with a huge scale, situations such as mud loss or drill string flushing often occur, increasing the well control risk, and lacking physical data such as cores and logging, which brings great difficulties to the description and evaluation of reservoir bodies; ② Fault-controlled fracture-vug reservoir bodies have strong heterogeneity, and the existing data such as cores and logging are on a small scale and cannot reflect the scale of the reservoir body around the well.

[0004] Therefore, it is necessary to develop a method for evaluating or predicting the scale of fault-controlled fracture-vug reservoirs specifically.

[0005] CN115822563B discloses a high-yield well design method for ultra-deep fault-controlled fracture-vug oil and gas reservoirs, which relates to the field of ultra-deep oil and gas exploration technology, and includes the following steps: constructing a grid structure model of fault-controlled fracture-vug reservoir bodies based on strike-slip tectonic fractures and volume adjustment and reservoir formation mechanisms; selecting favorable target areas; estimating the geological reserves, spatial distribution, and scale of favorable target areas through spatial carving; evaluating the connectivity of reservoir bodies in favorable target areas through discrete tracking technology; establishing a high-yield well productivity prediction model; designing the trajectory of non-target layers; and designing acid fracturing communication modes according to the geological models of different grid reservoirs. This invention solves the problem of target selection through strike-slip fault three-dimensional interpretation and reservoir prediction; and solves the problem of difficult estimation of the reserves of fault-controlled fracture-vug reservoir bodies through reservoir body spatial carving technology; however, the estimation of the reserves of fault-controlled fracture-vug reservoir bodies provided by this invention is still based on physical data such as logging, and it is difficult to predict the reserves of fault-controlled fracture-vug reservoir bodies in the case where physical data is difficult to obtain.

[0006] CN114021466B discloses a method for predicting the effective fracture network volume of shale gas based on flowback data and machine learning. The steps are as follows: Establish a two-phase flowback model of fracturing fluid. Based on the flowback data, invert the effective fracture network volume of the fractured shale gas well to obtain a labeled data set of the effective fracture network volume of shale gas; Establish a suitable comprehensive feature index calculation model for feature selection to obtain strongly correlated features affecting the effective fracture network volume of shale gas; Perform correlation calculation and principal component analysis on the strongly correlated features selected by feature selection, and establish a machine learning prediction model for the effective fracture network volume of shale gas; Apply the established prediction model for the effective fracture network volume of shale gas, use the feature importance evaluation method PI to calculate the relative importance of each feature to the effective fracture network volume, obtain the main control factors of the post-fracture effect of the fractured shale gas well, establish a genetic algorithm optimization workflow for the construction parameters of the fractured shale gas well, and optimize the design of the fracturing construction parameters. The invention ingeniously adopts the bright flowback model of fracturing fluid to realize the prediction of the effective fracture network volume of shale gas while reducing the dependence on physical data. However, there are obvious differences in the distribution characteristics, formation mechanisms, and exploration potential of shale gas reservoirs and fault-controlled fracture-vug reservoirs. It is difficult to apply the method of the invention to fault-controlled fracture-vug reservoirs with a multi-group fracture-vug aggregate structure.

[0007] For fault-controlled fracture-vug reservoirs, due to the strong heterogeneity of the reservoir body, high and stable production can only be achieved through reservoir stimulation measures such as acid fracturing or acidizing. Therefore, complete reservoir stimulation data is available. The scale of this reservoir stimulation data is large and can meet the demand for large-scale data required to describe the reservoir body size.

[0008] Therefore, how to efficiently utilize reservoir stimulation data to qualitatively analyze and evaluate the scale of fracture-vug controlled reservoirs is one of the key issues studied by those skilled in the art. Summary of the Invention

[0009] Aiming at the problem that there are no conventional core, logging and other physical data for the description of fault-controlled fracture-vug reservoirs in the prior art, or the small scale of core, logging and other data makes it difficult to describe the reservoir body of fault-controlled fracture-vug reservoirs, the present invention provides a method for qualitatively evaluating the scale of fault-controlled fracture-vug reservoirs. By transforming and fitting the displacement curve in the reservoir stimulation data, the reservoir stimulation data can be directly applied to the evaluation of the reservoir body size, and it is simpler and more intuitive.

[0010] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0011] A method for qualitatively evaluating the scale of fault-controlled fracture-vug reservoirs includes the following steps:

[0012] S1. Collect the reservoir stimulation data of the fault-controlled fracture-vug reservoir to be evaluated;

[0013] S2. Obtain the fitted pump pressure curve according to the reservoir stimulation data collected in step S1;

[0014] S3. Compare the actual pump pressure curve of the to-be-evaluated fault-controlled fracture-vug reservoir with the fitted pump pressure curve obtained in step S2, and qualitatively evaluate the development and scale of the to-be-evaluated fault-controlled fracture-vug reservoir according to the significance degree of the curve amplitude difference and the magnitude of the curve amplitude difference.

[0015] Preferably, the reservoir stimulation data collected in step S1 includes displacement data and pump pressure data.

[0016] Preferably, the method for obtaining the fitted pump pressure curve in step S2 includes the following steps:

[0017] S201. Extract the pump pressure data and displacement data of the reservoir stimulation data in step S1;

[0018] S202. Take the highest pump pressure of the pump pressure data obtained in step S201 as a constraint condition, and calculate the fitted pump pressure curve according to the displacement-pump pressure function relationship of the under-developed drilling reservoir of the reservoir, in combination with the displacement data.

[0019] Further preferably, the method for obtaining the displacement-pump pressure function relationship of the under-developed drilling reservoir of the reservoir includes the following steps:

[0020] (1) Extract the reservoir stimulation data of the under-developed drilling reservoir of the reservoir;

[0021] (2) Extract the displacement data and pump pressure data of the reservoir stimulation data described in step (1);

[0022] (3) Take the displacement data obtained in step (2) as the independent variable and the pump pressure data as the dependent variable, and use regression analysis to obtain the displacement-pump pressure function relationship.

[0023] Even more preferably, the under-developed drilling reservoir of step (1) needs to be pre-selected, and the under-developed drilling reservoir to be pre-selected needs to meet the following conditions:

[0024] ① There is no phenomenon of drill string flushing and mud loss during drilling, and there is little oil and gas show;

[0025] ② The logging reservoir is under-developed: there are no type I and type II reservoirs, and there is very little development or no development of type III reservoirs;

[0026] ③ The production performance shows a dry layer.

[0027] Even further preferably, the displacement-pump pressure function relationship in step (3) is as follows:

[0028] P = a 1 L b + a2 ;

[0029] Among them, P represents the pump pressure, a 1 , b, a 2 are variables, and L represents the displacement.

[0030] Furthermore, preferably, the method of regression analysis in step (3) is as follows:

[0031] (3.1) Select the "exponential" function regression model; this is because in the case of an uncommunicated large-scale reservoir, continuously injecting fracturing fluid into the wellbore is equivalent to continuously injecting liquid into a closed container. A slight change in displacement will cause an exponential increase in the pressure inside the container;

[0032] (3.2) In the case of using the function regression model described in step (3.1), use the least squares method to fit the regression equation of displacement and pump pressure.

[0033] Most preferably, the method for calculating the fitted pump pressure curve described in step S202 is: using the least squares method to solve for the a 1 , b and a 2 ;

[0034] The least squares method includes the steps of data transformation, linearization processing, residual calculation, minimization of the sum of squared residuals, and calculation results in sequence;

[0035] Specifically, the least squares method includes the following steps:

[0036] ① Perform a logarithmic transformation on the displacement-pump pressure function relationship to obtain a new equation ln(P) = ln(a 1 ) + b * ln(L) + ln(a 2 );

[0037] ② Convert the equation obtained in step ① into a linear function form, that is, Y = AX + B, where Y = ln(P), A = b, X = ln(L), and B = ln(a 2 );

[0038] ③ Based on step ②, for each data point (L i , ln(P i ))), calculate its residual e i = Yi - (AX i + B);

[0039] ④ Solve for A and B that minimize ∑(e i 2 ) calculated in step ③;

[0040] ⑤ According to the results obtained in step ④, inversely deduce to obtain a 1 , b and a2 value

[0041] The present invention also provides an application of the method for qualitatively evaluating the scale of a fault-controlled fracture-vug reservoir in reservoir stimulation.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] By calculating and fitting the displacement-pump pressure curve in the existing reservoir stimulation data, the reservoir stimulation data can be directly applied to the evaluation of the reservoir body scale, which is simpler and more intuitive; it solves the difficult problem of describing the reservoir body scale in the case of no physical data such as cores and logging or small-scale data of cores and logging. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of a method for qualitatively evaluating the scale of a fault-controlled fracture-vug reservoir provided by the present invention;

[0045] Figure 2 is an actual reservoir stimulation data curve graph of a drilling reservoir with underdeveloped reservoirs preferred by the present invention;

[0046] Figure 3 is a pump pressure-fitted pump pressure curve graph of a drilling reservoir with underdeveloped reservoirs preferred by the present invention;

[0047] Figure 4 is an actual reservoir stimulation data curve graph of the fault-controlled fracture-vug reservoir to be evaluated in the embodiment of the present invention;

[0048] Figure 5 is a schematic diagram of the amplitude difference between the actual pump pressure and the fitted pump pressure of the fault-controlled fracture-vug reservoir to be evaluated in the embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] Embodiment A method for qualitatively evaluating the scale of a fault-controlled fracture-vug reservoir and its application

[0050] 1. Determine the function model

[0051] (1) Select a drilling reservoir with underdeveloped reservoirs preferably:

[0052] Select a drilling reservoir with underdeveloped reservoirs preferably, and study the relationship between pump pressure data and displacement data to determine an accurate function model. The criteria for underdeveloped reservoirs are:

[0053] ① During the drilling process, there is no phenomenon of drill string flushing and mud loss (the phenomenon of drill string flushing and mud loss is a response characteristic of reservoir development), and there is little oil and gas show;

[0054] ② Logging shows underdeveloped reservoirs: there are no type I and type II reservoirs, only sporadic type III reservoirs are developed, and even type III reservoirs are not developed;

[0055] ③The production shows as a dry layer.

[0056] (2) Determination of the function model

[0057] a. Data extraction: Preferably select the drilling reservoir with underdeveloped reservoirs; then extract the displacement data as the independent variable and the pump pressure data as the dependent variable from the actual reservoir stimulation data ( Figure 2 ) of the preferably selected drilling reservoir with underdeveloped reservoirs.

[0058] b. Data fitting and establishment of the functional relationship: Use the independent variable and the dependent variable obtained in the data extraction step for regression analysis:

[0059] Data observation: Observe the reservoir stimulation data and find that when the large-scale reservoir is not connected or the reservoir around the well is underdeveloped, the pump pressure increases with the increase of the displacement, showing an obvious positive correlation;

[0060] Function selection: According to the characteristics of the data set ( Figure 2 ), select the "exponential" function regression model (when the reservoir is underdeveloped or the large-scale reservoir has not been connected, continuously injecting fracturing fluid into the wellbore is equivalent to continuously injecting liquid into a closed container. A slight change in the displacement will cause an exponential increase in the pressure in the container;

[0061] Fitting model: Use the least squares method to fit an optimal regression equation by establishing the mathematical relationship between the variable (displacement) and the variable (pump pressure);

[0062] Obtain the functional relationship between the pump pressure and the displacement under the condition of underdeveloped reservoirs, and calculate the fitting pump pressure curve graph of underdeveloped reservoirs through the displacement data according to this functional relationship ( Figure 3 ): P = a 1 L b + a 2 ; where P represents the pump pressure, a 1 , b, a 2 are variables, and L represents the displacement.

[0063] 2. Qualitative evaluation of the reservoir

[0064] (a) Solve the variables: With the highest pump pressure in the reservoir stimulation data of the drilling well of the fracture-cavity type reservoir to be evaluated as the constraint condition, use the displacement data and the established functional relationship, and solve the unknown variables a 1 , b and a 2 of the 3 parameters in the function model:

[0065] ①Perform a logarithmic transformation on the displacement-pump pressure functional relationship to obtain a new equation ln(P) = ln(a 1 ) + b * ln(L) + ln(a 2 );

[0066] ② Convert the equation obtained in step ① into the form of a linear function, i.e., Y = AX + B, where Y = ln(P), A = b, X = ln(L), and B = ln(a 2 );

[0067] ③ Based on step ②, for each data point (Li, ln(P i ), calculate its residual e i = Yi - (AX i + B);

[0068] ④ Solve for A and B that minimize ∑(e i 2 ) calculated in step ③;

[0069] ⑤ Based on the results obtained in step ④, inversely deduce the values of a 1 , b, and a 2 .

[0070] Obtain a pump pressure curve assuming underdeveloped reservoir conditions.

[0071] (b) Comparative analysis: Compare and analyze the actual pump pressure curve and the fitted pump pressure curve in Figure 4 . If there is an obvious amplitude difference between the fitted pump pressure curve and the actual pump pressure curve, it indicates that the reservoir of this well is developed. Judge the size of the reservoir according to the magnitude of the amplitude difference. The larger the amplitude difference, the larger the reservoir size ( Figure 5 ).

[0072] Figure 4 and Figure 5 are actual application cases, Figure 4 is the actual acid fracturing curve of a certain well; Figure 5 is the calculation of the fitted pump pressure curve using the method in the patent: It can be seen from this figure that there is a large amplitude difference between the fitted pump pressure curve and the actual pump pressure curve, indicating that the reservoir is developed and the reservoir size is large.

[0073] In the initial production stage of this well, under a 14mm choke, the flowing wellhead pressure is 47.6 MPa, the daily oil production is 382 m 3 , the daily gas production is 1.05 million cubic meters, and the equivalent oil and gas production is 1220 t / d; the actual production effect verifies the correctness of the reservoir size analysis.

[0074] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than limiting the protection scope of the present invention. Any simple modification or equivalent replacement of the technical solution of the present invention by those of ordinary skill in the art does not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A method for qualitatively evaluating the scale of fault-controlled fracture-vuggy reservoirs, characterized in that, it includes the following steps: S1. Collect reservoir stimulation data of the fault-controlled fracture-vuggy reservoir to be evaluated; S2. Obtain a fitted pump pressure curve based on the reservoir stimulation data collected in step S1; S3. Compare the actual pump pressure curve of the fault-controlled fracture-vuggy reservoir to be evaluated with the fitted pump pressure curve obtained in step S2, and qualitatively evaluate the development and scale of the fault-controlled fracture-vuggy reservoir to be evaluated according to the significance of the curve amplitude difference and the magnitude of the curve amplitude difference.

2. The method according to claim 1, characterized in that, the reservoir stimulation data collected in step S1 includes displacement data and pump pressure data.

3. The method according to claim 1, characterized in that, the method for obtaining the fitted pump pressure curve in step S2 includes the following steps: S201. Extract the pump pressure data and displacement data of the reservoir stimulation data in step S1; S202. Take the maximum pump pressure of the pump pressure data obtained in step S201 as a constraint condition, and calculate the fitted pump pressure curve according to the displacement-pump pressure function relationship of the under-developed drilling reservoirs in the reservoir, in combination with the displacement data.

4. The method according to claim 3, characterized in that, the method for obtaining the displacement-pump pressure function relationship of the under-developed drilling reservoirs in the reservoir includes the following steps: (1) Extract the reservoir stimulation data of the under-developed drilling reservoirs in the reservoir; (2) Extract the displacement data and pump pressure data of the reservoir stimulation data described in step (1).

5. The method according to claim 4, characterized in that, the method for obtaining the displacement-pump pressure function relationship of the under-developed drilling reservoirs in the reservoir further includes the following steps: Take the displacement data obtained in step (2) as the independent variable and the pump pressure data as the dependent variable, and use regression analysis to obtain the displacement-pump pressure function relationship.

6. The method according to claim 5, characterized in that, the regression analysis method includes: Select the "exponential" function regression model; in the case of using the function regression model, use the least squares method to fit the regression equation of displacement and pump pressure.

7. The method according to claim 4, characterized in that, the under-developed drilling reservoirs in step (1) need to be pre-optimized.

8. The method according to claim 7, characterized in that, the under-developed drilling reservoirs in the pre-optimized reservoir need to meet the conditions: There is no drill string flushing and mud loss during drilling, and there is little oil and gas show.

9. The method according to claim 8, characterized in that, the under-developed drilling reservoirs in the pre-optimized reservoir also need to meet the conditions: the logging reservoir is under-developed: there are no type I and type II reservoirs, and there are very few or no developed type III reservoirs.

10. The method according to claim 9, characterized in that, the under-developed drilling reservoirs in the pre-optimized reservoir also need to meet the conditions: the production performance is a dry layer.

11. The method according to claim 6, characterized in that, the displacement-pump pressure function relationship is as follows: P = a 1 L b + a 2 ; Among them, P represents the pump pressure, a 1 , b, a 2 are variables, and L represents the displacement.

12. The method according to claim 3, characterized in that, The method for calculating the fitted pump pressure curve described in step S202 is as follows: Using the least squares method to solve for the a 1 , b and a 2 .

13. The method according to claim 12, characterized in that, The least squares method includes the steps of data transformation, linearization processing, residual calculation, minimization of the sum of squared residuals, and calculation results in sequence.

14. The method according to claim 13, wherein, the least squares method includes the following steps: ①Logarithmically transform the displacement-pump pressure function relationship to obtain a new equation ln(P) = ln(a 1 ) + b * ln(L) + ln(a 2 ); ②Convert the equation obtained in step ① into the form of a linear function, i.e., Y = AX + B, where Y = ln(P), A = b, X = ln(L), and B = ln(a 2 ); ③On the basis of step ②, for each data point (L i , ln(P i ))), calculate its residual e i = Yi - (AX i + B); ④ Solve for A and B that minimize ∑(e i 2 ) calculated in step ③; ⑤Derive a by reverse deduction based on the result obtained in step ④ 1 , b, and a 2 values 15. Application of the method according to any one of claims 1-14 in reservoir reconstruction.

Images