A method, device and equipment for selecting a well completion mode

Abstract

The specification provides a selection method, device and equipment of a well completion mode. The method comprises: obtaining membership degrees of wellbore complexity data corresponding to different well completion modes; processing the membership degrees of the wellbore complexity data corresponding to the different well completion modes by using a wellbore complexity index prediction model to obtain wellbore complexity indexes corresponding to the different well completion modes, the wellbore complexity index prediction model being established in advance by using membership degrees corresponding to wellbore complexity data samples; obtaining engineering data corresponding to the different well completion modes, determining risk levels corresponding to the different well completion modes according to the wellbore complexity indexes and the engineering data; and selecting a target well completion mode from the different well completion modes according to the risk levels corresponding to the different well completion modes. Based on the above method, the optimal well completion mode can be accurately determined.

Description

Technical Field

[0001] This specification relates to the technical field of oil and gas development, and in particular to a method, device, and equipment for selecting a well completion method. Background Art

[0002] Well completion refers to the establishment of a reasonable connection channel or method between the oil and gas layer and the wellbore at the bottom of the well. This ensures good connectivity between the wellbore and the reservoir, enabling high and stable production and maintaining long-term wellbore stability. The selection of a completion method is crucial to well completion operations. The rationality of a well's completion method directly affects the smooth future development and production of the well.

[0003] The existing methods for selecting completion methods are relatively limited in scope and only apply to certain types of oil and gas reservoirs, such as carbonate fractured gas reservoirs and low-permeability oil and gas reservoirs. Furthermore, they only consider geological factors and are unable to accurately select the optimal completion method, resulting in poor results and low economic benefits in oil and gas exploration and development.

[0004] To address the above issues, no effective solutions have been proposed so far. Summary of the Invention

[0005] This specification provides a method, device and equipment for selecting a completion method to solve the problem that the existing technology cannot accurately select the optimal completion method, resulting in poor oil and gas exploration and development effects and low economic benefits.

[0006] In a first aspect, an embodiment of this specification provides a method for selecting a completion method, including:

[0007] Obtain the membership of wellbore complexity data corresponding to different completion methods;

[0008] Using a wellbore complexity index prediction model to process the membership of wellbore complexity data corresponding to different completion methods, to obtain wellbore complexity indices corresponding to the different completion methods, wherein the wellbore complexity index prediction model is pre-established using the membership corresponding to the wellbore complexity data samples;

[0009] Acquire engineering data corresponding to different completion methods, and determine risk levels corresponding to the different completion methods based on the wellbore complexity index and the engineering data;

[0010] According to the risk levels corresponding to different completion methods, a target completion method is selected from different completion methods.

[0011] In one embodiment, the wellbore complexity data includes: wellbore trajectory tortuosity index, wellbore temperature, wellbore pressure, leakage, wellbore expansion rate, and clearance expansion rate; accordingly, obtaining the membership of the wellbore complexity data corresponding to different completion methods includes:

[0012] Obtain the wellbore trajectory tortuosity index membership, wellbore temperature membership, wellbore pressure membership, leakage rate membership, wellbore diameter expansion rate membership, and gap expansion rate membership corresponding to different completion methods.

[0013] In one embodiment, the wellbore complexity data further includes: well inclination and azimuth, the well inclination is used to determine the well inclination arc length, and the azimuth is used to determine the azimuth arc length; the wellbore trajectory tortuosity index is determined based on the well inclination arc length and the azimuth arc length.

[0014] In one embodiment, obtaining the wellbore trajectory tortuosity index membership corresponding to different completion methods includes:

[0015] Obtaining an eye trajectory tortuosity, a first target eye trajectory tortuosity, and a second target eye trajectory tortuosity, wherein the first target eye trajectory tortuosity is greater than a preset eye trajectory tortuosity threshold, and the second target eye trajectory tortuosity is less than the preset eye trajectory tortuosity threshold;

[0016] Determining the wellbore trajectory tortuosity index membership based on the wellbore trajectory tortuosity, the first target wellbore trajectory tortuosity, and the second target wellbore trajectory tortuosity;

[0017] The obtaining of wellbore temperature membership corresponding to different completion methods includes:

[0018] Establish the membership function corresponding to the wellbore temperature;

[0019] According to the membership function corresponding to the wellbore temperature, the wellbore temperature membership corresponding to different completion methods is determined.

[0020] In one embodiment, the wellbore complexity index prediction model is pre-established using the membership corresponding to the wellbore complexity data samples, and includes:

[0021] According to the following formula, a wellbore complexity index prediction model is established:

[0022] D co =w TI3D *TI 3D +w T *T+w P *P+w η *η+w K *K+w DS *K DS

[0023] Among them, D co is the wellbore complexity index; w TI3D is the weight corresponding to the wellbore trajectory tortuosity index sample; TI 3D is the membership degree corresponding to the wellbore trajectory tortuosity index sample; wT is the weight corresponding to the wellbore temperature sample; T is the membership degree corresponding to the wellbore temperature sample; w P is the weight corresponding to the wellbore pressure sample; P is the membership degree corresponding to the wellbore pressure sample; w η is the weight corresponding to the missing sample; η is the membership degree corresponding to the missing sample; w K is the weight corresponding to the well diameter expansion rate sample; K is the membership degree corresponding to the well diameter expansion rate sample; w DS is the weight corresponding to the gap expansion rate sample; K DS is the membership degree corresponding to the gap expansion rate sample.

[0024] In one embodiment, the engineering data includes: tubing running friction, tubing buckling index, and triaxial safety factor. Accordingly, determining the risk level corresponding to different completion methods based on the wellbore complexity index and the engineering data includes:

[0025] Determining target string entry friction resistances corresponding to different completion methods in sequence from the string entry friction resistances corresponding to different completion methods, and determining target triaxial safety factors corresponding to different completion methods in sequence from the triaxial safety factors corresponding to different completion methods, wherein the target string entry friction group is greater than a preset string entry friction group threshold, and the target triaxial safety factor is less than a preset triaxial safety factor threshold;

[0026] The risk scoring rules corresponding to the wellbore complexity index, string running friction resistance, string buckling index and triaxial safety factor are retrieved in sequence, and the risk values ​​corresponding to the wellbore complexity index, target string running friction resistance, string buckling index and target triaxial safety factor are determined in sequence;

[0027] The risk values ​​corresponding to the wellbore complexity index, target string running friction, string buckling index, and target triaxial safety factor are summed to obtain the comprehensive risk values ​​corresponding to different completion methods.

[0028] According to the comprehensive risk values ​​corresponding to different completion methods, the risk levels corresponding to different completion methods are determined.

[0029] In one embodiment, selecting a target completion method from different completion methods according to risk levels corresponding to different completion methods includes:

[0030] The risk levels corresponding to different completion methods are compared to determine a target risk level in the risk level and a target completion method corresponding to the target risk level, wherein the target risk level includes an acceptable risk.

[0031] In a second aspect, the embodiments of this specification further provide a device for selecting a completion method, including:

[0032] An acquisition module is used to obtain the membership of wellbore complexity data corresponding to different completion methods;

[0033] A prediction module is used to process the membership of wellbore complexity data corresponding to different completion methods using a wellbore complexity index prediction model to obtain wellbore complexity indices corresponding to different completion methods, wherein the wellbore complexity index prediction model is pre-established using the membership corresponding to the wellbore complexity data samples;

[0034] a determination module, configured to obtain engineering data corresponding to different completion methods, and determine risk levels corresponding to the different completion methods based on the wellbore complexity index and the engineering data;

[0035] The selection module is used to select a target completion method from different completion methods based on the risk levels corresponding to the different completion methods.

[0036] On the third aspect, an embodiment of this specification also provides a device for selecting a completion method, including a processor and a memory for storing processor-executable instructions. When the processor executes the instructions, the method for selecting the completion method in the above embodiment is implemented.

[0037] In a fourth aspect, an embodiment of this specification further provides a computer-readable storage medium having computer instructions stored thereon, and when the computer-readable storage medium executes the instructions, the method for selecting the completion method in the above embodiment is implemented.

[0038] The embodiments of this specification provide a method, device and equipment for selecting a completion method. First, the membership of the wellbore complexity data corresponding to different completion methods is obtained. Secondly, the membership of the wellbore complexity data corresponding to different completion methods is processed using a wellbore complexity index prediction model to obtain the wellbore complexity index corresponding to the different completion methods. The wellbore complexity index prediction model is pre-established using the membership corresponding to the wellbore complexity data samples. Then, the engineering data corresponding to the different completion methods are obtained, and the risk levels corresponding to the different completion methods are determined based on the wellbore complexity index and the engineering data. Finally, based on the risk levels corresponding to the different completion methods, a target completion method is selected from the different completion methods. In the embodiments of this specification, by using the wellbore complexity index prediction model to process the membership of the wellbore complexity data corresponding to different completion methods, the wellbore complexity index corresponding to the different completion methods can be accurately and quickly obtained. The optimal completion method is selected by obtaining engineering data corresponding to different completion methods and combining these data with the corresponding wellbore complexity index. This approach considers the influence of wellbore complexity and engineering conditions on completion method selection, improving the accuracy of completion method selection. By obtaining the risk levels corresponding to different completion methods and selecting the target completion method based on these risk levels, the completion string running risks of different completion methods can be quantified, allowing for the rapid selection of the lowest-risk completion method. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of this specification, the following is a brief introduction to the drawings required for use in the embodiments. The drawings described below are only some of the embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0040] Figure 1 This is a flow chart of a method for selecting a completion method provided in an embodiment of this specification;

[0041] Figure 2 It is a risk score combination diagram provided in the embodiments of this specification;

[0042] Figure 3 This is a risk level classification diagram provided in the embodiments of this specification;

[0043] Figure 4 This is a graph showing changes in well inclination and azimuth with well depth provided in the embodiments of this specification;

[0044] Figure 5 This is a graph showing changes in wellbore size versus well depth provided in the embodiments of this specification;

[0045] Figure 6 is a graph showing changes in the wellbore trajectory tortuosity index versus well depth provided in an embodiment of this specification;

[0046] Figure 7 This is a graph showing pressure changes with well depth provided in the embodiments of this specification;

[0047] Figure 8 This is a graph showing temperature changes with well depth provided in the embodiments of this specification;

[0048] Figure 9 This is a graph showing the comprehensive evaluation results of the risk of running the tubing string during open hole completion provided in the embodiments of this specification;

[0049] Figure 10 This is a graph showing the comprehensive evaluation results of the risks of running the tubing string during liner completion provided in the embodiments of this specification;

[0050] Figure 11 This is a graph showing the comprehensive evaluation results of the risk of running the tubing string during perforation completion provided in the embodiments of this specification;

[0051] Figure 12 This is a comparison chart of the comprehensive evaluation results of string running risks for open hole completion, liner completion, and perforation completion provided in the embodiments of this specification;

[0052] Figure 13 This is a diagram showing the predicted results of the tonnage of the open hole completion string encountering resistance provided in the embodiments of this specification;

[0053] Figure 14 This is a schematic diagram of the structure of a well completion method selection device provided in an embodiment of this specification;

[0054] Figure 15 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0055] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments derived by those skilled in the art based on the embodiments in this specification without creative effort shall fall within the scope of protection of this specification.

[0056] Well completion is a crucial component of the oil and gas production process. Its primary task is to ensure good connectivity between the wellbore and the reservoir, enabling the well to maintain high and stable production for an extended period, and maintaining long-term wellbore stability. Well completion operations are directly related to the effectiveness and economic benefits of oil and gas exploration and development. The selection of a completion method is crucial to well completion operations. The rationality of a well's completion method directly impacts the smooth future development and production of that well. The completion method must match the geological characteristics of the producing stratum, meet various engineering requirements during long-term production, and adapt to changes in formation properties during oil and gas development to minimize formation damage, increase oil and gas production, extend the life of the well, and maximize the recovery of oil and gas resources. Currently, common oil and gas well completion methods include openhole completion, perforation completion, and liner completion.

[0057] Conventional completion methods are primarily selected based on considerations of reservoir type, rock mechanical properties, wellbore stability, and sand production, combined with factors such as well productivity analysis and completion costs. Designers typically make qualitative selections based on field experience and regional conditions. This approach requires a high level of personal experience and must meet the completion design requirements for medium- and shallow-layer oil and gas reservoirs.

[0058] With the shift in oil and gas exploration and development targets, the depth of oil and gas reservoirs is increasingly developing towards deep and ultra-deep layers. Wellbore conditions, wellbore structures, and completion procedures are becoming more complex. The procedures and risks faced by completion operations are becoming greater, and the requirements for the accuracy and scientific nature of the completion method selection are becoming increasingly stringent. Through literature research and combined with the actual needs of current deep and ultra-deep well completion operations, it is concluded that the existing methods for selecting or determining completion methods have the following limitations:

[0059] (1) The existing completion selection method is relatively limited in its application and is only applicable to a certain type of oil and gas reservoir, such as carbonate fractured gas reservoirs and low permeability oil and gas reservoirs;

[0060] (2) The wellbore conditions, wellbore structures, and completion procedures of deep and ultra-deep wells are more complex. The existing completion method selection method does not consider the impact of the complexity of the oil and gas wellbore itself (such as wellbore trajectory changes, temperature and pressure, casing size, and leakage degree) on the selection of the completion method;

[0061] (3) The existing method for selecting completion methods only considers geological factors such as reservoir characteristics, wellbore stability, and formation sand production, but does not consider the impact of engineering conditions, such as the friction resistance of the completion string, buckling, safety factor, and other engineering risks.

[0062] In response to the above-mentioned problems existing in the existing methods and the specific causes of the above-mentioned problems, this application considers introducing a method, device and equipment for selecting a completion method. By comprehensively considering the wellbore complexity data and engineering data corresponding to different completion methods, the optimal completion method can be selected. This can improve the accuracy of the selection of the completion method and provide guidance for the formulation of risk control measures during the completion operation.

[0063] Based on the above ideas, this specification proposes a method for selecting a completion method. First, the membership of the wellbore complexity data corresponding to different completion methods is obtained. Secondly, the membership of the wellbore complexity data corresponding to different completion methods is processed using the wellbore complexity index prediction model to obtain the wellbore complexity index corresponding to the different completion methods. The wellbore complexity index prediction model is pre-established using the membership corresponding to the wellbore complexity data samples. Then, the engineering data corresponding to the different completion methods are obtained, and the risk levels corresponding to the different completion methods are determined based on the wellbore complexity index and the engineering data. Finally, based on the risk levels corresponding to the different completion methods, the target completion method is selected from the different completion methods. See Figure 1 As shown, the embodiment of this specification provides a method for selecting a completion method. In specific implementation, the method may include the following contents.

[0064] S101: Obtaining the membership of wellbore complexity data corresponding to different completion methods.

[0065] In some embodiments, the above-mentioned different completion methods may include: open hole completion, perforation completion, liner completion, through-hole completion, gravel liner completion, etc. Of course, the completion methods are not limited to the above examples. Technicians in the relevant field may make other changes based on the technical essence of the embodiments of this specification. However, as long as the functions and effects achieved are the same or similar to those of the embodiments of this specification, they should be covered within the protection scope of the embodiments of this specification.

[0066] In some embodiments, the wellbore complexity data may include: wellbore trajectory tortuosity index, wellbore temperature, wellbore pressure, leakage, wellbore diameter expansion rate, and clearance expansion rate. Accordingly, the membership of the wellbore complexity data corresponding to different completion methods may include:

[0067] Obtain the wellbore trajectory tortuosity index membership, wellbore temperature membership, wellbore pressure membership, leakage rate membership, wellbore diameter expansion rate membership, and gap expansion rate membership corresponding to different completion methods.

[0068] In some embodiments, the wellbore complexity data may further include: well inclination and azimuth, wherein the well inclination may be used to determine the well inclination arc length, and the azimuth may be used to determine the azimuth arc length; the wellbore trajectory tortuosity index may be determined based on the well inclination arc length and the azimuth arc length.

[0069] In some embodiments, since the wellbore trajectory is a curve in three-dimensional space, the key parameters of the curve are represented by the well inclination angle and the azimuth angle. The wellbore trajectory torsion index in three-dimensional space can be obtained by calculating the wellbore trajectory torsion index and the azimuth torsion index. Here, wellbore trajectory torsion index samples and azimuth torsion index samples can be obtained in advance, and then a wellbore trajectory torsion index prediction model (or a wellbore trajectory torsion index calculation model) can be established based on the wellbore trajectory torsion index samples and the azimuth torsion index samples. In actual application, the wellbore trajectory torsion index and the azimuth torsion index can be processed based on the pre-established wellbore trajectory torsion index prediction model to accurately and quickly obtain the wellbore trajectory torsion index.

[0070] In some embodiments, the wellbore trajectory tortuosity index may be determined based on the well inclination arc length and the azimuth arc length, and may include:

[0071] According to the well inclination arc length and azimuth arc length, the well inclination torsional index is determined according to the following formula:

[0072]

[0073] Among them, TI Inc is the well deviation torsional index; n is the number of curved sections in the wellbore trajectory; L IncC is the well inclination arc length, m; L AzmCi is the azimuth arc length of the i-th curve, m; L AzmSi is the inclination arc length of the i-th curve;

[0074] According to the well inclination arc length and azimuth arc length, the azimuth distortion index is determined according to the following formula:

[0075]

[0076] Among them, TI Azmc is the azimuth distortion index; n is the number of curved segments in the wellbore trajectory; L Azmc is the azimuth arc length, m; L AzmCi is the azimuth arc length of the i-th curve, m; L AzmSi is the inclination arc length of the i-th curve;

[0077] According to the wellbore trajectory torsional index and the azimuth torsional index, the wellbore trajectory torsional index is determined according to the following formula (which can be the wellbore trajectory torsional index prediction model mentioned above):

[0078]

[0079] Among them, TI 3Dis the wellbore trajectory tortuosity index in three-dimensional space (or wellbore tortuosity index in three-dimensional space); TI Inc is the well deviation torsional index; TI Azmc is the orientation distortion index.

[0080] In some embodiments, the above-mentioned acquisition of the wellbore trajectory tortuosity index membership corresponding to different completion methods may include:

[0081] Obtaining an eye trajectory tortuosity, a first target eye trajectory tortuosity, and a second target eye trajectory tortuosity, wherein the first target eye trajectory tortuosity is greater than a preset eye trajectory tortuosity threshold, and the second target eye trajectory tortuosity is less than the preset eye trajectory tortuosity threshold;

[0082] The wellbore trajectory tortuosity index membership is determined according to the wellbore trajectory tortuosity, the first target wellbore trajectory tortuosity and the second target wellbore trajectory tortuosity.

[0083] In some embodiments, the first target eye trajectory tortuosity can be the maximum value of the wellbore trajectory tortuosity, and the second target eye trajectory tortuosity can be the minimum value of the wellbore trajectory tortuosity. The eye trajectory tortuosity can be the wellbore trajectory tortuosity index. The method for obtaining the eye trajectory tortuosity can refer to the method for obtaining the wellbore trajectory tortuosity index and will not be further described in this specification. The preset eye trajectory tortuosity threshold can be set according to actual needs and is not specifically limited in this specification.

[0084] In some embodiments, determining the wellbore trajectory tortuosity index membership based on the wellbore trajectory tortuosity, the first target wellbore trajectory tortuosity, and the second target wellbore trajectory tortuosity may include:

[0085] The wellbore trajectory tortuosity index membership is determined according to the following formula (linear processing method):

[0086]

[0087] Among them, TI 3D is the wellbore trajectory tortuosity index membership, x ij is the tortuosity of the wellbore trajectory at well i and depth j, x j,max is the first target well trajectory tortuosity (i.e. the maximum value of the well trajectory tortuosity), x j,min is the second target wellbore trajectory tortuosity (i.e., the minimum value of the wellbore trajectory tortuosity).

[0088] In some embodiments, the above-mentioned acquisition of wellbore temperature membership corresponding to different completion methods may include:

[0089] Establish the membership function corresponding to the wellbore temperature;

[0090] According to the membership function corresponding to the wellbore temperature, the wellbore temperature membership corresponding to different completion methods is determined.

[0091] In some embodiments, the membership function corresponding to the wellbore temperature may be established according to the following formula (exponential processing method):

[0092]

[0093] Where T is the wellbore temperature membership; x is the wellbore temperature, °C; exp is the exponential function with the natural constant e as the base.

[0094] In some embodiments, the wellbore temperature membership corresponding to different completion methods can be determined based on the membership function corresponding to the above-mentioned wellbore temperature. The membership function corresponding to the wellbore temperature can be established in the following manner: First, the pre-set temperature division rule can be called. Then, the wellbore temperature can be divided according to the pre-set temperature division rule. The pre-set temperature division rule can be, for example: when the wellbore temperature is <150°C, it can be divided into normal temperature; when the wellbore temperature is 150°C≤t<177°C, it can be divided into high temperature; when the wellbore temperature is 177°C≤t≤200°C, it can be divided into ultra-high temperature. Finally, based on the division result of the wellbore temperature, based on fuzzy mathematics theory, an exponential processing method can be used to construct the membership function corresponding to the wellbore temperature (the constant in the membership function corresponding to the wellbore temperature can be determined in combination with field experience and the basic type of fuzzy membership function (such as exponential type), and this specification does not make specific restrictions on this).

[0095] In some embodiments, the wellbore pressure membership can be determined based on the membership function corresponding to the wellbore pressure, the leakage membership can be determined based on the membership function corresponding to the leakage, the wellbore diameter expansion rate membership can be determined based on the membership function corresponding to the wellbore diameter expansion rate, and the gap expansion rate membership can be determined based on the membership function corresponding to the gap expansion rate. The membership function corresponding to the wellbore pressure, the membership function corresponding to the leakage, and the membership function corresponding to the wellbore diameter expansion rate can be established in the same manner as the membership function corresponding to the wellbore temperature, i.e., the pre-set pressure division rule, the pre-set leakage division rule, and the pre-set wellbore diameter expansion rate division rule can all be called in sequence. Then, the wellbore pressure is divided according to the pre-set pressure division rule, the leakage is divided according to the pre-set leakage division rule, and the wellbore diameter expansion rate (the wellbore diameter expansion rate can also be called the wellbore diameter expansion rate) is divided according to the pre-set wellbore diameter expansion rate division rule. Finally, based on the division results corresponding to wellbore pressure, leakage, and wellbore expansion rate, the exponential processing method was used based on fuzzy mathematics theory to establish the membership functions corresponding to wellbore pressure, leakage, wellbore expansion rate, and gap expansion rate.

[0096] The membership function corresponding to the wellbore pressure can be established according to the following formula:

[0097]

[0098] Where P is the wellbore pressure membership; x1 is the wellbore pressure; exp is the exponential function with the natural constant e as the base.

[0099] The membership function corresponding to the loss amount can be established according to the following formula:

[0100]

[0101] Among them, η is the membership of the loss amount; x2 is the loss amount.

[0102] The membership function corresponding to the wellbore expansion rate can be established according to the following formula:

[0103]

[0104] Among them, K is the well diameter expansion rate membership; x3 is the well diameter expansion rate.

[0105] In some embodiments, the gap expansion rate may be a gap expansion rate between tubulars, a gap expansion rate between a downhole tool (e.g., a drilling tool) and a casing, or a gap expansion rate between open hole sections, or may be referred to as a gap value between tubulars, a gap value between a downhole tool (e.g., a drilling tool) and a casing, or a gap value between open hole sections. The membership of the gap expansion rate may be determined based on a membership function corresponding to the gap expansion rate, wherein the membership function corresponding to the gap expansion rate may be established according to the following formula:

[0106]

[0107] Among them, K DS is the membership of the gap expansion rate; x4 is the gap value, in mm; exp is an exponential function with the natural constant e as the base; a and b are constants in the fuzzy membership function, which can be determined according to the tubing size and tool size of the specific well in actual application.

[0108] In some embodiments, the membership of the gap expansion rate corresponding to different completion methods can be determined based on the membership function corresponding to the above-mentioned gap expansion rate. The membership function corresponding to the gap expansion rate can be established in the following manner: First, the pre-set gap expansion rate division rule is retrieved. Then, the gap expansion rate can be divided according to the pre-set gap expansion rate division rule. The pre-set gap expansion rate division rule can be, for example: when the gap value is ≤10mm, the gap degree can be divided into small; when the gap value is 10mm-50mm, the gap degree can be divided into medium; when the gap value is ≥50mm, the gap degree can be divided into large. Finally, based on the division result of the gap expansion rate, based on fuzzy mathematics theory, a small linear processing method is used to construct the membership function corresponding to the gap expansion rate.

[0109] In some embodiments, the above-mentioned preset temperature division rules and the preset pressure division rules can be as shown in Table 1, and the above-mentioned preset leakage volume division rules, the preset wellbore expansion rate division rules, and the preset gap expansion rate division rules can be as shown in Table 2, Table 3, and Table 4, respectively.

[0110] Table 1 Temperature and pressure division rules

[0111]

[0112] Table 2 Rules for dividing the amount of leakage

[0113] Lost circulation type Microleakage Small leak Middle leakage Big leak Serious loss Leakage rate (m3 / d) ≤120 120～360 360～720 720～1440 ≥1440

[0114] Table 3 Well diameter expansion rate classification rules

[0115] Grading Small middle big Well diameter expansion rate (%) ≤8 8～15 ≥15

[0116] Table 4 Gap expansion rate division rules

[0117] Degree of clearance Small middle big Clearance value (mm) ≤10 10～50 ≥50

[0118] In some embodiments, membership can refer to a numerical representation of the degree to which an element belongs to a set, usually represented by a real number between 0 and 1. It is a basic concept used to describe fuzzy sets, and can be used to represent the position and degree of belonging of an element in a set. In fuzzy set theory, membership is defined as a similarity measure between an element and a set, that is, a numerical value representing the degree to which an element belongs to a set. By obtaining the wellbore trajectory torsional index membership, wellbore temperature membership, wellbore pressure membership, leakage membership, wellbore diameter expansion rate membership, and gap expansion rate membership corresponding to different completion methods, the foundation can be laid for accurately and quickly calculating the wellbore complexity index corresponding to different completion methods, thereby quantifying the complexity of the oil and gas wellbore itself.

[0119] S102: Using a wellbore complexity index prediction model to process the membership of wellbore complexity data corresponding to different completion methods, to obtain wellbore complexity indices corresponding to different completion methods, wherein the wellbore complexity index prediction model is pre-established using the membership corresponding to the wellbore complexity data samples.

[0120] In some embodiments, the wellbore complexity data samples can be obtained first, that is, the wellbore trajectory tortuosity index samples, wellbore temperature samples, wellbore pressure samples, leakage samples, wellbore diameter expansion rate samples, and gap expansion rate samples are obtained. Then, the membership corresponding to the wellbore complexity data samples is obtained, that is, the membership corresponding to the wellbore trajectory tortuosity index samples, the membership corresponding to the wellbore temperature samples, the membership corresponding to the wellbore pressure samples, the membership corresponding to the leakage sample, the membership corresponding to the wellbore diameter expansion rate sample, and the membership corresponding to the gap expansion rate sample are obtained. Finally, according to the membership corresponding to the wellbore trajectory tortuosity index samples, the membership corresponding to the wellbore temperature samples, the membership corresponding to the wellbore pressure samples, the membership corresponding to the leakage sample, the membership corresponding to the wellbore diameter expansion rate sample, and the membership corresponding to the gap expansion rate sample, a wellbore complexity index prediction model can be established according to the following formula:

[0121] D co =w TI3D *TI 3D +w T *T+w P *P+w η *η+w K *K+w DS *K DS

[0122] Among them, D co is the wellbore complexity index; w TI3D is the weight corresponding to the wellbore trajectory tortuosity index sample; TI 3D is the membership degree corresponding to the wellbore trajectory tortuosity index sample; w T is the weight corresponding to the wellbore temperature sample; T is the membership degree corresponding to the wellbore temperature sample; w P is the weight corresponding to the wellbore pressure sample; P is the membership degree corresponding to the wellbore pressure sample; w η is the weight corresponding to the missing sample; η is the membership degree corresponding to the missing sample; w K is the weight corresponding to the well diameter expansion rate sample; K is the membership degree corresponding to the well diameter expansion rate sample; w DS is the weight corresponding to the gap expansion rate sample; K DS is the membership degree corresponding to the gap expansion rate sample.

[0123] Among them, the weights corresponding to the wellbore complexity data samples can be determined by statistically analyzing the on-site downhole complexity and combining it with the expert system scoring. Of course, the method of determining the weights is not limited to the above examples. Technical personnel in the relevant field may make other changes based on the technical essence of the embodiments of this specification. However, as long as the functions and effects achieved are the same or similar to those of the embodiments of this specification, they should be covered within the scope of protection of the embodiments of this specification.

[0124] By establishing a wellbore complexity index prediction model, the efficiency and accuracy of obtaining the wellbore complexity index can be improved. In specific implementation, it is only necessary to input the membership of the wellbore complexity data corresponding to different completion methods into the wellbore complexity index prediction model to obtain the wellbore complexity index corresponding to different completion methods. Before selecting the optimal completion method, by considering the complexity of the oil and gas wellbore itself, that is, considering the wellbore complexity index obtained from data such as the eye trajectory torsion index, wellbore temperature, wellbore pressure, leakage, wellbore expansion rate, and gap expansion rate, it can lay the foundation for the subsequent establishment of a comprehensive and reliable risk assessment system, quantify the completion string entry risks of different completion methods, and select the optimal completion method.

[0125] S103: Acquire engineering data corresponding to different completion methods, and determine risk levels corresponding to different completion methods based on the wellbore complexity index and the engineering data.

[0126] In some embodiments, the engineering data may include: tubing running friction, tubing buckling index, and triaxial safety factor. Accordingly, the risk levels corresponding to different completion methods are determined based on the wellbore complexity index and the engineering data. In specific implementation, the following steps may be involved:

[0127] Determining target string entry friction resistances corresponding to different completion methods in sequence from the string entry friction resistances corresponding to different completion methods, and determining target triaxial safety factors corresponding to different completion methods in sequence from the triaxial safety factors corresponding to different completion methods, wherein the target string entry friction group is greater than a preset string entry friction group threshold, and the target triaxial safety factor is less than a preset triaxial safety factor threshold;

[0128] The risk scoring rules corresponding to the wellbore complexity index, string running friction resistance, string buckling index and triaxial safety factor are retrieved in sequence, and the risk values ​​corresponding to the wellbore complexity index, target string running friction resistance, string buckling index and target triaxial safety factor are determined in sequence;

[0129] The risk values ​​of the wellbore complexity index, target string running friction, string buckling index, and target triaxial safety factor are summed to obtain the comprehensive risk values ​​corresponding to different completion methods.

[0130] According to the comprehensive risk values ​​corresponding to different completion methods, the risk levels corresponding to different completion methods are determined.

[0131] In some embodiments, the above-mentioned pipe string running friction resistance may be the friction resistance generated when the completion string is run in (the completion string run in may be a part of the completion operation) by different completion methods. The pipe string running friction resistance and the triaxial safety factor can be calculated using mature mechanical related theoretical models, which will not be described in detail in this specification. The above-mentioned pipe string buckling index can be defined as describing whether the pipe string buckles and the degree of buckling, and the buckling index can be determined based on the axial force and the buckling imminent load (the calculation of the axial force and the buckling imminent load can refer to the existing calculation formula, which will not be described in detail in this specification). Specifically, the above-mentioned pipe string buckling index can be determined based on the axial force and the buckling imminent load according to the following formula:

[0132]

[0133] Where b is the pipe string buckling index. If the axial force is less than the critical buckling load, the pipe string buckling index is 1, indicating that the pipe string has not buckled. If the axial force is greater than the critical buckling load, the pipe string buckling index is the ratio of the axial force to the critical buckling load, indicating that the pipe string has buckled.

[0134] In some embodiments, after obtaining engineering data such as the tubing string entry friction, tubing string buckling index, and triaxial safety factor, the wellbore complexity index, tubing string entry friction, tubing string buckling index, and triaxial safety factor can be combined to explore the impact of the wellbore's own complexity and engineering conditions on the completion method selection, thereby improving the accuracy of completion method selection. Specifically, first, the target tubing string entry friction corresponding to each completion method can be determined in sequence from the tubing string entry friction corresponding to each completion method, that is, the maximum tubing string entry friction corresponding to each completion method can be determined. The target triaxial safety factor corresponding to each completion method can also be determined in sequence from the triaxial safety factors corresponding to each completion method, that is, the minimum triaxial safety factor corresponding to each completion method can be determined. Then, the risk scoring rules corresponding to the wellbore complexity index, tubing string entry friction, tubing string buckling index, and triaxial safety factor can be sequentially retrieved to determine the risk values ​​corresponding to the wellbore complexity index, target tubing string entry friction, tubing string buckling index, and target triaxial safety factor. Specifically, the risk scoring rules corresponding to the wellbore complexity index can be retrieved to determine the risk value corresponding to the wellbore complexity index; the risk scoring rules corresponding to the tubing entry friction resistance can be retrieved to determine the risk value corresponding to the target tubing entry friction resistance; the risk scoring rules corresponding to the tubing buckling index can be retrieved to determine the risk value corresponding to the tubing buckling index; and the risk scoring rules corresponding to the triaxial safety factor can be retrieved to determine the risk value corresponding to the target triaxial safety factor. Finally, the sum of the risk values ​​corresponding to the wellbore complexity index, the target tubing entry friction resistance, the tubing buckling index, and the target triaxial safety factor can be determined as a comprehensive risk value. Based on the comprehensive risk values ​​corresponding to different completion methods, the corresponding risk levels of different completion methods can be determined.

[0135] The risk scoring rules for the wellbore complexity index can be: when the wellbore complexity index is ≤ 200, the risk value corresponding to the wellbore complexity index can be determined to be 1, and the risk rating is L (low); when the wellbore complexity index is 200 < ≤ 400, the risk value corresponding to the wellbore complexity index can be determined to be 3, and the risk rating is M (medium); when the wellbore complexity index is > 400, the risk value corresponding to the wellbore complexity index can be determined to be 5, and the risk rating is H (high). The risk scoring rules for the wellbore complexity index can be found in Table 5.

[0136] The risk scoring rules for string running friction resistance can be as follows: when the friction tonnage is ≤ 3t (30kN), the risk value for string running friction resistance can be determined to be 1, and the risk rating is L (low); when the friction tonnage is 3t (30kN) < 5t (50kN), the risk value for string running friction resistance can be determined to be 3, and the risk rating is M (medium); when the friction tonnage is > 5t (50kN), the risk value for string running friction resistance can be determined to be 5, and the risk rating is H (high). The risk scoring rules for string running friction resistance can be found in Table 6.

[0137] The risk scoring rules for the pipe string buckling index can be as follows: when the buckling index = 1, the risk value corresponding to the pipe string buckling index can be determined to be 1, and the risk rating can be L (low); when the buckling index is 1 < the buckling index < 2, the risk value corresponding to the pipe string buckling index can be determined to be 3, and the risk rating can be M (medium); when the buckling index is ≥ 2, the risk value corresponding to the pipe string buckling index can be determined to be 5, and the risk rating can be H (high). The risk scoring rules for the pipe string buckling index can be found in Table 7.

[0138] The risk scoring rules for the triaxial safety factor can be as follows: when the triaxial safety factor is ≥1.5, the risk value corresponding to the triaxial safety factor can be determined to be 1, and the risk rating is L (low); when the triaxial safety factor is 1.0 ≤ <1.5, the risk value corresponding to the triaxial safety factor can be determined to be 3, and the risk rating is M (medium); when the triaxial safety factor is <1.0, the risk value corresponding to the triaxial safety factor can be determined to be 5, and the risk rating is H (high). The risk scoring rules for the triaxial safety factor can be found in Table 8.

[0139] Table 5 Wellbore complexity index scoring table

[0140]

[0141]

[0142] Table 6 Pipe string entry friction rating table

[0143]

[0144] Table 7 Pipe string buckling index score table

[0145] scope Flexion index = 1 1 < buckling index < 2 Flexion index ≥ 2 Risk Rating L (low) M (medium) H (high) Score (points) 1 3 5

[0146] Table 8 Three-axis safety factor table

[0147]

[0148] In some embodiments, a risk score combination chart (or table) can be established in advance based on the risk values ​​corresponding to the wellbore complexity index, string running friction, string buckling index and triaxial safety factor by superimposing 81 grid scores, such as Figure 2 As shown, Figure 2In the example, the risk values ​​of the wellbore complexity index can be set to 1, 3, and 5 respectively. Then, when the risk value of the wellbore complexity index is 1, the risk value of the pipe string friction resistance is set to one group of 1, 3, and 5, the risk value of the pipe string buckling index is set to three groups of 1, 3, and 5, and the risk value of the triaxial safety factor is set to 9 groups of 1, 3, and 5. When the risk value of the wellbore complexity index is set to 3 or 5, the same is true above. And so on, a risk score combination chart (or table) is finally obtained. After establishing the risk score combination chart (or table), a risk level classification chart (or table) can be further established based on the total score (i.e., comprehensive risk value) in the risk score combination chart (or table), such as Figure 3 As shown, Figure 3 Medium: If the comprehensive risk value is between 4 and 6, the completion method's risk level is considered acceptable; if it is between 8 and 10, the completion method's risk level is considered critical; and if it is between 12 and 20, the completion method's risk level is considered unacceptable. Acceptable risk indicates a low risk during tubing run; near risk indicates a moderate risk during tubing run; and unacceptable risk indicates a high risk during tubing run.

[0149] In some embodiments, the risk levels corresponding to different completion methods are determined based on the comprehensive risk values ​​corresponding to the different completion methods. That is, the risk level classification chart (or table) established in advance can be used to determine the risk range of the comprehensive risk value, and finally, the risk levels corresponding to the different completion methods are determined. For example, if the comprehensive risk value for openhole completion is 8, the comprehensive risk value for liner completion is 6, and the comprehensive risk value for perforation completion is 6, then the comprehensive risk values ​​for liner completion and perforation completion can be determined to be within the risk range of 4-6, and the risk level of liner completion and perforation completion is acceptable, indicating that the risk of string running in liner completion and perforation completion is low. If the comprehensive risk value for openhole completion is 8, then the risk range is within the range of 8-10, and then the risk level of openhole completion can be determined to be critical, indicating that the risk of string running in openhole completion is moderate.

[0150] By pre-establishing a risk scoring combination diagram (or table) based on the wellbore complexity index, string running friction, string buckling index, and triaxial safety factor, and then further establishing a risk level classification diagram (or table), the risk levels corresponding to different completion methods can be accurately and quickly determined based on their comprehensive risk values, thereby laying the foundation for subsequently screening the optimal completion method based on the risk levels of different completion methods.

[0151] S104: Selecting a target completion method from different completion methods based on risk levels corresponding to different completion methods.

[0152] In some embodiments, the above-mentioned selection of a target completion method from different completion methods based on the risk levels corresponding to different completion methods may include:

[0153] The risk levels corresponding to different completion methods are compared to determine a target risk level in the risk level and a target completion method corresponding to the target risk level, wherein the target risk level includes an acceptable risk.

[0154] In some embodiments, after determining the risk levels corresponding to different completion methods, such as determining that the risk level of open hole completion is critical risk and the risk levels of liner completion and perforation completion are acceptable risks, the risk levels corresponding to different completion methods can be compared, and then the acceptable risk among the risk levels can be selected, and the target completion method corresponding to the acceptable risk can be determined. The target completion method can be used as the optimal completion method, that is, the completion method with the lowest tubing running risk. For example, the risk levels of open hole completion, liner completion, and perforation completion can be compared. Since the risk levels of liner completion and perforation completion are acceptable risks, liner completion and perforation completion can be used as the optimal completion methods.

[0155] In some embodiments, after selecting a target completion method from different completion methods, the specific implementation may further include:

[0156] Based on the risk levels corresponding to different completion methods, formulate corresponding risk control measures for different completion methods;

[0157] Based on the risk control measures, different degrees of risk control are performed on completion operations under different completion methods.

[0158] In some embodiments, after selecting the optimal completion method, guidance can also be provided for the formulation of risk management measures during the completion operation. That is, different risk management measures can be formed according to the risk levels of different completion methods. For completion methods that are at near risk or unacceptable risk, risk management can be strengthened during the completion operation, thereby achieving the purpose of ensuring the safety of the completion operation.

[0159] The above method is described below in conjunction with a specific embodiment. However, it should be noted that this specific embodiment is only for better illustrating the present application and does not constitute an improper limitation to the present application.

[0160] Before implementation, data samples such as well depth, well inclination, azimuth, leakage, wellbore expansion rate, and clearance expansion rate can be collected during actual oil and gas well drilling. The wellbore inclination and azimuth data samples can also be used to determine wellbore arc length samples and azimuth arc length samples. Based on the wellbore arc length and azimuth arc length samples, wellbore torsion index samples and azimuth torsion index samples can then be determined. A wellbore trajectory torsion index prediction model can then be established based on the wellbore trajectory torsion index prediction model. Wellbore trajectory torsion index samples can also be obtained by calculating wellbore temperature and wellbore pressure samples using existing mature theoretical models. The membership degrees corresponding to leakage samples, wellbore expansion rate samples, clearance expansion rate samples, wellbore trajectory torsion index samples, wellbore temperature samples, and wellbore pressure samples can also be determined. Based on the membership degrees corresponding to the data samples, a wellbore complexity index prediction model can then be established.

[0161] In specific implementation, the wellbore complexity index prediction model can be used to process the membership of wellbore complexity data corresponding to different completion methods to obtain the wellbore complexity index corresponding to each completion method. Next, the tubing running friction, tubing buckling index, and triaxial safety factor corresponding to each completion method are obtained. A tubing running risk assessment index system based on the wellbore complexity index, tubing running friction, tubing buckling index, and triaxial safety factor is then established. Based on this tubing running risk assessment index system, the tubing running risks of different completion methods can be quantitatively evaluated, and the completion method with the lowest risk can be selected as the optimal completion method.

[0162] By comprehensively considering key engineering data related to the wellbore complexity of oil and gas wells and the risks associated with string running, a risk assessment system was established to quantitatively analyze the string running risks associated with different completion methods and evaluate their adaptability. This effectively guides the selection of completion methods and improves their accuracy. Determining the string running risks associated with different completion methods also provides guidance for the development of risk management measures during completion operations, ensuring the safety of completion operations.

[0163] In a specific scenario, a horizontal well can be used as an example to comprehensively evaluate the string running risks in different completion methods and select the optimal completion method. The specific implementation can include the following steps:

[0164] Step 1: Collect basic parameters such as well depth, well inclination, azimuth, leakage, wellbore expansion rate, and the gap between the tubing string / downhole tool and the casing / open hole section (or gap expansion rate) during the actual drilling process of the oil and gas well;

[0165] Step 2: Pre-establishing a wellbore trajectory torsional index prediction model (or a wellbore trajectory torsional index calculation model), and obtaining the wellbore trajectory torsional index at different depths throughout the wellbore based on the established wellbore trajectory torsional index prediction model;

[0166] Step 3: Based on the existing mature theoretical model, obtain wellbore temperature and pressure data;

[0167] Step 4: Pre-establish a wellbore complexity index prediction model (or wellbore complexity index calculation model). The weights of various influencing factors in the wellbore complexity index prediction model can be determined by statistically analyzing the complexity of the wellbore on-site and combining it with expert system scoring. The wellbore complexity index can be obtained based on the wellbore complexity index prediction model established in advance.

[0168] Step 5: Obtain engineering data, such as string running friction, string buckling index, and triaxial safety factor. Then, establish a string running risk assessment index system for different completion methods based on the wellbore complexity index, string running friction, string buckling index, and triaxial safety factor (i.e., a risk scoring combination chart (or table) and a risk level classification chart (or table) can be established). Quantitatively evaluate the string running risks of different completion methods and determine the optimal completion method.

[0169] Among them, see Figure 4 As shown, Figure 4 The curves of well inclination and azimuth changing with well depth are shown. Figure 4 The left side shows the curve of well inclination (or horizontal section extension distance) changing with well depth. Figure 4 The right side shows the curve of the change of azimuth with well depth. Figure 4 As can be seen on the left: as the well depth increases, the horizontal section extension distance also increases. Figure 4 As can be seen on the right, as the horizontal segment extends, the azimuth gradually changes from north to south.

[0170] See Figure 5 As shown, Figure 5 The curve of wellbore size (i.e. well diameter) changing with well depth is shown. Figure 5 It can be seen from the figure that with the increase of well depth, the wellbore size fluctuates. When the well depth increases to 7500m, 7650m and 7700m, the increase in well diameter is large. Among them, when the well depth reaches 7700m, the increase in well diameter is the largest.

[0171] See Figure 6 As shown, Figure 6 The curve of the wellbore trajectory tortuosity index changing with well depth is shown. Figure 6It can be seen that when the well depth is 0m-5000m, the corresponding wellbore trajectory torsional index does not change; when the well depth is 5000m-8000m, the corresponding wellbore trajectory torsional index does not change; when the well depth increases to 5000m, the wellbore trajectory torsional index begins to decrease.

[0172] See Figure 7 、 Figure 8 As shown, Figure 7 The curve of pressure changing with well depth is shown. Figure 8 The temperature variation curve with well depth is shown. Figure 7 、 8 It can be seen from the figure that as the well depth increases, the pressure and temperature increase linearly.

[0173] See Figure 9 、 Figure 10 、 Figure 11 As shown, Figure 9 The comprehensive evaluation results of string running risk during open hole completion are shown. Figure 10 The comprehensive evaluation results of the risk of string running during liner completion are shown. Figure 11 The comprehensive evaluation results of the risk of string running during perforation completion are shown. Figure 9 、 Figure 10 、 Figure 11 It can be seen that the risk score or risk value of openhole completion is 8, the risk score or risk value of liner completion is 6, and the risk score or risk value of perforated completion is 6; the complexity index or wellbore complexity index of openhole completion, liner completion, and perforated completion is 141.9; the maximum friction resistance of openhole completion, liner completion, and perforated completion is 157.9 kN, 113.2 kN, and 93.4 kN, respectively; the minimum triaxial safety factors of openhole completion, liner completion, and perforated completion are 2.24, 2.14, and 2.12, respectively (where the minimum safety factor is calculated based on the calculation of axial force, friction resistance, and Mises stress with the help of existing mechanical analysis software); the buckling sections where tubing buckling occurs in openhole completion, liner completion, and perforated completion are 6394-6710 m, none, and none, respectively. The above data and the pre-established risk level classification diagram (or table) can be combined to establish a comparison diagram (or table) of the comprehensive evaluation results of the string running risk of open hole completion, liner completion and perforation completion. Figure 12 It can be seen from the figure that the risk level of open hole completion is critical, which is higher than that of liner completion and perforation completion. Therefore, liner completion or perforation completion is recommended for this well, that is, liner completion or perforation completion can be used as the optimal completion method.

[0174] See Figure 13 As shown, Figure 13The figure shows the predicted results of the resistance tonnage of the open hole completion string (wherein the resistance tonnage prediction result can be calculated based on the weight of the string in the mud minus the friction resistance. The existing resistance tonnage calculation algorithm can be referred to and will not be described in detail in this specification). Figure 13 As can be seen from the data, the resistance tonnage encountered in openhole completions begins to change as the well depth increases by 7,000 m. At depths between 7,000 m and 9,000 m, the resistance tonnage first increases, then decreases, and then increases again. By predicting the resistance tonnage encountered in openhole completions, targeted risk prevention and control measures can be implemented to ensure the safety of openhole completion operations.

[0175] Although this specification provides examples such as the following examples or the accompanying Figure 14 The method operation steps or device structure shown are conventional or without creative labor, but the method or device may include more or fewer operation steps or module units after partial merger. In the steps or structures where there is no necessary causal relationship logically, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure shown in the embodiments or drawings of this specification. When the method or module structure is applied to an actual device, server or terminal product, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or drawings (for example, in an environment of parallel processors or multi-threaded processing, or even in an implementation environment of distributed processing and server clusters).

[0176] Based on the above-mentioned method for selecting a completion method, this specification also proposes an embodiment of a device for selecting a completion method. Figure 14 As shown, the device may specifically include the following modules:

[0177] The acquisition module 1401 can be used to obtain the membership of wellbore complexity data corresponding to different completion methods;

[0178] Prediction module 1402 may be configured to process the membership of wellbore complexity data corresponding to different completion methods using a wellbore complexity index prediction model to obtain wellbore complexity indices corresponding to the different completion methods. The wellbore complexity index prediction model is pre-established using the membership corresponding to the wellbore complexity data samples.

[0179] The determination module 1403 may be used to obtain engineering data corresponding to different completion methods, and determine the risk levels corresponding to the different completion methods based on the wellbore complexity index and the engineering data;

[0180] The selection module 1404 may be used to select a target completion method from different completion methods based on a comparison of risk levels corresponding to different completion methods.

[0181] In some embodiments, the wellbore complexity data in the above-mentioned acquisition module 1401 may include: wellbore trajectory tortuosity index, wellbore temperature, wellbore pressure, leakage, wellbore diameter expansion rate, and gap expansion rate; the above-mentioned acquisition module 1401 can be specifically used to obtain the wellbore trajectory tortuosity index membership, wellbore temperature membership, wellbore pressure membership, leakage membership, wellbore diameter expansion rate membership, and gap expansion rate membership corresponding to different completion methods.

[0182] In some embodiments, the wellbore complexity data in the above-mentioned acquisition module 1401 may also include: well inclination and azimuth, the well inclination can be used to determine the well inclination arc length, and the azimuth can be used to determine the azimuth arc length; the wellbore trajectory torsionalness index is determined based on the well inclination arc length and the azimuth arc length.

[0183] In some embodiments, the acquisition module 1401 can also be used to obtain the eye trajectory tortuosity, the first target eye trajectory tortuosity and the second target eye trajectory tortuosity, wherein the first target eye trajectory tortuosity is greater than the preset eye trajectory tortuosity threshold, and the second target eye trajectory tortuosity is less than the preset eye trajectory tortuosity threshold; and determine the wellbore trajectory tortuosity index membership according to the eye trajectory tortuosity, the first target eye trajectory tortuosity and the second target eye trajectory tortuosity.

[0184] In some embodiments, the acquisition module 1401 may be further configured to establish a membership function corresponding to the wellbore temperature; and determine the wellbore temperature membership corresponding to different completion methods based on the membership function corresponding to the wellbore temperature.

[0185] In some embodiments, the prediction module 1402 may be used to establish a wellbore complexity index prediction model according to the following formula:

[0186] D co =w TI3D *TI 3D +w T *T+w P *P+w η *η+w K *K+w DS *K DS

[0187] Among them, D co is the wellbore complexity index; w TI3D is the weight corresponding to the wellbore trajectory tortuosity index sample; TI 3D is the membership degree corresponding to the wellbore trajectory tortuosity index sample; w T is the weight corresponding to the wellbore temperature sample; T is the membership degree corresponding to the wellbore temperature sample; w P is the weight corresponding to the wellbore pressure sample; P is the membership degree corresponding to the wellbore pressure sample; wη is the weight corresponding to the missing sample; η is the membership degree corresponding to the missing sample; w K is the weight corresponding to the well diameter expansion rate sample; K is the membership degree corresponding to the well diameter expansion rate sample; w DS is the weight corresponding to the gap expansion rate sample; K DS is the membership degree corresponding to the gap expansion rate sample.

[0188] In some embodiments, the engineering data in the above-mentioned determination module 1403 may include: pipe string entry friction resistance, pipe string buckling index and triaxial safety factor; the above-mentioned determination module 1403 can be specifically used to determine the target pipe string entry friction resistance corresponding to different completion methods from the pipe string entry friction resistance corresponding to different completion methods, and determine the target triaxial safety factor corresponding to different completion methods from the triaxial safety factors corresponding to different completion methods, the target pipe string entry friction group is greater than the preset pipe string entry friction group threshold, and the target triaxial safety factor is less than the preset triaxial safety factor. Full coefficient threshold; sequentially retrieve the risk scoring rules corresponding to the wellbore complexity index, tubing running friction resistance, tubing buckling index, and triaxial safety factor, and sequentially determine the risk values ​​corresponding to the wellbore complexity index, target tubing running friction resistance, tubing buckling index, and target triaxial safety factor; sum the risk values ​​corresponding to the wellbore complexity index, target tubing running friction resistance, tubing buckling index, and target triaxial safety factor to obtain the comprehensive risk values ​​corresponding to different completion methods; and determine the risk levels corresponding to different completion methods based on the comprehensive risk values ​​corresponding to different completion methods.

[0189] In some embodiments, the selection module 1404 may be specifically used to compare risk levels corresponding to different completion methods, determine a target risk level in the risk level and a target completion method corresponding to the target risk level, wherein the target risk level includes an acceptable risk.

[0190] It should be noted that the units, devices or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described in terms of functions and are divided into various modules and described separately. Of course, when implementing this specification, the functions of each module can be implemented in the same or multiple software and / or hardware, or the module that implements the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0191] As can be seen from the above, the device for selecting a completion method provided in the embodiments of this specification can not only quantify the string running risks of different completion methods, but also accurately select the completion method with the lowest risk level.

[0192] The embodiments of this specification also provide an electronic device based on the above-mentioned completion method selection method, including a processor and a memory for storing processor executable instructions. When the processor is specifically implemented, it can perform the following steps according to the instructions: obtain the membership of the wellbore complexity data corresponding to different completion methods; use the wellbore complexity index prediction model to process the membership of the wellbore complexity data corresponding to the different completion methods to obtain the wellbore complexity index corresponding to the different completion methods, and the wellbore complexity index prediction model is pre-established using the membership corresponding to the wellbore complexity data sample; obtain engineering data corresponding to the different completion methods, and determine the risk level corresponding to the different completion methods based on the wellbore complexity index and the engineering data; select a target completion method from the different completion methods based on the risk level corresponding to the different completion methods.

[0193] In order to complete the above instructions more accurately, refer to Figure 15 As shown, the embodiment of this specification also provides another specific electronic device, wherein the electronic device includes a network communication port 1501, a processor 1502 and a memory 1503, and the above structures are connected through internal cables so that each structure can perform specific data interaction.

[0194] The network communication port 1501 can be used to obtain the membership of wellbore complexity data corresponding to different completion methods.

[0195] The processor 1502 can be specifically used to use the wellbore complexity index prediction model to process the membership of the wellbore complexity data corresponding to different completion methods to obtain the wellbore complexity index corresponding to the different completion methods, and the wellbore complexity index prediction model is pre-established using the membership corresponding to the wellbore complexity data samples; obtain the engineering data corresponding to the different completion methods, and determine the risk levels corresponding to the different completion methods based on the wellbore complexity index and the engineering data; select the target completion method from the different completion methods based on the risk levels corresponding to the different completion methods.

[0196] The memory 1503 may be specifically used to store corresponding instruction programs.

[0197] In this embodiment, the network communication port 1501 can be a virtual port that is bound to different communication protocols, thereby being capable of sending or receiving different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0198] In this embodiment, the processor 1502 may be implemented in any suitable manner. For example, the processor may take the form of a microprocessor or a processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, an embedded microcontroller, etc. This specification is not intended to limit this.

[0199] In this embodiment, the memory 1503 may include multiple levels. In a digital system, anything that can store binary data can be a memory; in an integrated circuit, a circuit with a storage function that has no physical form is also called a memory, such as RAM, FIFO, etc.; in a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0200] An embodiment of the present specification also provides a computer storage medium based on the above-mentioned completion method selection method, wherein the computer storage medium stores computer program instructions, which, when executed, implement the following: obtaining the membership of wellbore complexity data corresponding to different completion methods; using a wellbore complexity index prediction model to process the membership of wellbore complexity data corresponding to different completion methods to obtain wellbore complexity indices corresponding to different completion methods, wherein the wellbore complexity index prediction model is pre-established using the membership corresponding to wellbore complexity data samples; obtaining engineering data corresponding to different completion methods, and determining the risk levels corresponding to different completion methods based on the wellbore complexity index and the engineering data; and selecting a target completion method from different completion methods based on the risk levels corresponding to different completion methods.

[0201] In this embodiment, the storage medium includes, but is not limited to, random access memory (RAM), read-only memory (ROM), cache, hard disk drive (HDD), or memory card. The memory can be used to store computer program instructions. The network communication unit can be an interface configured in accordance with the standards specified by the communication protocol for network connection communication.

[0202] Although this specification provides the method operation steps as described in the embodiments or flow charts, more or fewer operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the device or client product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiments or the drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, product or device. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. Words such as first and second are used to represent names and do not represent any particular order.

[0203] Those skilled in the art will also appreciate that, in addition to implementing the controller in pure computer-readable program code, it is entirely possible to implement the same functionality by logically programming the method steps in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, and the like. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered structures within the hardware component. Alternatively, the devices for implementing various functions can be considered both software modules implementing the method and structures within the hardware component.

[0204] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, and the like that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0205] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that this specification can be implemented by means of software plus the necessary general hardware platform. Based on this understanding, the technical solution of this specification can essentially be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of this specification.

[0206] The various embodiments in this specification are described in a progressive manner. References to the common or similar parts of the various embodiments are sufficient. Each embodiment focuses on the differences from the other embodiments. This specification can be used in a variety of general-purpose or specialized computer system environments or configurations. For example, personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments that include any of the above systems or devices.

[0207] Although the present specification has been described with reference to the embodiments, persons skilled in the art will appreciate that there are many variations to the present specification without departing from the spirit of the present specification, and it is intended that the appended claims encompass such variations without departing from the spirit of the present specification.

Claims

1. A method for selecting a completion method, characterized in that: The method comprises: Obtain the membership of wellbore complexity data corresponding to different completion methods; Using a wellbore complexity index prediction model to process the membership of wellbore complexity data corresponding to different completion methods, to obtain wellbore complexity indices corresponding to the different completion methods, wherein the wellbore complexity index prediction model is pre-established using the membership corresponding to the wellbore complexity data samples; Acquire engineering data corresponding to different completion methods, and determine risk levels corresponding to the different completion methods based on the wellbore complexity index and the engineering data; Select the target completion method from different completion methods based on the risk levels corresponding to the different completion methods; Wherein, the wellbore complexity data includes: wellbore trajectory tortuosity index, wellbore temperature, wellbore pressure, leakage, wellbore diameter expansion rate, and gap expansion rate; accordingly, obtaining the membership of the wellbore complexity data corresponding to different completion methods includes: obtaining the membership of the wellbore trajectory tortuosity index, wellbore temperature, wellbore pressure, leakage, wellbore diameter expansion rate, and gap expansion rate corresponding to different completion methods; The wellbore complexity data also includes: well inclination and azimuth, the well inclination is used to determine the well inclination arc length, and the azimuth is used to determine the azimuth arc length; the wellbore trajectory tortuosity index is determined based on the well inclination arc length and the azimuth arc length; The obtaining of the wellbore trajectory tortuosity index membership corresponding to different completion methods includes: Obtaining an eye trajectory tortuosity, a first target eye trajectory tortuosity, and a second target eye trajectory tortuosity, wherein the first target eye trajectory tortuosity is greater than a preset eye trajectory tortuosity threshold, and the second target eye trajectory tortuosity is less than the preset eye trajectory tortuosity threshold; According to the wellbore trajectory tortuosity, the first target wellbore trajectory tortuosity and the second target wellbore trajectory tortuosity, the wellbore trajectory tortuosity index membership is determined according to the following formula: Among them, TI 3D is the wellbore trajectory tortuosity index membership, x ij is the tortuosity of the wellbore trajectory at well i and depth j, x j,max is the tortuosity of the first target eye trajectory, x j,min is the tortuosity of the eye trajectory of the second target; The obtaining of wellbore temperature membership corresponding to different completion methods includes: Establish the membership function corresponding to the wellbore temperature; According to the membership function corresponding to the wellbore temperature, the wellbore temperature membership corresponding to different completion methods is determined; The engineering data includes: string running friction, string buckling index, and triaxial safety factor. Accordingly, the risk level corresponding to different completion methods is determined based on the wellbore complexity index and the engineering data, including: Determining target string entry friction resistances corresponding to different completion methods in sequence from the string entry friction resistances corresponding to different completion methods, and determining target triaxial safety factors corresponding to different completion methods in sequence from the triaxial safety factors corresponding to different completion methods, wherein the target string entry friction group is greater than a preset string entry friction group threshold, and the target triaxial safety factor is less than a preset triaxial safety factor threshold; The risk scoring rules corresponding to the wellbore complexity index, string running friction resistance, string buckling index and triaxial safety factor are retrieved in sequence, and the risk values ​​corresponding to the wellbore complexity index, target string running friction resistance, string buckling index and target triaxial safety factor are determined in sequence; The risk values ​​corresponding to the wellbore complexity index, target string running friction, string buckling index, and target triaxial safety factor are summed to obtain the comprehensive risk values ​​corresponding to different completion methods. According to the comprehensive risk values ​​corresponding to different completion methods, the risk levels corresponding to different completion methods are determined.

2. The method according to claim 1, characterized in that The wellbore complexity index prediction model is established in advance using the membership corresponding to the wellbore complexity data samples, and includes: According to the following formula, a wellbore complexity index prediction model is established: Where Dco is the wellbore complexity index; w TI3D is the weight corresponding to the wellbore trajectory tortuosity index sample; TI 3D is the membership degree corresponding to the wellbore trajectory tortuosity index sample; w T is the weight corresponding to the wellbore temperature sample; T is the membership degree corresponding to the wellbore temperature sample; w p is the weight corresponding to the wellbore pressure sample; P is the membership degree corresponding to the wellbore pressure sample; is the weight corresponding to the missing samples; is the membership degree corresponding to the missing sample; w K is the weight corresponding to the well diameter expansion rate sample; K is the membership degree corresponding to the well diameter expansion rate sample; w DS is the weight corresponding to the gap expansion rate sample; K DS is the membership degree corresponding to the gap expansion rate sample.

3. The method according to claim 1, characterized in that The target completion method is selected from different completion methods according to the risk levels corresponding to the different completion methods, including: The risk levels corresponding to different completion methods are compared to determine a target risk level in the risk level and a target completion method corresponding to the target risk level, wherein the target risk level includes an acceptable risk.

4. A device for selecting a completion method, characterized in that: The device comprises: An acquisition module is used to obtain the membership of wellbore complexity data corresponding to different completion methods; A prediction module is used to process the membership of wellbore complexity data corresponding to different completion methods using a wellbore complexity index prediction model to obtain wellbore complexity indices corresponding to different completion methods, wherein the wellbore complexity index prediction model is pre-established using the membership corresponding to the wellbore complexity data samples; a determination module, configured to obtain engineering data corresponding to different completion methods, and determine risk levels corresponding to the different completion methods based on the wellbore complexity index and the engineering data; A selection module is used to select a target completion method from different completion methods based on the risk levels corresponding to the different completion methods; Wherein, the wellbore complexity data includes: wellbore trajectory tortuosity index, wellbore temperature, wellbore pressure, leakage, wellbore diameter expansion rate, and gap expansion rate; accordingly, obtaining the membership of the wellbore complexity data corresponding to different completion methods includes: obtaining the membership of the wellbore trajectory tortuosity index, wellbore temperature, wellbore pressure, leakage, wellbore diameter expansion rate, and gap expansion rate corresponding to different completion methods; The wellbore complexity data also includes: well inclination and azimuth, the well inclination is used to determine the well inclination arc length, and the azimuth is used to determine the azimuth arc length; the wellbore trajectory tortuosity index is determined based on the well inclination arc length and the azimuth arc length; The obtaining of the wellbore trajectory tortuosity index membership corresponding to different completion methods includes: Obtaining an eye trajectory tortuosity, a first target eye trajectory tortuosity, and a second target eye trajectory tortuosity, wherein the first target eye trajectory tortuosity is greater than a preset eye trajectory tortuosity threshold, and the second target eye trajectory tortuosity is less than the preset eye trajectory tortuosity threshold; According to the wellbore trajectory tortuosity, the first target wellbore trajectory tortuosity and the second target wellbore trajectory tortuosity, the wellbore trajectory tortuosity index membership is determined according to the following formula: Among them, TI 3D is the wellbore trajectory tortuosity index membership, x ij is the tortuosity of the wellbore trajectory at well i and depth j, x j,max is the tortuosity of the first target eye trajectory, x j,min is the tortuosity of the eye trajectory of the second target; The obtaining of wellbore temperature membership corresponding to different completion methods includes: Establish the membership function corresponding to the wellbore temperature; According to the membership function corresponding to the wellbore temperature, the wellbore temperature membership corresponding to different completion methods is determined; The engineering data includes: string running friction, string buckling index, and triaxial safety factor. Accordingly, the risk level corresponding to different completion methods is determined based on the wellbore complexity index and the engineering data, including: Determining target string entry friction resistances corresponding to different completion methods in sequence from the string entry friction resistances corresponding to different completion methods, and determining target triaxial safety factors corresponding to different completion methods in sequence from the triaxial safety factors corresponding to different completion methods, wherein the target string entry friction group is greater than a preset string entry friction group threshold, and the target triaxial safety factor is less than a preset triaxial safety factor threshold; The risk scoring rules corresponding to the wellbore complexity index, string running friction resistance, string buckling index and triaxial safety factor are retrieved in sequence, and the risk values ​​corresponding to the wellbore complexity index, target string running friction resistance, string buckling index and target triaxial safety factor are determined in sequence; The risk values ​​corresponding to the wellbore complexity index, target string running friction, string buckling index, and target triaxial safety factor are summed to obtain the comprehensive risk values ​​corresponding to different completion methods. According to the comprehensive risk values ​​corresponding to different completion methods, the risk levels corresponding to different completion methods are determined.

5. A device for selecting a completion method, characterized in that: The method comprises a processor and a memory for storing processor-executable instructions, wherein the processor implements the steps of the method according to any one of claims 1 to 3 when executing the instructions.

6. A computer-readable storage medium, characterized in that Computer instructions are stored thereon, and when the instructions are executed, the steps of the method according to any one of claims 1 to 3 are implemented.

Images