Optimization method, device, electronic equipment and storage medium for reservoir geological model

Abstract

The present disclosure provides an optimization method, device, electronic device and storage medium for a reservoir geological model, the method comprising determining the well control boundary of the reservoir geological model; calculating the well control reserves within the well control boundary range, and determining the influencing factors of the well control reserves within the well control boundary range; selecting a corresponding optimization method to optimize the reservoir geological model according to the influencing factors of the well control reserves within the well control boundary range. The method optimizes the reservoir geological model locally, so that the well control reserves of the optimized reservoir geological model are closer to the target well control reserves calculated based on dynamic data, thereby improving the precision and accuracy of the reservoir geological model and optimizing the simulation effect. Moreover, the method optimizes the volume parameters and / or attribute parameters of different types of reservoir bodies while keeping the original reservoir body morphology unchanged, and has high flexibility and strong operability.

Description

Technical Field

[0001] The present disclosure relates to the technical field of oil exploration and development, and in particular to an optimization method, device, electronic equipment and storage medium for an oil reservoir geological model. Background Art

[0002] Fracture-cavity reservoirs are highly discrete reservoirs. As a special type of petroleum reservoir, fracture-cavity reservoirs have extremely strong heterogeneity, and their reservoir space types are mainly large caves, dissolution pores and fractures of different scales. Due to the strong heterogeneity and diversity of reservoir space of fracture-cavity reservoirs, it is still a technical problem to establish a reservoir geological model that conforms to the geological laws of reservoir development and meets the needs of reservoir numerical simulation. One of the prominent contradictions is the inconsistency between the reservoir geological model and the actual production dynamic data during numerical simulation, which affects the effect of historical fitting.

[0003] At present, the research on dynamic data constraint modeling is mostly aimed at simple clastic reservoirs, which are not applicable to complex fracture-cavity carbonate reservoirs, and the purpose of these studies is mostly to fit the production pressure data. Therefore, in the actual production of fracture-cavity carbonate reservoirs, it is often the case that the well-controlled reserves of individual wells in the reservoir geological model are quite different from the dynamic reserves calculated based on dynamic data. Therefore, the current simulation of well-controlled reserves is still lacking, and the accuracy and precision of the reservoir geological model used are poor, resulting in poor numerical simulation results. Summary of the invention

[0004] In response to the above problems, the present disclosure provides an optimization method, device, electronic device and storage medium for an oil reservoir geological model, which solves the problems in the prior art that the well-controlled reserves and dynamic reserves of the oil reservoir geological model are quite different, and the accuracy and precision of the oil reservoir geological model are poor, resulting in poor numerical simulation results.

[0005] In a first aspect, the present disclosure provides a method for optimizing a reservoir geological model, the method comprising:

[0006] Step S1: determining the well control boundary of the reservoir geological model;

[0007] Step S2: calculating the well controlled reserves within the well controlled boundary range, and determining the influencing factors of the well controlled reserves within the well controlled boundary range;

[0008] Step S3: According to the influencing factors of the well-controlled reserves within the well-controlled boundary, a corresponding optimization method is selected to optimize the reservoir geological model.

[0009] According to an embodiment of the present disclosure, optionally, in the above-mentioned reservoir geological model optimization method, step S1 includes the following steps:

[0010] Step S11: determining adjacent wells within a first preset range near the target well of the reservoir geological model, and sequentially connecting the midpoints of the lines connecting the target well and each of the adjacent wells to form a closed polygon as a first boundary;

[0011] Step S12: determining all faults within the first boundary, and correcting the first boundary by taking the faults whose distance from the target well is greater than a preset distance as part of the boundary line, thereby obtaining a second boundary;

[0012] Step S13: determining whether the target well is an acid-fractured well, and when the target well is an acid-fractured well and its fracturing range exceeds the second boundary, correcting the second boundary to obtain a third boundary and making the fracturing range within the third boundary;

[0013] Step S14: determining the positional relationship between the third boundary and the isolated cave in the reservoir geological model, and when the third boundary passes through the isolated cave and the isolated cave belongs to the reservoir body, correcting the third boundary to obtain a fourth boundary and make the isolated cave located within the range of the fourth boundary;

[0014] Step S15: Determine the pressure propagation range boundary of the target well based on the pressure data of the target well during the production process. When the pressure propagation range boundary is within the fourth boundary range, use the pressure propagation range boundary as a part of the boundary line to correct the fourth boundary, thereby obtaining the final well control boundary.

[0015] According to an embodiment of the present disclosure, optionally, in the above-mentioned method for optimizing a reservoir geological model, the reservoir geological model includes a three-dimensional orthogonal grid system, and the step of calculating the well-controlled reserves within the well-controlled boundary range includes the following steps:

[0016] Step S21: gridding all caves within the well control boundary according to the three-dimensional orthogonal grid system to obtain a plurality of grids;

[0017] Step S22: calculating and accumulating the oil-containing volumes in all squares one by one to obtain the oil-containing volume within the well control boundary;

[0018] Step S23: converting the oil-containing volume within the well control boundary range into the well control reserves within the well control boundary range by combining the surface crude oil density and the surface crude oil volume coefficient.

[0019] According to an embodiment of the present disclosure, optionally, in the above-mentioned method for optimizing the reservoir geological model, the well-controlled reserves within the well-controlled boundary range are calculated according to the following formula:

[0020]

[0021] Wherein, N0 is the well-controlled reserves within the well-controlled boundary;

[0022] J is the total number of squares within the well control boundary;

[0023] V B (k) is the volume of the kth square;

[0024] N TG (k) is the net-to-gross ratio of the kth square;

[0025] φ(k) is the porosity of the kth square;

[0026] S0(k) is the oil saturation of the kth square;

[0027] B0 is the volume coefficient of surface crude oil;

[0028] ρ0 is the density of crude oil on the ground.

[0029] According to an embodiment of the present disclosure, optionally, in the above-mentioned method for optimizing the reservoir geological model, in step S3,

[0030] When the influencing factor of the well controlled reserves within the well controlled boundary range is a volume parameter, the optimization method is a volume optimization method;

[0031] When the influencing factor of the well-controlled reserves within the well-controlled boundary is an attribute parameter, the optimization method is an attribute optimization method;

[0032] When the influencing factors of the well-controlled reserves within the well-controlled boundary range include both volume parameters and attribute parameters, the optimization method is a method combining the volume optimization method and the attribute optimization method.

[0033] According to an embodiment of the present disclosure, optionally, in the above-mentioned reservoir geological model optimization method, in step S3, when the influencing factor of the well-controlled reserves within the well-controlled boundary range is a volume parameter, a volume optimization method is selected to optimize the reservoir geological model, comprising the following steps:

[0034] Step S311: taking a cave located within the well control boundary and whose reserves are affected by volume parameters as a first cave to be optimized;

[0035] Step S312: disturbing the volume of each first cave respectively, recalculating the well controlled reserves within the well controlled boundary after each disturbance and calculating the difference between the well controlled reserves and the well controlled reserves target value to obtain a first difference, and comparing the absolute value of the first difference with a first preset end threshold;

[0036] Step S313: when the absolute value of the first difference of the volume of at least one first cave after the disturbance is less than the first preset end threshold, the disturbance is terminated and the optimized reservoir geological model is obtained;

[0037] Step S314: When the absolute values ​​of the first differences in the volumes of all the first caves after being disturbed are greater than or equal to the first preset end threshold, return to step S312 to disturb the volumes of the first caves again.

[0038] According to an embodiment of the present disclosure, optionally, in the above-mentioned method for optimizing the reservoir geological model, in step S312, the volume of each first cave is disturbed respectively, comprising the following steps:

[0039] The volume of each first cave is uniformly expanded outward or reduced inward by a layer of a square size while keeping the shape of the first cave unchanged;

[0040] Confirm the attribute parameters of the first cave; wherein, for the first cave with an expanded volume, the attribute parameters of the expanded part of the grid are the average value of the corresponding attribute parameters of the surrounding grids, so that the attribute parameters of the expanded first cave are gradually transitioned in space; for the first cave with a reduced volume, the attribute parameters of each grid remain unchanged.

[0041] According to an embodiment of the present disclosure, optionally, in the above-mentioned reservoir geological model optimization method, in step S3, when the influencing factor of the well-controlled reserves within the well-controlled boundary range is an attribute parameter, selecting an attribute optimization method to optimize the reservoir geological model includes the following steps:

[0042] Step S321: taking a cave located within the well control boundary and whose reserves are affected by attribute parameters as a second cave to be optimized;

[0043] Step S322: disturbing the attribute parameters of all second caves simultaneously, recalculating the well-controlled reserves within the well-controlled boundary after each disturbance, and analyzing the difference between the well-controlled reserves and the well-controlled reserves target value to obtain a second difference;

[0044] Step S323: accept or reject the current disturbance according to the comparison result between the absolute value of the second difference after the current disturbance and the absolute value of the second difference after the previous disturbance; wherein, when the absolute value of the second difference after the current disturbance is less than the absolute value of the second difference after the previous disturbance, accept the current disturbance, and store the disturbance factor and the corresponding second difference used in the current disturbance process as the latest disturbance factor and the latest second difference;

[0045] Step S324: confirming the current disturbance number and comparing it with the preset disturbance number;

[0046] Step S325: when the current disturbance number is greater than or equal to the preset disturbance number, the disturbance is terminated and the reservoir geological model corresponding to the latest disturbance factor currently stored is used as the optimized reservoir geological model;

[0047] Step S326: When the current disturbance number is less than the preset disturbance number, compare the absolute value of the latest second difference currently stored with the second preset end threshold value:

[0048] When the absolute value of the latest second difference currently stored is less than the second preset end threshold, the disturbance is ended and the reservoir geological model corresponding to the latest disturbance factor currently stored is used as the optimized reservoir geological model;

[0049] When the absolute value of the latest second difference currently stored is greater than or equal to the second preset end threshold, return to step S322 to perform the next disturbance on the attribute parameters of all second caves.

[0050] According to an embodiment of the present disclosure, optionally, in the above-mentioned method for optimizing the reservoir geological model, in the step S322, when the second cave is an empty cave and an undrilled cave, the attribute parameters of all the second caves are disturbed simultaneously, including the following steps:

[0051] Multiply the attribute parameters of all the squares of all the second caves by the corresponding disturbance factors at the same time, so as to change the attribute parameters of all the second caves proportionally;

[0052] The disturbance factor is a constant and is 0.8 to 1.2 times the ratio of the well-controlled reserves target value to the well-controlled reserves before optimization.

[0053] According to an embodiment of the present disclosure, optionally, in the above-mentioned method for optimizing the reservoir geological model, in the step S322, when the second cave is a non-emptied cave encountered during drilling, the attribute parameters of all the second caves are simultaneously disturbed, including the following steps:

[0054] Multiplying the attribute parameters of all the squares of the second cave by the corresponding disturbance factor at the same time, so as to change the attribute parameters of the second cave proportionally;

[0055] Among them, the disturbance factor of the second cave located at the target well position is 1, the disturbance factor of the second cave located within the second preset range near the target well decreases as the distance between the second cave and the target well increases, and the disturbance factor of the second cave located outside the second preset range near the target well is a constant and less than 1.

[0056] According to an embodiment of the present disclosure, optionally, in the above-mentioned reservoir geological model optimization method, in the step S3, when the influencing factors of the well-controlled reserves within the well-controlled boundary range include volume parameters and attribute parameters, a method combining the volume optimization method and the attribute optimization method is selected to optimize the reservoir geological model, including the following steps:

[0057] Step S331: taking a cave located within the well control boundary and whose reserves are jointly affected by volume parameters and attribute parameters as a third cave to be optimized;

[0058] Step S332: optimizing the third cave by a volume optimization method and calculating the change value of the well-controlled reserves within the well-controlled boundary before and after the volume optimization to obtain a first change value of the well-controlled reserves;

[0059] Step S333: Optimize the third cave by the attribute optimization method and calculate the change value of the well controlled reserves within the well controlled boundary before and after the attribute optimization to obtain a second change value of the well controlled reserves; wherein the second change value of the well controlled reserves is equal to the difference between the expected value of the well controlled reserves change value and the first change value of the well controlled reserves, and the expected value of the well controlled reserves change value is the difference between the well controlled reserves target value and the well controlled reserves before volume optimization.

[0060] In a second aspect, the present disclosure provides an optimization device for an oil reservoir geological model, the device comprising:

[0061] A boundary determination module is used to determine the well control boundary of the reservoir geological model;

[0062] A reserve calculation and influencing factor determination module, used to calculate the well-controlled reserves within the well-controlled boundary range and determine the influencing factors of the well-controlled reserves within the well-controlled boundary range;

[0063] The model optimization module is used to select a corresponding optimization method to optimize the reservoir geological model according to the influencing factors of the well controlled reserves within the well controlled boundary range.

[0064] In a third aspect, the present disclosure provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for optimizing a reservoir geological model as described in any one of the first aspects is performed.

[0065] In a fourth aspect, the present disclosure provides a storage medium, wherein a computer program stored in the storage medium can be executed by one or more processors and can be used to implement the method for optimizing a reservoir geological model as described in any one of the first aspects.

[0066] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects:

[0067] The present disclosure provides an optimization method, device, electronic device and storage medium for a reservoir geological model, the method comprising determining the well control boundary of the reservoir geological model; calculating the well control reserves within the well control boundary range, and determining the influencing factors of the well control reserves within the well control boundary range; selecting a corresponding optimization method to optimize the reservoir geological model according to the influencing factors of the well control reserves within the well control boundary range. The method optimizes the reservoir geological model locally, so that the well control reserves of the optimized reservoir geological model are closer to the target well control reserves calculated based on dynamic data, thereby improving the precision and accuracy of the reservoir geological model and optimizing the simulation effect. Moreover, the method optimizes the volume parameters and / or attribute parameters of different types of reservoir bodies while keeping the original reservoir body morphology unchanged, and has high flexibility and strong operability. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0069] Figure 1 A schematic diagram of a flow chart of a method for optimizing a reservoir geological model provided in an embodiment of the present disclosure;

[0070] Figure 2 A schematic diagram of a well control boundary provided in an embodiment of the present disclosure;

[0071] Figure 3 A schematic diagram of a PDA double logarithmic curve of a target well TA1 provided in an embodiment of the present disclosure;

[0072] Figure 4 A schematic diagram of the pressure propagation range boundary of a target well TA1 provided in an embodiment of the present disclosure;

[0073] Figure 5 A schematic diagram of cave volume disturbance provided by an embodiment of the present disclosure;

[0074] Figure 6 A schematic diagram of a flow chart of optimizing a reservoir geological model by selecting a volume optimization method provided in an embodiment of the present disclosure;

[0075] Figure 7 A schematic diagram of a flow chart of optimizing a reservoir geological model by selecting an attribute optimization method provided in an embodiment of the present disclosure;

[0076] Figure 8 A schematic diagram of porosity disturbance of an emptied and undrilled cave provided in an embodiment of the present disclosure;

[0077] Fig. 9 A schematic diagram of porosity disturbance of a non-emptied cave encountered in drilling provided by an embodiment of the present disclosure;

[0078] Fig.10 A schematic diagram of a unit reservoir geological model in the Tahe area is provided for the embodiment of the present disclosure;

[0079] Fig.11 A schematic diagram of a well control boundary based on a unit reservoir geological model of the Tahe area provided in an embodiment of the present disclosure;

[0080] Fig.12 A schematic diagram of the planar distribution of reservoir bodies within the well control boundary range before optimization based on a certain unit reservoir geological model in the Tahe area provided in an embodiment of the present disclosure;

[0081] Fig.13 A schematic diagram of the spatial distribution of reservoirs within the well control boundary before optimization based on a certain unit reservoir geological model in the Tahe area provided in an embodiment of the present disclosure;

[0082] Fig.14 A schematic diagram of the planar distribution of reservoir bodies within the well control boundary after optimization of a certain unit reservoir geological model in the Tahe area provided in an embodiment of the present disclosure;

[0083] Fig.15 A schematic diagram of the spatial distribution of reservoirs within the well control boundary after optimization of a unit reservoir geological model in the Tahe area provided in an embodiment of the present disclosure;

[0084] Fig.16 A schematic diagram of the structure of an optimization device for a reservoir geological model provided in an embodiment of the present disclosure;

[0085] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0086] The following will describe the implementation methods of the present disclosure in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present disclosure applies technical means to solve technical problems and achieve corresponding technical effects can be fully understood and implemented accordingly. The embodiments of the present disclosure and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the protection scope of the present disclosure.

[0087] Meanwhile, in the following description, many specific details are set forth for the purpose of explanation to provide a thorough understanding of the embodiments of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without the specific details or the specific manner described herein.

[0088] Embodiment 1

[0089] Figure 1 For a flow chart of a reservoir geological model optimization method provided in an embodiment of the present disclosure, please refer to Figure 1This embodiment provides a method for optimizing a reservoir geological model, comprising:

[0090] Step S1: Determine the well control boundary of the reservoir geological model.

[0091] Specifically, step S1 includes the following steps:

[0092] Step S11: determining adjacent wells within a first preset range near the target well of the reservoir geological model, and sequentially connecting the midpoints of the lines connecting the target well and each of the adjacent wells to form a closed polygon as a first boundary;

[0093] Step S12: determining all faults within the first boundary, and correcting the first boundary by taking the faults whose distance from the target well is greater than a preset distance as part of the boundary line, thereby obtaining a second boundary;

[0094] Step S13: determining whether the target well is an acid-fractured well, and when the target well is an acid-fractured well and its fracturing range exceeds the second boundary, correcting the second boundary to obtain a third boundary and making the fracturing range within the third boundary;

[0095] Step S14: determining the positional relationship between the third boundary and the isolated cave in the reservoir geological model, and when the third boundary passes through the isolated cave and the isolated cave belongs to the reservoir body, correcting the third boundary to obtain a fourth boundary and make the isolated cave located within the range of the fourth boundary;

[0096] Step S15: Determine the pressure propagation range boundary of the target well based on the pressure data of the target well during the production process. When the pressure propagation range boundary is within the fourth boundary range, use the pressure propagation range boundary as a part of the boundary line to correct the fourth boundary, thereby obtaining the final well control boundary.

[0097] It should be noted that if Figure 2 As shown, when the pressure propagation range boundary is outside, the pressure propagation problem of the target well is not considered, and the fourth boundary is used as the final well control boundary. If only part of the pressure propagation range boundary is within the fourth boundary range, only the part of the pressure propagation range boundary within the fourth boundary range is used as part of the boundary line to correct the fourth boundary to obtain the final well control boundary. Fracture-cavity carbonate reservoirs are severely heterogeneous, and the scale and distribution direction of the reservoir are highly uncertain. When determining the well control boundary, not only the distance and connectivity between the single well (the target well) and the adjacent well should be considered, but also factors such as the shielding of the fault and the connectivity of the isolated cave should be considered.

[0098] In general, the first preset range is usually 1.5 times the average well spacing. For acid fracturing wells, it is considered that the fracture reservoirs within the fracturing range are all connected, so the final well control boundary must include the fractures within the fracturing range.

[0099] What is obtained before step S15 is a static boundary, and what is obtained in step S15 is a dynamic boundary.

[0100] As the oil well production proceeds, the pressure wave continues to propagate outward. The propagation of the pressure wave has a certain range. Only the oil reservoir reserves within the pressure propagation range belong to the well-controlled reserves of the target well, and the oil reservoirs outside the pressure propagation range do not belong to the well-controlled reserves of the target well. The pressure propagation range boundary is a closed boundary. In this embodiment, taking the square pressure propagation range boundary as an example, according to the pressure data of the target well during the production process, the characteristics of the pressure propagation to each boundary and its propagation time can be obtained. According to this time period, the distance between the target well and a certain boundary of the pressure propagation range can be determined, and then the distances between the four boundaries of the closed boundary and the target well are determined in turn. The specific process of determining the pressure propagation range boundary is as follows: the double logarithmic curve of the PDA (Production Data Analysis) of the target well is used to perform an edge detection test method to determine the pressure propagation range under quasi-steady-state conditions. When the pressure propagates to the propagation range boundary, a boundary feature will appear on the PDA curve, and then the position of the propagation range boundary (i.e., the distance from the target well) is determined according to the distance from the target well corresponding to the time period of the boundary feature.

[0101] Take a target well TA1 as an example. Figure 3 As shown in FIG. 1 , the PDA double logarithmic curve is first used to perform the edge detection test method. The characteristics of the four boundaries (east, west, south, and north directions) of the pressure propagation range appear on the PDA double logarithmic curve in sequence (characteristics 1, 2, 3, and 4). The distances corresponding to the time periods of these four characteristics are respectively used as the distances between the four boundaries of the pressure propagation range and the target well, as shown in FIG. Figure 4 As shown, a schematic diagram of the pressure propagation range boundary is obtained. Then, combined with the static boundary (the fourth boundary), the final well control boundary is obtained.

[0102] Step S2: Calculate the well-controlled reserves within the well-controlled boundary range, and determine the influencing factors of the well-controlled reserves within the well-controlled boundary range.

[0103] The reservoir geological model is established based on a three-dimensional orthogonal grid system.

[0104] Specifically, in step S2, the step of calculating the well controlled reserves within the well controlled boundary range includes the following steps:

[0105] Step S21: gridding all caves within the well control boundary according to the three-dimensional orthogonal grid system to obtain a plurality of grids;

[0106] Step S22: calculating and accumulating the oil-containing volumes in all squares one by one to obtain the oil-containing volume within the well control boundary;

[0107] Step S23: converting the oil-containing volume within the well control boundary range into the well control reserves within the well control boundary range by combining the surface crude oil density and the surface crude oil volume coefficient.

[0108] It should be noted that, because the reservoir geological model is established based on a three-dimensional orthogonal grid system, the caves in the reservoir geological model are automatically gridded to obtain a plurality of grids.

[0109] Specifically, the calculation formula for the well controlled reserves within the well controlled boundary is as follows:

[0110]

[0111] Wherein, N0 is the well-controlled reserves within the well-controlled boundary, in t;

[0112] J is the total number of squares within the well control boundary;

[0113] V B (k) is the volume of the kth square, in m 3 ;

[0114] N TG (k) is the net-to-gross ratio of the kth square;

[0115] φ(k) is the porosity of the kth square;

[0116] S0(k) is the oil saturation of the kth square;

[0117] B0 is the volume coefficient of surface crude oil;

[0118] ρ0 is the density of crude oil on the ground, in units of 10 3 kg / m 3 .

[0119] Step S3: According to the influencing factors of the well-controlled reserves within the well-controlled boundary, a corresponding optimization method is selected to optimize the reservoir geological model.

[0120] According to the formula for calculating the well-controlled reserves in step S2, it can be found that the formula is the sum of the reserves of all the squares of all the caves within the well-controlled boundary. From the formula, the influencing factor of the well-controlled reserves within the well-controlled boundary may be the volume V of the square. Band volume parameters such as the total number of squares J, or the net-to-gross ratio of the squares N TG , porosity φ and oil saturation S0 and other attribute parameters. The sources of uncertainty of various parameters are different, mainly from the uncertainty of reservoir volume modeling process and the uncertainty of reservoir attribute modeling process. Among them, the total number of grids J and the grid volume V B The uncertainty of these two parameters comes from the uncertainty in the reservoir (cavern) volume modeling process. TG The uncertainties of the three parameters, porosity φ and oil saturation S0, come from the uncertainty in the process of reservoir property modeling.

[0121] When there is a contradiction between well-controlled reserves and dynamic reserves, the cause of the contradiction should be analyzed in combination with actual production. For example, for emptied caves, the assignment of attribute parameters is semi-quantitatively determined using the dynamic analogy method. When there is a contradiction between the well-controlled reserves and the dynamic reserves calculated using these attribute parameters, the uncertainty of attribute parameters such as porosity and oil saturation has the greatest impact. The attribute optimization method should be selected to optimize such caves. For example, for caves located at the target well, the attribute parameters of such caves are relatively accurate, and the volume of the cave (total number of grids J and grid volume V B ) has greater uncertainty, and the volume optimization method should be selected to optimize this type of cave. The uncertainty of the attribute parameters outside a certain distance of the well point (target well) is large. At this time, the attribute optimization method should be selected to optimize this type of cave.

[0122] A relatively accurate attribute model is the prerequisite for obtaining relatively accurate well-controlled reserves. However, in the actual modeling process, many factors will increase the uncertainty of the attribute model. For example, although some wells encounter caves, there is no corresponding porosity logging interpretation due to the emptying of the drill bit. The assignment of the cave porosity is mostly determined by analogy, which has certain uncertainty. When the attribute parameters of the cave become the main factors affecting the calculation of well-controlled reserves, it is necessary to adjust the attribute model within the well-controlled boundary range to achieve the purpose of optimizing the well-controlled reserves, that is, the attribute optimization method should be selected to optimize such caves. In fracture-cave reservoirs, most of the storage space is large caves. Therefore, when optimizing the volume and attributes of the reservoir body by volume optimization method and / or attribute optimization method, the optimization target is large caves.

[0123] Specifically, the selection principle of the optimization method is as follows: when the influencing factor of the well controlled reserves within the well control boundary range is a volume parameter, the optimization method is a volume optimization method; when the influencing factor of the well controlled reserves within the well control boundary range is an attribute parameter, the optimization method is an attribute optimization method; when the influencing factor of the well controlled reserves within the well control boundary range includes both volume parameters and attribute parameters, the optimization method is a method combining the volume optimization method and the attribute optimization method.

[0124] These three optimization methods are described below.

[0125] The first optimization method: in the step S3, when the influencing factor of the well controlled reserves within the well controlled boundary range is an attribute parameter, an attribute optimization method is selected to optimize the reservoir geological model, including the following steps:

[0126] Step S311: taking a cave located within the well control boundary and whose reserves are affected by volume parameters as a first cave to be optimized;

[0127] Step S312: disturbing the volume of each first cave respectively, recalculating the well controlled reserves within the well controlled boundary after each disturbance and calculating the difference between the well controlled reserves and the well controlled reserves target value to obtain a first difference, and comparing the absolute value of the first difference with a first preset end threshold;

[0128] Step S313: when the absolute value of the first difference of the volume of at least one first cave after the disturbance is less than the first preset end threshold, the disturbance is terminated and the optimized reservoir geological model is obtained;

[0129] Step S314: When the absolute values ​​of the first differences in the volumes of all the first caves after being disturbed are greater than or equal to the first preset end threshold, return to step S312 to disturb the volumes of the first caves again.

[0130] The volume of each first cave is disturbed respectively, including the following steps: the volume of each first cave is uniformly expanded outward or reduced inward by a layer of a square size while keeping the shape of the first cave unchanged; the attribute parameters of the first cave are confirmed; wherein, for the first cave with expanded volume, the attribute parameters of the squares of the expanded part are the average value of the corresponding attribute parameters of the surrounding squares, so that the attribute parameters of the expanded first cave are gradually transitioned in space; for the first cave with reduced volume, the attribute parameters of each square remain unchanged.

[0131] Specifically, the method for expanding the first cave is that, in the reservoir geological model before optimization, the set of all squares representing a first cave within the well control boundary is denoted as S. Then, for any square u(i, j, k), there is:

[0132] u(i, j, k)∈S{a square representing a first cave within the well control boundary}

[0133] For all u(i, j, k)∈S, let u(i±1, j±1, k±1)∈S, such that Figure 5As shown in the figure, the volume of the first cave is uniformly expanded outward by a layer of the size of a square grid while keeping the shape of the first cave unchanged. Moreover, the net-to-gross ratio, porosity, oil saturation and other attribute parameters of the expanded part are the average values ​​of the corresponding attribute parameters of the surrounding squares, so as to ensure that the attribute parameters of the first cave after expansion are gradually transitioned in space. Therefore, for the first cave with uniform attributes, the attribute parameters of the expanded part are consistent with those of other parts.

[0134] As for the first cave whose volume has been reduced, only the number of squares of the first cave has changed, and the attribute parameters of each square have not changed.

[0135] It should be noted that in the volume perturbation method, multiple first caves can be disturbed together or one by one.

[0136] like Figure 6 As shown, taking the expansion of the volume of the first cave as an example, a method of perturbation one by one is provided in this embodiment, including: ① perturbing the volume of each first cave one by one, recalculating the well controlled reserves within the well controlled boundary range after each first cave is disturbed and calculating the difference between the well controlled reserves and the well controlled reserves target value to obtain the first difference and compare its absolute value with the first preset end threshold; ② when the absolute value of the first difference after the current first cave is disturbed is less than the first preset end threshold, ending the disturbance and obtaining the optimized reservoir geological model; ③ when the absolute values ​​of the first differences after all the first caves are disturbed are greater than or equal to the first preset end threshold, returning to step ① to perturb the volume of each first cave again.

[0137] The second optimization method: Figure 7 As shown, in step S3, when the influencing factor of the well controlled reserves within the well controlled boundary range is an attribute parameter, an attribute optimization method is selected to optimize the reservoir geological model, including the following steps:

[0138] Step S321: taking a cave located within the well control boundary and whose reserves are affected by attribute parameters as a second cave to be optimized;

[0139] Step S322: disturbing the attribute parameters of all second caves simultaneously, recalculating the well-controlled reserves within the well-controlled boundary after each disturbance, and analyzing the difference between the well-controlled reserves and the well-controlled reserves target value to obtain a second difference;

[0140] Step S323: accept or reject the current disturbance according to the comparison result between the absolute value of the second difference after the current disturbance and the absolute value of the second difference after the previous disturbance; wherein, when the absolute value of the second difference after the current disturbance is less than the absolute value of the second difference after the previous disturbance, accept the current disturbance, and store the disturbance factor and the corresponding second difference used in the current disturbance process as the latest disturbance factor and the latest second difference;

[0141] Step S324: confirming the current disturbance number and comparing it with the preset disturbance number;

[0142] Step S325: when the current disturbance number is greater than or equal to the preset disturbance number, the disturbance is terminated and the reservoir geological model corresponding to the latest disturbance factor currently stored is used as the optimized reservoir geological model;

[0143] Step S326: When the current disturbance number is less than the preset disturbance number, compare the absolute value of the latest second difference currently stored with the second preset end threshold value:

[0144] When the absolute value of the latest second difference currently stored is less than the second preset end threshold, the disturbance is ended and the reservoir geological model corresponding to the latest disturbance factor currently stored is used as the optimized reservoir geological model;

[0145] When the absolute value of the latest second difference currently stored is greater than or equal to the second preset end threshold, return to step S322 to perform the next disturbance on the attribute parameters of all second caves.

[0146] Among them, the above-mentioned property optimization method has some differences in the optimization process for emptying caves and non-emptying caves.

[0147] For the emptied and un-drilled karst reservoirs within the well control boundary, when the attribute parameters of such karst caves are disturbed, the attribute parameters of all the squares of the second karst cave are multiplied by the corresponding disturbance factor C at the same time each time the disturbance is performed, so as to change the attribute parameters of all the second karst caves proportionally; wherein the disturbance factor C is a constant value and is 0.8 to 1.2 times the ratio of the well control reserve target value to the well control reserve before optimization (original well control reserve). Taking the disturbance of porosity in the attribute parameters as an example, the schematic diagram of the porosity disturbance of the drilled non-emptied karst cave is as follows: Figure 8 For the porosity optimization process, a change to the cave porosity model is called a disturbance.

[0148] For non-emptied cave reservoirs drilled within the well control boundary range, when the attribute parameters of such caves are disturbed, each disturbance will multiply the attribute parameters of all squares of the second cave by the corresponding disturbance factor C at the same time, so as to change the attribute parameters of all second caves proportionally. Since there are relatively accurate attribute parameter interpretation data at the target well (well point), the closer the distance to the well point, the lower the uncertainty of the attribute parameters, so the attribute parameters of the second cave at the well point (target well) remain unchanged, and the disturbance factor C is 1, and then the disturbance factor C of the second cave within the second preset range decreases as the distance between the second cave and the well point increases (its decreasing trend is a sine curve), and the disturbance factor C of the second cave further out becomes a constant, that is, a fixed value, and it can be concluded that the constant is less than 1. Taking the disturbance of porosity in the attribute parameters as an example, the porosity disturbance model is defined, and the porosity disturbance schematic diagram is as follows: Fig. 9 shown.

[0149] The third optimization method: when the influencing factors of the well controlled reserves within the well controlled boundary range include volume parameters and attribute parameters, a method combining the volume optimization method and the attribute optimization method is selected to optimize the reservoir geological model, including the following steps:

[0150] Step S331: taking a cave located within the well control boundary and whose reserves are jointly affected by volume parameters and attribute parameters as a third cave to be optimized;

[0151] Step S332: optimizing the third cave by a volume optimization method and calculating the change value of the well-controlled reserves within the well-controlled boundary before and after the volume optimization to obtain a first change value of the well-controlled reserves;

[0152] Step S333: optimizing the third cave by the attribute optimization method and calculating the change value of the well controlled reserves within the well controlled boundary range before and after the attribute optimization to obtain a second change value of the well controlled reserves; wherein the second change value of the well controlled reserves is equal to the difference between the expected value of the well controlled reserves change value and the first change value of the well controlled reserves, and the expected value of the well controlled reserves change value is the difference between the well controlled reserves target value and the well controlled reserves before volume optimization;

[0153] Step S334: Calculate the ratio of the first change value of the well-controlled reserves to the second change value of the well-controlled reserves to obtain a disturbance coefficient α.

[0154] That is, △B=△B V +△B P , α=(△B V ) / (△B P), wherein the larger the disturbance coefficient α is, the greater the impact of the uncertainty of the volume parameters on the reserves of the third cave is, and the smaller the disturbance coefficient α is, the greater the impact of the uncertainty of the attribute parameters on the reserves of the third cave is. After obtaining the disturbance coefficient α, the third cave can be optimized again by only using the volume optimization method or the attribute optimization method according to the size of the disturbance coefficient α, which is more flexible and more operable. Here, the specific steps of the volume optimization method and the attribute optimization method are the same as the specific steps of the volume optimization method and the attribute optimization method provided above, and will not be repeated here.

[0155] It should be noted that well-controlled reserves are one of the important bases for the formulation and adjustment of oilfield development technical plans, and the main methods for determining them are static method and dynamic method. The static method is to use geological models to determine the well-controlled reserves of a single well, i.e., the method used to calculate the well-controlled reserves in the embodiments of the present disclosure. The dynamic method is based on the relationship between geological reserves and dynamic data, and uses dynamic data to reversely infer geological reserves. The methods mainly include material balance method, decline curve method, water drive curve method, etc.

[0156] In the present disclosure, the dynamic reserves calculated by the dynamic method based on the dynamic data are used as the target well-controlled reserves to optimize the geological model. The well-controlled reserves of the optimized reservoir geological model are closer to the target well-controlled reserves calculated based on the dynamic data, which improves the precision and accuracy of the reservoir geological model and thus optimizes the simulation effect.

[0157] Specifically, the well-controlled reserves target value is calculated according to the following steps:

[0158] Determining a porous medium model of the target well according to the state parameters of the target well;

[0159] Acquire the property parameters of the target well, and determine the single well control area of ​​the target well based on the porous medium model of the target well and in combination with well test data and / or production performance data;

[0160] The single-well controlled reserves of the target well are determined according to the attribute parameters of the target well and the single-well controlled area.

[0161] Among them, the step of determining the porous medium model specifically includes: classifying the target well according to the state parameters of the target well to obtain a single well classification result; determining the geological characteristics of the target well according to the single well classification result; and determining the porous medium model of the target well according to the geological characteristics. The porous medium model is one of a three-porosity dual-permeability model, a three-porosity single-permeability model, a dual-porosity dual-permeability model, a dual-porosity single-permeability model or a single-porosity medium model. Among them, according to the state parameters of the target well, the target well is classified and clustered for analysis, and the state parameters used include: the emptying condition during the drilling process, the leakage condition during the drilling process, the characteristics of the seismic profile through the well, the pump pressure condition during the acid fracturing process, the fracture information interpreted by the logging, the reservoir connectivity, the core reservoir development, the initial daily oil production, the cumulative oil production, the oil pressure change amplitude and the production decline condition and other parameters.

[0162] Specifically, for a certain well, if the draining exceeds 0.5 meters during drilling, the weight corresponding to the draining situation is 1; if the draining does not exceed 0.5 meters, the weight corresponding to the draining situation is 0; if there is no draining phenomenon, the weight corresponding to the draining situation is -1.

[0163] If the mud loss during drilling exceeds 1,000 cubic meters, the weight corresponding to the loss is 1; if the loss does not exceed 1,000 cubic meters, the weight corresponding to the loss is 0; if there is no loss, the weight corresponding to the loss is -1.

[0164] If the through-well seismic profile characteristics of the well show bead-like reflection characteristics, then the weight corresponding to the state parameter (i.e., through-well seismic profile) is 1; if the through-well seismic profile characteristics show other types of reflection characteristics, then the weight corresponding to the state parameter is 0; if the through-well seismic profile characteristics show no reflection characteristics, then the weight corresponding to the state parameter is -1.

[0165] If the pump pressure decreases by more than 5% during the acid fracturing process, the weight corresponding to the pump pressure is 1; if the pump pressure decreases by no more than 5%, the weight corresponding to the pump pressure is 0; if the pump pressure is otherwise, the weight corresponding to the pump pressure is -1.

[0166] If there are more than 10 fractures in the fracture information interpreted by the well logging, the weight corresponding to the fracture is 1; if there are 1 to 10 fractures, the weight corresponding to the fracture is 0; if there are no fractures, the weight corresponding to the fracture is -1.

[0167] The connectivity of the reservoir in the well can be determined by the geological model of the well. Specifically, if the karst caves in the drilling area are connected to more than one large fracture, then the connectivity is good, and the weight corresponding to the connectivity of the reservoir is 1; if the karst pores in the drilling area are only connected to microfractures, then the connectivity is general, and the weight corresponding to the connectivity of the reservoir is 0; if there are only microfractures in the drilling area, then the connectivity is poor, and the weight corresponding to the connectivity of the reservoir is -1.

[0168] For the core reservoir development of the well, if the pores, holes and fractures are all relatively developed, the weight corresponding to the core reservoir development is 1; if the pores and fractures are relatively developed, the weight corresponding to the core reservoir development is 0; if it is other cases, the weight corresponding to the core reservoir development is -1.

[0169] For the initial daily oil production of the production well, in this embodiment, the average value of the production well in the first month of production is used for analysis. 3 , then the weight corresponding to the initial daily oil production is 1; if the initial daily oil production is between 10 and 100m 3 , then the weight corresponding to the initial daily oil production is 0; if the initial daily oil production is less than 10m 3 , then the weight corresponding to the initial daily oil production is -1.

[0170] For the cumulative oil production of the production well, in this embodiment, the oil production accumulated to the current time is used for analysis. 4 m 3 , then the weight corresponding to the cumulative oil production is 1; if the cumulative oil production is 1×10 4 ~10×10 4 m 3 If the cumulative oil production is less than 1×10 4 m 3 , then the weight corresponding to the cumulative oil production is -1.

[0171] For the oil pressure variation of the production well, in this embodiment, the oil pressure monitored for two consecutive days is used for analysis. If the oil pressure variation is less than 5%, the weight corresponding to the oil pressure variation is 1; if the oil pressure variation is between 0.5% and 5%, the weight corresponding to the oil pressure variation is 0; if the oil pressure variation is less than 0.5%, the weight corresponding to the oil pressure variation is -1.

[0172] For the production decline of production wells, if the production decline is less than 1%, the weight corresponding to the production decline is 1; if the production decline is 1% to 10%, the weight corresponding to the production decline is 0; if the production decline is greater than 10%, the weight corresponding to the production decline is -1.

[0173] In this way, the weights corresponding to the various state parameters of each production well can be determined. Subsequently, in this embodiment, based on the weights corresponding to the various state parameters of each production well, each production well is classified by cluster analysis, thereby obtaining a single well classification result. Specifically, based on the data matrix, a single well classification result, that is, the type of each production well, can be obtained by cluster analysis.

[0174] Among them, the target wells can be divided into the first type of wells (i.e., Type I wells), the second type of wells (i.e., Type II wells) and the third type of wells (i.e., Type III wells).

[0175] Among them, the first type of wells are production wells drilled in fracture-cavity reservoir development zones with good geological characteristics, including: the reservoirs are mainly caves, the surrounding fractures are densely developed, there are large fractures that can play a diversion role, and the connectivity between the reservoirs is good.

[0176] The second type of wells are production wells drilled in areas with more developed fractures. Their geological characteristics include: the presence of small dissolved pores and / or caves in the drilled area, the presence of larger fractures and good connectivity between reservoir bodies.

[0177] The third type of wells are production wells that encounter reservoirs that only have isolated caves and / or pores, small fractures, etc. Their geological characteristics are that the connectivity between reservoirs is very poor and fluids are difficult to flow.

[0178] In the step of determining the single well control area of ​​the target well, when the target well is a production well that has undergone a well test, the single well control area of ​​the target well is determined based on the well test data and / or production dynamic data; if the target well is a production well that has not undergone a well test, the single well control area of ​​the target well is determined based on the production dynamic data.

[0179] The specific steps of determining the single well control area of ​​the target well specifically include: determining the bottom hole pressure of the target well according to the production dynamic data; determining the control shape of the target well according to the bottom hole pressure and the porous medium model; and determining the single well control area of ​​the target well based on the control shape and in combination with the well control reserve parameters. The well control reserve parameters include: reservoir porosity, crude oil viscosity and comprehensive compressibility.

[0180] When the control shape is circular or quasi-circular, the single well control area of ​​the target well is calculated according to the following expression:

[0181]

[0182] When the control shape is a rectangle or a quasi-rectangle, the single well control area of ​​the target well is calculated according to the following expression:

[0183]

[0184] Among them, A represents the single well control area, k represents the formation permeability, t s represents the start time of the pseudo-steady-state period, t represents the production time, φ represents the reservoir porosity, μ represents the crude oil viscosity, C t Indicates the comprehensive compression factor.

[0185] The control shape of the target well is determined by curve fitting based on the bottom hole pressure.

[0186] When the control shape is circular or quasi-circular, when determining the single well control area of ​​the target well, the quasi-steady-state time period of the target well is also determined according to the fitting curve, and the well control reserve parameter is corrected according to the bottom hole pressure in the quasi-steady-state time period to obtain the corrected well control reserve parameter; thus, the single well control area of ​​the target well is determined according to the corrected well control reserve parameter. If the control shape is circular or quasi-circular but the fitting curve does not contain the quasi-steady-state time period, the single well control area of ​​the target well is determined according to the production section with a water cut less than a preset water cut threshold, thereby calculating the well control reserve target value.

[0187] The disclosed embodiment provides a method for optimizing a reservoir geological model, the method comprising determining the well control boundary of the reservoir geological model; calculating the well control reserves within the well control boundary, and determining the influencing factors of the well control reserves within the well control boundary; and selecting a corresponding optimization method to optimize the reservoir geological model according to the influencing factors of the well control reserves within the well control boundary. The method optimizes the reservoir geological model locally, so that the well control reserves of the optimized reservoir geological model are closer to the target well control reserves calculated based on dynamic data, thereby improving the precision and accuracy of the reservoir geological model and optimizing the simulation effect. Moreover, the method optimizes the volume parameters and / or attribute parameters of different types of reservoirs while keeping the original karst cave reservoir morphology unchanged, and has high flexibility and strong operability.

[0188] Embodiment 2

[0189] Based on the first embodiment, this embodiment illustrates the method described in the first embodiment through a specific implementation case.

[0190] This embodiment is based on the reservoir geological model and dynamic data of a unit in the Tahe area, selects a production well W in the unit, and uses the above method to optimize the reservoir geological model.

[0191] like Fig.10 As shown in the figure, it can be seen from the established reservoir geological model that Well W encountered a cave with a reserve of 170,000 tons, which is far lower than the 650,000 tons predicted by the dynamic method (dynamic reserves). As Well W was subjected to acid fracturing measures, a series of cracks were generated near the wellbore, connecting the cave reservoirs within a certain range nearby. Therefore, the dynamic reserves predicted by the dynamic method should be the sum of the reserves of the cave reservoirs connected to Well W.

[0192] Before optimizing the reservoir geological model of Well W, the above method is applied to determine the well control boundary of Well W. First, determine the neighboring wells (TK1 to TK5) at different directions at a certain distance (first preset range) near Well W, connect the midpoints of the lines connecting Well W and each neighboring well in turn, form a closed polygon as the first boundary (i.e., the initial well control boundary), and then consider the shielding effect. It is observed that there are faults between Well W and Wells TK3, TK4, and TK5, and the distance from Well W to these three faults is greater than the fault shielding distance threshold d f (i.e., the preset distance), so these three faults are taken as part of the boundary line to obtain the updated well control boundary, i.e., the second boundary. Because the acid fracturing range is located within the second boundary, and the second boundary does not pass through the isolated cave, and in this embodiment, the influence of the pressure propagation range boundary of the target well W is not considered, the second boundary is the final well control boundary, such as Fig.11 shown.

[0193] Then, after determining the well control boundary, we need to further determine the karst cave reservoirs within the well control boundary. According to the depth of the perforation section of Well W and the connectivity between the well and the karst cave reservoirs, we screen out those karst cave reservoirs that have a large difference in depth from the perforation section of Well W and are not connected to Well W, and obtain the karst cave reservoirs within the well control range of Well W, such as Fig.12 and 13As shown, these cave reservoirs are taken as the objects of subsequent optimization. It can be seen that there are 4 cave reservoirs within the well control boundary of Well W, among which Well W drilled one cave and the other 3 caves are distributed within a certain range of Well W. The reserves of these four caves are calculated using the well control reserve calculation formula. From the calculation results, it can be seen that the well control reserves within the well control boundary are 492,000 tons, which is still a certain distance from the dynamic reserves (650,000 tons). Considering that the cave drilled by the well is a non-emptying cave and is located at the well point, it has a relatively accurate attribute parameter value. Its reserves are more affected by the uncertainty of volume parameters. Therefore, the volume optimization method is selected to optimize the reservoir geological model. During the optimization process, the volume of the cave reservoir within the well control boundary is optimized while keeping the original shape unchanged, as shown in the figure. Fig.14 and 15 Table 1 is the statistical table of reserves of each cave before and after optimization.

[0194] Table 1

[0195]

[0196] As shown in Table 1, after the above optimization, the well-controlled reserves and dynamic reserves (650,000 tons) of Well W are basically consistent, which not only ensures that the morphology and distribution of the cave reservoir are constrained by various static data, but also that its reserves are consistent with the production dynamic data.

[0197] Embodiment 3

[0198] Fig.16 For a schematic diagram of a reservoir geological model optimization device provided in an embodiment of the present disclosure, please refer to Fig.16 This embodiment provides an optimization device 100 for an oil reservoir geological model, including a boundary determination module 101 , a reserve calculation and influencing factor determination module 102 , and a model optimization module 103 .

[0199] The boundary determination module 101 is used to determine the well control boundary of the reservoir geological model;

[0200] The reserve calculation and influencing factor determination module 102 is used to calculate the well-controlled reserves within the well-controlled boundary range and determine the influencing factors of the well-controlled reserves within the well-controlled boundary range;

[0201] The model optimization module 103 is used to select a corresponding optimization method to optimize the reservoir geological model according to the influencing factors of the well controlled reserves within the well controlled boundary range.

[0202] The boundary determination module 101 determines the well control boundary of the reservoir geological model, the reserve calculation and influencing factor determination module 102 calculates the well control reserves within the well control boundary range, and determines the influencing factors of the well control reserves within the well control boundary range, and finally the model optimization module 103 selects the corresponding optimization method to optimize the reservoir geological model according to the influencing factors of the well control reserves within the well control boundary range.

[0203] The specific embodiment of the method for optimizing the reservoir geological model based on the above modules has been described in detail in the first embodiment and will not be repeated here.

[0204] Embodiment 4

[0205] The embodiment of the present application provides an electronic device, which may be a mobile phone, a computer or a tablet computer, etc., including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method for optimizing the reservoir geological model as described in Embodiment 1 is implemented. It is understood that the electronic device may also include a multimedia component, an input / output (I / O) interface, and a communication component.

[0206] The processor is used to execute all or part of the steps in the reservoir geological model optimization method in Example 1. The memory is used to store various types of data, which may include instructions of any application or method in the electronic device, and data related to the application.

[0207] The processor can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is used to execute the optimization method of the reservoir geological model in the above-mentioned embodiment 1.

[0208] The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0209] Embodiment 5

[0210] This embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, a server, an App application store, etc., on which a computer program is stored. When the computer program is executed by a processor, the following method steps can be implemented:

[0211] Step S1: determining the well control boundary of the reservoir geological model;

[0212] Step S2: calculating the well controlled reserves within the well controlled boundary range, and determining the influencing factors of the well controlled reserves within the well controlled boundary range;

[0213] Step S3: According to the influencing factors of the well-controlled reserves within the well-controlled boundary, a corresponding optimization method is selected to optimize the reservoir geological model.

[0214] The specific implementation process of the above method steps can be found in Example 1, and this embodiment will not be repeated here.

[0215] In summary, the present disclosure provides an optimization method, device, electronic device and storage medium for a reservoir geological model, the method comprising determining the well control boundary of the reservoir geological model; calculating the well control reserves within the well control boundary range, and determining the influencing factors of the well control reserves within the well control boundary range; according to the influencing factors of the well control reserves within the well control boundary range, selecting a corresponding optimization method to optimize the reservoir geological model. The method optimizes the reservoir geological model locally, so that the well control reserves of the optimized reservoir geological model are closer to the target well control reserves calculated based on dynamic data, thereby improving the precision and accuracy of the reservoir geological model, thereby optimizing the simulation effect. Moreover, the method optimizes the volume parameters and / or attribute parameters of different types of reservoir bodies while keeping the original reservoir body morphology unchanged, with high flexibility and strong operability.

[0216] In the several embodiments provided in the present disclosure, it should be understood that the disclosed method can also be implemented in other ways. The method embodiments described above are merely illustrative.

[0217] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0218] Although the embodiments disclosed in the present disclosure are as above, the contents described are only embodiments adopted for facilitating the understanding of the present disclosure and are not intended to limit the present disclosure. Any technician in the technical field to which the present disclosure belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present disclosure, but the scope of patent protection of the present disclosure shall still be subject to the scope defined in the attached claims.

Claims

1. A method for optimizing a reservoir geological model, characterized in that: The method comprises: Step S1: determining the well control boundary of the reservoir geological model; Step S2: calculating the well controlled reserves within the well controlled boundary range, and determining the influencing factors of the well controlled reserves within the well controlled boundary range; Step S3: According to the influencing factors of the well-controlled reserves within the well-controlled boundary range, select a corresponding optimization method to optimize the reservoir geological model; wherein, the step S1 includes the following steps: Step S11: determining adjacent wells within a first preset range near the target well of the reservoir geological model, and sequentially connecting the midpoints of the lines connecting the target well and each of the adjacent wells to form a closed polygon as a first boundary; Step S12: determining all faults within the range of the first boundary, and correcting the first boundary by taking the faults whose distance from the target well is greater than a preset distance as part of the boundary line, thereby obtaining a second boundary; Step S13: determining whether the target well is an acid-fractured well, and when the target well is an acid-fractured well and its fracturing range exceeds the second boundary, correcting the second boundary to obtain a third boundary and making the fracturing range within the range of the third boundary; Step S14: determining the positional relationship between the third boundary and the isolated cave in the reservoir geological model, and when the third boundary passes through the isolated cave and the isolated cave belongs to the reservoir body, correcting the third boundary to obtain a fourth boundary and make the isolated cave located within the range of the fourth boundary; Step S15: Determine the pressure propagation range boundary of the target well based on the pressure data of the target well during the production process. When the pressure propagation range boundary is within the range of the fourth boundary, use the pressure propagation range boundary as a part of the boundary line to correct the fourth boundary, thereby obtaining the final well control boundary.

2. The method according to claim 1, characterized in that In step S2, the reservoir geological model includes a three-dimensional orthogonal grid system, and the step of calculating the well-controlled reserves within the well-controlled boundary range includes the following steps: Step S21: gridding all caves within the well control boundary according to the three-dimensional orthogonal grid system to obtain a plurality of grids; Step S22: calculating and accumulating the oil-containing volumes in all squares one by one to obtain the oil-containing volume within the well control boundary; Step S23: converting the oil-containing volume within the well control boundary range into the well control reserves within the well control boundary range by combining the surface crude oil density and the surface crude oil volume coefficient.

3. The method according to claim 2, characterized in that The well controlled reserves within the well controlled boundary are calculated according to the following formula: Wherein, N0 is the well-controlled reserves within the well-controlled boundary; J is the total number of squares within the well control boundary; V B (k) is the volume of the kth square; N TG (k) is the net-to-gross ratio of the kth square; φ(k) is the porosity of the kth square; S0(k) is the oil saturation of the kth square; B0 is the volume coefficient of surface crude oil; ρ0 is the density of crude oil on the ground.

4. The method according to claim 1, characterized in that: In the step S3, When the influencing factor of the well controlled reserves within the well controlled boundary range is a volume parameter, the optimization method is a volume optimization method; When the influencing factor of the well-controlled reserves within the well-controlled boundary is an attribute parameter, the optimization method is an attribute optimization method; When the influencing factors of the well-controlled reserves within the well-controlled boundary range include both volume parameters and attribute parameters, the optimization method is a method combining the volume optimization method and the attribute optimization method.

5. The method according to claim 4, characterized in that In the step S3, when the influencing factor of the well controlled reserves within the well controlled boundary range is a volume parameter, a volume optimization method is selected to optimize the reservoir geological model, including the following steps: Step S311: taking a cave located within the well control boundary and whose reserves are affected by volume parameters as a first cave to be optimized; Step S312: disturbing the volume of each first cave respectively, recalculating the well controlled reserves within the well controlled boundary after each disturbance and calculating the difference between the well controlled reserves and the well controlled reserves target value to obtain a first difference, and comparing the absolute value of the first difference with a first preset end threshold; Step S313: when the absolute value of the first difference of the volume of at least one first cave after the disturbance is less than the first preset end threshold, the disturbance is terminated and the optimized reservoir geological model is obtained; Step S314: When the absolute values ​​of the first differences in the volumes of all the first caves after being disturbed are greater than or equal to the first preset end threshold, return to step S312 to disturb the volumes of the first caves again.

6. The method according to claim 5, characterized in that In step S312, the volume of each first cave is disturbed respectively, including the following steps: The volume of each first cave is uniformly expanded outward or reduced inward by a layer of a square size while keeping the shape of the first cave unchanged; Confirm the attribute parameters of the first cave; wherein, for the first cave with an expanded volume, the attribute parameters of the expanded part of the grid are the average value of the corresponding attribute parameters of the surrounding grids, so that the attribute parameters of the expanded first cave are gradually transitioned in space; for the first cave with a reduced volume, the attribute parameters of each grid remain unchanged.

7. The method according to claim 4, characterized in that In step S3, when the influencing factor of the well controlled reserves within the well controlled boundary range is an attribute parameter, an attribute optimization method is selected to optimize the reservoir geological model, including the following steps: Step S321: taking a cave located within the well control boundary and whose reserves are affected by attribute parameters as a second cave to be optimized; Step S322: disturbing the attribute parameters of all second caves simultaneously, recalculating the well-controlled reserves within the well-controlled boundary after each disturbance, and analyzing the difference between the well-controlled reserves and the well-controlled reserves target value to obtain a second difference; Step S323: accept or reject the current disturbance according to the comparison result between the absolute value of the second difference after the current disturbance and the absolute value of the second difference after the previous disturbance; wherein, when the absolute value of the second difference after the current disturbance is less than the absolute value of the second difference after the previous disturbance, accept the current disturbance, and store the disturbance factor and the corresponding second difference used in the current disturbance process as the latest disturbance factor and the latest second difference; Step S324: confirming the current disturbance number and comparing it with the preset disturbance number; Step S325: when the current disturbance number is greater than or equal to the preset disturbance number, the disturbance is terminated and the reservoir geological model corresponding to the latest disturbance factor currently stored is used as the optimized reservoir geological model; Step S326: when the current disturbance number is less than the preset disturbance number, comparing the absolute value of the latest second difference currently stored with the second preset end threshold; When the absolute value of the latest second difference currently stored is less than the second preset end threshold, the disturbance is ended and the reservoir geological model corresponding to the latest disturbance factor currently stored is used as the optimized reservoir geological model; When the absolute value of the latest second difference currently stored is greater than or equal to the second preset end threshold, return to step S322 to perform the next disturbance on the attribute parameters of all second caves.

8. The method according to claim 7, characterized in that In step S322, when the second cave is an empty cave and an un-drilled cave, the attribute parameters of all the second caves are disturbed simultaneously, including the following steps: Multiply the attribute parameters of all the squares of all the second caves by the corresponding disturbance factors at the same time, so as to change the attribute parameters of all the second caves proportionally; The disturbance factor is a constant and is 0.8 to 1.2 times the ratio of the well-controlled reserves target value to the well-controlled reserves before optimization.

9. The method according to claim 7, characterized in that: In step S322, when the second cave is a non-emptied cave encountered during drilling, the attribute parameters of all the second caves are disturbed simultaneously, including the following steps: Multiplying the attribute parameters of all the squares of the second cave by the corresponding disturbance factor at the same time, so as to change the attribute parameters of the second cave proportionally; Among them, the disturbance factor of the second cave located at the target well position is 1, the disturbance factor of the second cave located within the second preset range near the target well decreases as the distance between the second cave and the target well increases, and the disturbance factor of the second cave located outside the second preset range near the target well is a constant and less than 1.

10. The method according to claim 4, characterized in that In step S3, when the influencing factors of the well controlled reserves within the well controlled boundary range include volume parameters and attribute parameters, a method combining the volume optimization method and the attribute optimization method is selected to optimize the reservoir geological model, including the following steps: Step S331: taking a cave located within the well control boundary and whose reserves are jointly affected by volume parameters and attribute parameters as a third cave to be optimized; Step S332: optimizing the third cave by a volume optimization method and calculating the change value of the well-controlled reserves within the well-controlled boundary before and after the volume optimization to obtain a first change value of the well-controlled reserves; Step S333: Optimize the third cave by the attribute optimization method and calculate the change value of the well controlled reserves within the well controlled boundary before and after the attribute optimization to obtain a second change value of the well controlled reserves; wherein the second change value of the well controlled reserves is equal to the difference between the expected value of the well controlled reserves change value and the first change value of the well controlled reserves, and the expected value of the well controlled reserves change value is the difference between the well controlled reserves target value and the well controlled reserves before volume optimization.

11. An optimization device for a reservoir geological model, characterized in that: The device comprises: A boundary determination module is used to determine the well control boundary of the reservoir geological model; A reserve calculation and influencing factor determination module, used to calculate the well-controlled reserves within the well-controlled boundary range and determine the influencing factors of the well-controlled reserves within the well-controlled boundary range; A model optimization module is used to select a corresponding optimization method to optimize the reservoir geological model according to the influencing factors of the well controlled reserves within the well control boundary range; wherein the boundary determination module includes: a first boundary determination unit, a second boundary determination unit, a third boundary determination unit, a fourth boundary determination unit and a well control boundary determination unit; wherein, The first boundary determination unit is configured to: determine neighboring wells within a first preset range near the target well of the reservoir geological model, and sequentially connect the midpoints of the lines connecting the target well and each of the neighboring wells to form a closed polygon as the first boundary; The second boundary determination unit is configured to: determine all faults within the range of the first boundary, and correct the first boundary by taking the faults whose distance from the target well is greater than a preset distance as part of the boundary line, thereby obtaining a second boundary; The third boundary determination unit is configured to: determine whether the target well is an acid-fractured well, and when the target well is an acid-fractured well and its fracturing range exceeds the second boundary, correct the second boundary to obtain a third boundary and make the fracturing range within the range of the third boundary; The fourth boundary determination unit is configured to: determine the positional relationship between the third boundary and the isolated cave in the reservoir geological model, and when the third boundary passes through the isolated cave and the isolated cave belongs to the reservoir body, correct the third boundary to obtain a fourth boundary and make the isolated cave located within the range of the fourth boundary; The well control boundary determination unit is configured to: determine the pressure propagation range boundary of the target well based on the pressure data of the target well during the production process; when the pressure propagation range boundary is within the range of the fourth boundary, use the pressure propagation range boundary as a part of the boundary line to correct the fourth boundary, thereby obtaining the final well control boundary.

12. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method for optimizing the reservoir geological model according to any one of claims 1 to 10 is executed.

13. A storage medium, characterized in that: The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the method for optimizing the reservoir geological model as described in any one of claims 1 to 10.

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