Optimal selection method for reservoir crustal stress dessert
By applying the frequency distribution model, stress coordinate system and superposition partition model in the evaluation of ground stress characteristics, a stress optimization formula was established, which solved the problem that it was difficult to accurately characterize the engineering dessert area of the reservoir ground stress evaluation, and achieved the precise selection of reservoir ground stress desserts and effective guidance on oil field development.
Patent Information
- Application Number
- CN202311558012.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-22
- Publication Date
- 2025-05-23
AI Technical Summary
The existing technology has a lack of theoretical technology in reservoir stress evaluation, making it difficult to accurately characterize the engineering dessert area inside the reservoir, resulting in the inability to effectively guide oil field exploration, development and production.
Through the horizontal stress characteristics assisted evaluation, the frequency distribution model, stress coordinate system and superposition partition model are used to establish stress optimization formulas and comparison evaluation methods to determine the target position of stress desserts.
It achieves accurate selection of reservoir stress desserts, fills the gap in the field of reservoir stress research, and provides effective technical means to guide oil field development and production.
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Figure CN120032007A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of reservoir geostress evaluation, and in particular to a method for optimizing reservoir geostress sweet spots. Background Art
[0002] China's continental strata are rich in oil resources. In the process of selecting well locations, target layers, and target points for development and production, not only geological factors should be considered, but also areas with high oil content and good physical properties should be given priority to delineate geological sweet spots. Engineering factors that affect the fracturing effect should also be considered. The expansion degree and morphological characteristics of the fracturing will also affect production capacity. Among them, geostress is one of the most important engineering influencing factors. The greater the horizontal principal stress, the easier it is for the fracturing to penetrate the layers and form a larger fracture height. The smaller the horizontal principal stress difference, the easier it is for a network of fractures to form.
[0003] Existing technologies ① Wang Yongzhuo et al., "Development and Design Method of Horizontal Well Box for Gulong Shale Oil in Songliao Basin" (Daqing Petroleum Geology and Development, Issue 5, 2021); In view of the fact that the first and second segments of Gulong shale oil have the characteristics of overpressure storage boxes, an innovative concept of horizontal well box development was proposed, and its theoretical connotation and substantive significance were given. On this basis, the results of drilling, logging, seismic, and comprehensive interpretation of enriched layers were integrated to select the optimal development box. By establishing a three-dimensional geological model and a stress field model, the distribution of the artificial fracture network of horizontal well volume fracturing was quantitatively analyzed, the development parameters of the horizontal well box were optimized, and the scale and efficiency development of Gulong shale oil was guided.
[0004] Existing technology ② Wang Fenglan et al. "Characteristics and classification evaluation of Gulong shale oil reservoirs in Songliao Basin" (Daqing Petroleum Geology and Development, Issue 5, 2021); In order to deepen the understanding of the characteristics of Gulong shale oil reservoirs, core observation, experimental analysis, well logging interpretation and other means were used to carry out research on reservoir lithology, lithofacies, electrical properties, physical properties, oil content, source rock characteristics, brittleness and geostress anisotropy. It is believed that Gulong shale has the characteristics of good reservoir properties, rich oil content, high organic matter abundance and thermal evolution, high brittleness index, and small horizontal stress difference. A preliminary classification and evaluation standard for Gulong shale oil reservoirs has been established, which can provide a reliable geological basis for target layer optimization and well site design.
[0005] Existing technology ③ Zheng Jiandong et al. "Seven Property Parameters and Logging Evaluation Methods for Enriched Layers in Gulong Shale Oil Reservoirs in Songliao Basin" (Daqing Petroleum Geology and Development, Issue 5, 2021); Based on the supporting core experiments and oil test data, through core calibration logging, the seven property parameter logging evaluation method and enriched layer classification evaluation standard such as lithology, porosity, oil saturation, organic carbon content and anisotropic geostress were established. The supporting acquisition and seven property parameter logging evaluation technology with lithology scanning, nuclear magnetic resonance and imaging logging series as the core has provided reliable basic data and technical guarantees for the interpretation, well site deployment and reserve submission of more than 200 new and old wells of Gulong shale oil. The formed technical process and method are of guiding significance for the evaluation of similar shale oil reservoirs in China.
[0006] Existing technology ④ Gu Wen et al. "Geological and engineering sweet spot characterization technology for deep shale gas in the western Chongqing area of the Sichuan Basin" (Proceedings of the 2021 Geophysical Exploration Technology Seminar of the China Petroleum Society, 2021); A geostatistical inversion method based on track similarity is used to predict high-quality shale in the Longmaxi Formation. This method not only improves the vertical resolution of reservoir prediction, but also effectively improves the lateral identification capability, solving the thin layer problem of high-quality shale gas exploration. In addition, based on the different influences of faults on shale gas and the characteristics of fractures of different scales, a set of supporting technical processes and fracture grading and classification evaluation technologies suitable for deep shale gas fracture prediction are summarized, which provides technical ideas and support for horizontal well drilling and fracturing in this area.
[0007] Existing technology ⑤ Yuan Meng et al. "Research and Application of Quantitative Prediction Methods for Geostress in Southeast Sichuan" (Proceedings of the 2021 Geophysical Exploration Technology Symposium of the China Petroleum Society, 2021); combined with pre-stack inversion and traditional stress model calculation methods, the support vector machine algorithm (SVM) in machine learning was introduced to correct the prediction results, and the high-quality shale layer of the Wufeng Formation-Longmaxi Formation in Block D in Southeast Sichuan was used as an example to carry out quantitative prediction of spatial geostress. The study shows that the spatial distribution law of geostress is related to factors such as structure and burial depth. The calculation results of the quantitative prediction method are highly consistent with the drilled wells, which effectively improves the accuracy of quantitative calculation of geostress, which is of reference significance for conducting engineering compressibility evaluation research and finding geostress engineering sweet spots.
[0008] Existing technology ⑥ Zhu Haiyan et al. "Four-dimensional geostress evolution of shale gas reservoirs and the law of complex fracture expansion in infill wells" (Acta Petrolei Sinica, Issue 9, 2021); Taking into account the heterogeneity and anisotropy of shale gas reservoir geomechanical parameters, natural fractures, etc., a set of simulation methods for the expansion of complex fractures in hydraulic fracturing of infill wells in shale gas reservoirs based on the four-dimensional geostress evolution of the reservoir was proposed, and a hydraulic fracturing complex fracture staggered expansion model of gas reservoir seepage-geomechanics coupling was established. After long-term exploitation of the oil field, the three-dimensional geostress of the reservoir near the old wells decreased, but the horizontal and bidirectional principal stress differences and vertical stress differences increased, and the increase in geostress difference was the largest in the wellbore of the old well, and the smaller it was closer to the infill well; affected by the changes in the formation stress state, the expansion law of complex fractures in hydraulic fracturing of infill wells was significantly different from that of old wells. Compared with old wells, the hydraulic fracturing cracks of infill wells are more complex near the wellbore, and the closer to the old wells, the simpler they are.
[0009] Existing technology ⑦ Hu Chunfeng et al. "Analysis of geological and engineering factors of the development effect of normal-pressure shale gas in Nanchuan, eastern Sichuan Basin" (Oil and Gas Reservoir Evaluation and Development, Issue 4, 2021); It is believed that the formation pressure coefficient, the degree of development of natural fracture networks, local complex structures, and geostress are the main factors affecting the development of normal-pressure shale gas. The pressure coefficient characterizes the driving energy of the formation, and a certain degree of natural fracture network can effectively improve the development effect. The local complex structure leads to a low drilling rate of high-quality shale, extrusion deformation, increased burial depth, and increased stress caused by the excessive angle between the horizontal section azimuth and the minimum principal stress azimuth, which will limit the complexity of the artificial fracture network to a certain extent. According to the plane difference distribution characteristics of the main controlling factors, selecting the best from the sweet, local optimization and adjustment are the basis for improving the development effect of normal-pressure shale gas.
[0010] The above literature first explains the importance of engineering factors in optimizing the target layer inside the reservoir; secondly, it discusses the influence of geostress in the development process from the aspects of fracturing effect, production capacity, single well EUR, etc.; finally, it concludes that the spatial distribution law of geostress is one of the important factors that determine the complex fracture morphology and engineering sweet spots of reservoir hydraulic fracturing. However, the above results only analyze geostress from a theoretical level, and do not provide an evaluation model or application method to guide development and production. There are two main defects: (1) There are limitations in the analysis perspective At present, the analysis and research on geostress and engineering sweet spots are mainly concentrated in the preliminary description stage. Neither a unified description model and evaluation system have been established, nor a mature application mode and implementation method have been formed.
[0011] (2) Lack of guidance in field application With regard to reservoir stress characteristics and engineering sweet spots, there is currently no technical means that can be combined with the site and guide oilfield exploration, development and production. Theoretical research has not yet formed a complete system, and there is an urgent need to form an effective working method.
[0012] Therefore, in view of the above shortcomings, a method for optimizing reservoir in-situ stress sweet spots is proposed. Summary of the invention
[0013] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides a method for optimizing reservoir geostress sweet spots, which achieves the effect of assisting sweet spot evaluation through horizontal geostress characteristics; fills the gap in the field of reservoir geostress research; and solves the problem of the lack of theoretical technology in reservoir geostress evaluation in existing conventional reservoir research methods, making it difficult to study the engineering sweet spots inside the reservoir and therefore unable to accurately characterize the advantageous sweet spots of the reservoir.
[0014] (II) Technical solution In order to solve the above problems, the present invention provides a preferred method for reservoir in-situ stress sweet spot, comprising: Step 1: Based on the logging interpretation results, the horizontal maximum principal stress in the ground stress is subtracted from the horizontal minimum principal stress to obtain the stress difference; Step 2: Based on the numerical characteristics of the maximum horizontal principal stress and stress difference of each monitoring point in the target layer, draw frequency distribution histograms respectively; Step 3: Based on the frequency distribution histogram characteristics of the horizontal maximum principal stress and the horizontal principal stress difference, the data distribution type is divided according to the frequency distribution model; Step 4: Optimize the threshold value for the two sets of data, the horizontal maximum principal stress and the horizontal principal stress difference, according to the corresponding frequency distribution model types; Step 5: Draw a stress coordinate system based on the data of all monitoring points in the target layer segment, with the horizontal maximum principal stress as the horizontal axis and the horizontal principal stress difference as the vertical axis; Step 6: In the stress coordinate system, circle the distribution range of all data points and draw the outer boundary rectangle; Step 7: In the stress coordinate system, draw the inner partition diamond according to the threshold value obtained in step 4; Step 8: In the stress coordinate system, select the best stress scatter point according to the superimposed partition model; Step 9: Establish a stress optimization formula based on the distribution characteristics of the optimal stress scatter points in the stress coordinate system; Step 10: Based on the stress optimization formula and the maximum horizontal principal stress, a stress difference evaluation benchmark curve is established to optimize the ground stress sweet spot. In this comparison process, the area where the horizontal principal stress difference is less than the benchmark curve is the optimal stress sweet spot.
[0015] Preferably, the frequency distribution model in step 2 is divided into three types: multi-peak continuous type, single-peak continuous type and multi-level step type.
[0016] Preferably, the threshold in step 4 needs to follow the principle of relative value selection, first peak positioning and proportion.
[0017] Preferably, the horizontal axis of the stress coordinate system in step five is the horizontal maximum principal stress, and the vertical axis is the horizontal principal stress difference; the stress coordinate system only intuitively responds to the data, and does not mean that the horizontal principal stress difference is a function of the horizontal maximum principal stress, and there is no clear logical connection between the horizontal and vertical coordinate data.
[0018] Preferably, the principle of drawing the outer boundary rectangle in step six is to enclose all the scattered data points together by a rectangle in the stress coordinate system, and the sides of the rectangle are parallel to the coordinate axis and are located in the coordinate system in a positive state to establish an outer boundary rectangle, which will provide a basis for further data optimization.
[0019] Preferably, in step 7, when drawing the inner partition rhombus, in the stress coordinate system, the selected threshold limits are connected by the rhombus, and the two vertices of the rhombus are (x 1 , y 1 ) and (x 2 , y 2 ). 1 is the horizontal principal stress difference threshold, x 1 is the median value of the maximum horizontal principal stress, y 2 is the median value of the horizontal principal stress difference, x 2 is the horizontal maximum principal stress threshold.
[0020] Preferably, in the superimposed partition model described in step eight, in the stress coordinate system, the outer boundary rectangle and the inner partition diamond divide all data points into 9 regions, and the characteristics of these nine regions are described by the horizontal maximum principal stress and the horizontal principal stress difference of three levels: low, medium and high.
[0021] Preferably, the stress optimization formula in step nine is a linear formula with the horizontal maximum principal stress as the independent variable and the stress difference as the dependent variable, and the formula is: (1) (2) (3).
[0022] (III) Beneficial effects The optimization method for reservoir in-situ stress sweet spots provided by the present invention utilizes the spatial distribution characteristics of in-situ stress, based on the frequency distribution model, stress coordinate system, and superimposed partition model, and determines the target location of the stress sweet spot through stress optimization formula and comparative evaluation. This solves the problem that the existing conventional reservoir research methods lack theoretical technology in reservoir in-situ stress evaluation, making it difficult to study the engineering sweet spots inside the reservoir, and therefore unable to accurately characterize the advantageous sweet spots of the reservoir. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] Figure 1 It is a technical flow chart of the method of the present invention; Figure 2 It is a schematic diagram of multi-modal continuous data type in the frequency distribution model of the method of the present invention; Figure 3 It is a schematic diagram of the unimodal continuous data type in the frequency distribution model of the method of the present invention.
[0024] Figure 4 The figure is a schematic diagram of the multi-level ladder data type in the frequency distribution model of the method of the present invention.
[0025] Figure 5 It is a schematic diagram of the stress coordinate system, outer boundary rectangle and inner partition diamond of the method of the present invention; Figure 6 It is a schematic diagram of the superimposed partition model of the method of the present invention; Figure 7 is a frequency distribution histogram of the maximum principal stress of the JHF well in the embodiment of the present invention; Figure 8 is a frequency distribution histogram of horizontal principal stress difference of the JHF well in an embodiment of the present invention; Fig. 9 is a JHF well stress coordinate system diagram of an embodiment of the present invention; Fig.10 1 is a comparison diagram of the JHF well stress optimization formula operation curve according to an embodiment of the present invention; Fig.11 It is a schematic diagram of JHF well ground stress sweet spot evaluation according to an embodiment of the present invention. Implementation
[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0027] In the description of the present invention, it is necessary to understand that the orientations or positional relationships indicated by “upper”, “lower”, “inside”, “outside”, “top”, “bottom”, etc. are all based on the orientations or positional relationships shown in the accompanying drawings. The purpose is only to facilitate the description of the present invention and simplify the description. It does not indicate or imply that the referred parts must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.
[0028] like Figure 1-6 As shown, the present invention provides a preferred method for reservoir in-situ stress sweet spot, specifically comprising: Step 1: Based on the logging interpretation results, the horizontal maximum principal stress in the ground stress is subtracted from the horizontal minimum principal stress to obtain the stress difference; Step 2: Based on the numerical characteristics of the maximum horizontal principal stress and stress difference of each monitoring point in the target layer, draw frequency distribution histograms respectively; like Figure 2-4 As shown, the frequency distribution model described in step 2 is divided into three types: multi-peak continuous type, single-peak continuous type and multi-level ladder type; Among them, the multi-peak continuous type reflects that the data as a whole is in a multi-cycle fluctuation state, the high-frequency numbers correspond to different and discontinuous data distribution intervals, the frequency presents a multi-level gradual continuous distribution characteristic, and the overall data distribution is dispersed; the unimodal continuous type reflects that the data as a whole is in a single-cycle fluctuation state, the high-frequency numbers often correspond to the median of the data interval, the frequency presents a single-level gradual continuous distribution characteristic, and the overall data distribution is concentrated; the multi-level step type reflects that in some adjacent or non-adjacent data intervals, the frequency distribution is near a certain value, and for another part of the data interval, the frequency distribution is near another value, the frequency presents a mutation characteristic, and the overall data distribution is uniform.
[0029] Step 3: Based on the frequency distribution histogram characteristics of the horizontal maximum principal stress and the horizontal principal stress difference, the data distribution type is divided according to the frequency distribution model; Step 4: Optimize the threshold value for the two sets of data, the horizontal maximum principal stress and the horizontal principal stress difference, according to the corresponding frequency distribution model types; In the present invention, the optimization method of the threshold value in step 4 mainly follows the following three principles: relative value selection, first peak positioning, and proportion consideration.
[0030] Among them, the relative value selection means that the threshold optimization process is to select a relatively favorable value within a single well, rather than an absolutely favorable value within the entire study area. Therefore, the threshold will change according to the relative characteristics of the single well; the first peak positioning means that when the frequency fluctuates with multiple peak values, the data interval corresponding to the first peak or the last peak can be directly selected as the threshold; the proportion consideration means that in order to meet production needs, the selected data volume needs to reach a certain proportion, and the threshold will be adjusted in this process.
[0031] Step 5: Draw a stress coordinate system based on the data of all monitoring points in the target layer segment, with the horizontal maximum principal stress as the horizontal axis and the horizontal principal stress difference as the vertical axis; like Figure 5 As shown in the figure, the horizontal axis of the stress coordinate system described in step 5 is the maximum horizontal principal stress, and the vertical axis is the horizontal principal stress difference. The "stress coordinate system" is just an intuitive response to the data, and does not mean that the horizontal principal stress difference is a function of the maximum horizontal principal stress. There is no clear logical connection between the horizontal and vertical coordinate data.
[0032] Step 6: In the stress coordinate system, circle the distribution range of all data points and draw the outer boundary rectangle; The outer boundary rectangle described in step six is in the stress coordinate system. All the scattered data points are enclosed together by the rectangle. The sides of the rectangle are parallel to the coordinate axis and are located in the coordinate system in a positive state. The outer boundary rectangle is established, and the outer boundary rectangle will provide a basis for further data optimization.
[0033] Step 7: In the stress coordinate system, draw the inner partition diamond according to the threshold value obtained in step 4; In step 7, the process of drawing the inner partition diamond is to connect the selected threshold limits through the diamond in the stress coordinate system, and the two vertices of the diamond are (x 1 , y 1 ) and (x 2 , y 2 ). 1 is the horizontal principal stress difference threshold, x 1 is the median value of the maximum horizontal principal stress, y 2 is the median value of the horizontal principal stress difference, x 2 is the horizontal maximum principal stress threshold. Figure 5 Shown as a dashed diamond.
[0034] Step 8: In the stress coordinate system, select the best stress scatter point according to the superimposed partition model; like Figure 6The figure shows the schematic diagram of the superimposed partition model in step eight. In the stress coordinate system, the outer boundary rectangle and the inner partition diamond divide all data points into 9 regions. The characteristics of these nine regions are described by the horizontal maximum principal stress and horizontal principal stress difference of three levels: low, medium and high: ① medium stress, medium difference, ② medium stress, high difference, ③ high stress, medium difference, ④ high stress, medium difference, ⑤ medium stress, low difference, ⑥ medium stress, low difference, ⑦ low stress, medium difference, ⑧ low stress, medium difference, ⑨ medium stress, high difference. During the reservoir fracturing process, cracks tend to expand from high stress areas to low stress areas; at the same time, the smaller the stress difference, the easier it is to form a network of cracks. Therefore, the ④ and ⑤ regions are preferred in the superimposed partition model.
[0035] Step 9: Establish a stress optimization formula based on the distribution characteristics of the optimal stress scatter points in the stress coordinate system; In the present invention, the stress data points selected by the "superimposed partition model" need to be converted into corresponding depth data to facilitate further sweet spot evaluation in a single well. However, since the "stress coordinate system" does not contain depth data, this conversion process needs to be completed through the "stress optimization formula". The "stress optimization formula" is a linear formula with the horizontal maximum principal stress as the independent variable and the stress difference as the dependent variable. Below the "stress optimization formula" in the "superimposed partition model" are two areas No. ④ and No. ⑤. The "stress optimization formula" is controlled by a threshold, and its formula is as follows: (1) (2) (3).
[0036] The "stress optimization formula" is the mathematical representation of the two regions ④ and ⑤ in the superimposed partition model. The two regions ④ and ⑤ are a pentagonal region controlled by five edges: First edge: the bottom vertex of the inner partition diamond (x 1 , y 1 ) and the perpendicular line to the bottom edge of the outer bounding rectangle. The first edge is x=x 1 , which limits the minimum value of x (maximum principal stress); Second side: the bottom vertex of the inner partition diamond (x 2 , y 2 ) and the right side of the outer bounding rectangle. The second side is y=y 2 , which limits the maximum value of y (stress difference); The third side: the bottom vertex of the inner partition diamond (x 1 , y 1 ) and the right vertex (x 2 , y 2) defines the maximum value of y (stress difference) as x (maximum principal stress) changes; The fourth and fifth edges: the bottom edge of the outer boundary rectangle and the right edge of the outer boundary rectangle. Since there are no data points outside the outer boundary rectangle, these two edges can be ignored.
[0037] Therefore, the stress optimization formula only needs to represent the first, second, and third edges, and consists of three formulas. According to the restriction condition of the first edge, it is expressed by the formula: (1) According to the restriction condition of the second edge, it is expressed by the formula: (2) According to the restriction condition of the third side, the formula is expressed as the line connecting the two points (x1, y1) and (x2, y2), that is, the stress difference calibration formula, which is expressed by the formula: (3).
[0038] Step 10: Through the stress optimization formula, based on the maximum horizontal principal stress, a stress difference evaluation benchmark curve is established to perform optimal evaluation of the ground stress sweet spot. In this comparison process, the area where the horizontal principal stress difference is less than the benchmark curve is the optimal stress sweet spot.
[0039] When drawing the stress difference evaluation benchmark curve, firstly, the data points that do not meet the requirements in the range are deleted according to formula (1) and formula (2), and then the horizontal coordinate values of the remaining points are substituted into formula (3) to calculate the upper limit value of Y (stress difference) corresponding to X (maximum principal stress) of each point, that is, the stress difference point benchmark value. The stress difference point benchmark value is plotted according to the depth to obtain the stress difference benchmark curve.
[0040] After obtaining the stress difference benchmark curve, it is compared with the stress difference curve. If the stress difference is less than the stress difference benchmark value, that is, the stress difference curve is below the stress difference benchmark curve, then the corresponding depth point meets the requirements of the ground stress sweet spot and is the optimal ground stress sweet spot area. Example
[0041] like Figure 7-11 As shown, taking the JHF well in Songliao Basin as an example, the application process of the present invention is explained.
[0042] Step 1: Calculate the horizontal principal stress difference at each location of the JHF well based on the logging interpretation results.
[0043] Step 2: Draw the frequency distribution histogram of the maximum horizontal principal stress and the horizontal principal stress difference of the JHF well. Figure 7 Figure 8 shown.
[0044] Step 3: According to the data distribution characteristics of the frequency distribution histogram, the type of data is judged. Both the horizontal maximum principal stress and the horizontal principal stress difference data belong to the multi-peak continuous type.
[0045] Step 4: For the two sets of data, the horizontal maximum principal stress and the horizontal principal stress difference, the threshold is optimized according to the corresponding frequency distribution model type. The horizontal maximum principal stress threshold is selected as 48MPa ( Figure 7 ), the horizontal principal stress difference threshold is 0.5MPa ( Figure 8 ).
[0046] Step 5: Fig. 9 As shown in the figure, a stress coordinate system is established with the maximum horizontal principal stress of the JHF well as the x-axis and the horizontal principal stress difference as the y-axis.
[0047] Step 6. In the stress coordinate system, circle the distribution range of all data points and draw an outer boundary rectangle. The four vertices of the outer boundary rectangle are: (43, 1), (43, 2), (51, 1), and (51, 2).
[0048] Step 7. In the stress coordinate system, draw an inner partition diamond according to the threshold value obtained in step 4. The four vertices of the inner partition diamond are: (47, 0.5), (48, 1), (47, 1.5), and (46, 1).
[0049] Step 8: In the stress coordinate system, according to the superimposed partition model, the optimal stress scatter point is selected, and the Figure 6 The area corresponding to areas ④ and ⑤ in the middle.
[0050] Step 9: Based on the superimposed zoning model, the data of JHF well are substituted into formulas (1), (2), and (3) to establish the stress optimization formula of JHF well, which is as follows: (4) (5) (6).
[0051] Step 10: The optimal formula for overstress is to establish a stress difference evaluation benchmark curve based on the maximum horizontal principal stress, and to conduct optimal evaluation of the ground stress sweet spot. In this comparison process, the area where the horizontal principal stress difference is less than the benchmark curve is the optimal stress sweet spot.
[0052] By using the JHF well stress optimization formula, taking the horizontal maximum principal stress as the independent variable, the following is obtained: Fig.10 The horizontal principal stress difference curve is shown.
[0053] The area where the horizontal principal stress difference is less than the benchmark curve is the optimal stress scatter point area. The two curves retain the part where the numerical relationship is consistent, and finally the optimal curve for evaluating the sweet spot of geostress engineering is obtained as follows: Fig.11 As shown, the optimization of the in-situ stress sweet spot of the JHF well is completed.
[0054] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.
Claims
1. A method for optimizing reservoir in-situ stress sweet spots, It is characterized in that include: Step 1: Based on the logging interpretation results, the horizontal maximum principal stress in the ground stress is subtracted from the horizontal minimum principal stress to obtain the stress difference; Step 2: Based on the numerical characteristics of the maximum horizontal principal stress and stress difference of each monitoring point in the target layer, draw frequency distribution histograms respectively; Step 3: Based on the frequency distribution histogram characteristics of the horizontal maximum principal stress and the horizontal principal stress difference, the data distribution type is divided according to the frequency distribution model; Step 4: Optimize the threshold value for the two sets of data, the horizontal maximum principal stress and the horizontal principal stress difference, according to the corresponding frequency distribution model types; Step 5: Draw a stress coordinate system based on the data of all monitoring points in the target layer segment, with the horizontal maximum principal stress as the horizontal axis and the horizontal principal stress difference as the vertical axis; Step 6: In the stress coordinate system, circle the distribution range of all data points and draw the outer boundary rectangle; Step 7: In the stress coordinate system, draw the inner partition diamond according to the threshold value obtained in step 4; Step 8: In the stress coordinate system, select the best stress scatter point according to the superimposed partition model; Step 9: Establish a stress optimization formula based on the distribution characteristics of the optimal stress scatter points in the stress coordinate system; Step 10: Based on the stress optimization formula and the maximum horizontal principal stress, a stress difference evaluation benchmark curve is established to optimize the ground stress sweet spot. In this comparison process, the area where the horizontal principal stress difference is less than the benchmark curve is the optimal stress sweet spot.
2. The preferred method for reservoir in-situ stress sweet spot according to claim 1, It is characterized in that The frequency distribution model in step 2 is divided into three types: multi-peak continuous type, single-peak continuous type and multi-level step type.
3. The preferred method for reservoir in-situ stress sweet spot according to claim 1, It is characterized in that The threshold optimization in step 4 needs to follow the principle of relative value selection, first peak positioning and proportion consideration.
4. The preferred method for reservoir in-situ stress sweet spot according to claim 1, It is characterized in that In the step 5, the horizontal axis of the stress coordinate system is the maximum horizontal principal stress, and the vertical axis is the horizontal principal stress difference; the stress coordinate system is only an intuitive response to the data, and does not mean that the horizontal principal stress difference is a function of the maximum horizontal principal stress. There is no clear logical connection between the horizontal and vertical coordinate data.
5. The preferred method for reservoir in-situ stress sweet spot according to claim 1, It is characterized in that The principle of drawing the outer boundary rectangle in step six is to enclose all the scattered data points together in the stress coordinate system through a rectangle, and the sides of the rectangle are parallel to the coordinate axis and are located in the coordinate system in a positive state to establish an outer boundary rectangle, which will provide a basis for further data optimization.
6. The preferred method for reservoir in-situ stress sweet spot according to claim 1, It is characterized in that In step 7, when drawing the inner partition diamond, in the stress coordinate system, the selected threshold limits are connected by the diamond, and the two vertices of the diamond are (x 1 , y 1 ) and (x 2 , y 2 ). 1 is the horizontal principal stress difference threshold, x 1 is the median value of the maximum horizontal principal stress, y 2 is the median value of the horizontal principal stress difference, x 2 is the maximum horizontal principal stress threshold.
7. The preferred method for reservoir in-situ stress sweet spot according to claim 1, It is characterized in that In the superimposed partition model described in step eight, in the stress coordinate system, the outer boundary rectangle and the inner partition diamond divide all data points into 9 regions, and describe the characteristics of these nine regions by the horizontal maximum principal stress and horizontal principal stress difference of three levels: low, medium and high.
8. The preferred method for reservoir in-situ stress sweet spot according to claim 1, It is characterized in that The stress optimization formula in step nine is a linear formula with the horizontal maximum principal stress as the independent variable and the stress difference as the dependent variable, and its formula is: （1） （2） （3）。