CN117495085A

CN117495085A - Well site implementation risk quantitative evaluation method

Info

Publication number: CN117495085A
Application number: CN202311429026.3A
Authority: CN
Inventors: 刘洪涛; 曹荣亮; 姜福聪; 王智会; 刘卿; 于洪承; 戴琦雯; 齐婧; 闫志刚; 杨华宾; 李明妍; 秦雯; 石沛; 刘晨帆
Original assignee: Petrochina Co Ltd; Daqing Oilfield Co Ltd
Current assignee: Petrochina Co Ltd; Daqing Oilfield Co Ltd
Priority date: 2023-10-31
Filing date: 2023-10-31
Publication date: 2024-02-02

Abstract

The invention relates to a well site implementation risk quantitative evaluation method, which comprises the following steps of 1, selecting single sand bodies with different reservoir characteristics as sample points, and establishing an effective porosity model; step 2, establishing a regression model; step 3, quantitatively scoring the sample, and establishing a well site implementation risk quantitative evaluation system sample database and a quantitative evaluation regression model; step 4, judging whether the oil saturation can be judged; step 5, carrying out cluster analysis and classification on the samples; step 6, performing well site implementation simulation on a plurality of implementation well sites one by one; and 7, feeding back the implementation well position in any implementation process to the quantitative evaluation regression model according to the type and the implementation abnormality of the quantitative evaluation regression model. According to the invention, by establishing a risk evaluation model, the method is subdivided aiming at high, medium and low qualitative classification methods, and the method has good guiding significance on the drilling operation of complex geological condition blocks.

Description

Well site implementation risk quantitative evaluation method

Technical Field

The invention relates to the technical field of well site implementation risk evaluation methods in the well drilling operation process, in particular to a well site implementation risk quantitative evaluation method.

Background

The current well site risk evaluation method mainly comprises the steps of qualitatively analyzing geological features such as structural positions of designed well sites, reservoir development conditions, oil-water distribution and the like according to development geologists, dividing the geological features into high risk, medium risk and low risk according to analysis results, wherein evaluation results often depend on experience guidance of professionals, and no quantitative evaluation system is provided for guiding well site implementation. With the development range of oil fields, the geological conditions of new areas are more complex, the quantitative characterization technology of geological parameters is more important, and a method for quantitatively and comprehensively evaluating the implementation risk of block well positions is needed.

Chinese patent publication No.: CN104615862a discloses a method for determining well location of high water content oil field based on evolutionary algorithm, comprising the steps of: establishing an oil reservoir model of a preset oil area, and dividing the oil reservoir model into grids; acquiring oil reservoir data, and obtaining a residual oil potential enrichment region according to the oil reservoir data and history fitting; calculating well position constraint values representing the distribution and the water injection sweep volume of the residual oil in the residual oil potential enrichment area to obtain a well position advantage area; performing iterative computation on the fitness function of the oil well in the dominant region by using an evolution algorithm, evaluating the fitness function by using a kriging algorithm, adjusting the evolution algorithm according to the fitness function, and taking multidimensional space, a block gold effect and local weight into consideration by the kriging algorithm; and carrying out iterative computation according to the evolutionary algorithm to obtain the maximum fitness function value so as to determine the well position. It follows that the method of determining well location has the following problems: the risk abnormality analysis can not be performed on the implementation well positions in the implementation process, and the implementation process adjustment can be performed on the implementation well positions associated in the subsequent process.

Disclosure of Invention

Therefore, the invention provides a well site implementation risk quantitative evaluation method, which is used for solving the problems of risk abnormality analysis of an implementation well site in an implementation process and implementation process adjustment of an associated implementation well site in a subsequent process in the prior art.

In order to achieve the above object, the present invention provides a well site implementation risk quantitative evaluation method, comprising,

step S1, selecting single sand bodies with different reservoir characteristics in the well completion as sample points, and establishing an effective porosity model according to effective pores, acoustic time differences and lithologic densities of a middle layer core in the sample points;

s2, establishing a regression model according to the relation between the horizontal permeability and the effective porosity in the sample;

s3, comparing and sorting the development conditions of the single sand reservoir in the sample, quantitatively scoring, establishing a well position implementation risk quantitative evaluation system sample database according to quantitative scoring results, and simultaneously establishing a quantitative evaluation regression model;

step S4, in the quantitative evaluation of the oil-water layer, judging whether the oil saturation can be judged according to the analysis results of MDT, nuclear magnetism, oil test and production perforation layers;

s5, selecting a maximum value of a well position implementation risk quantitative evaluation index and a single sand body well position evaluation score in a sample, and performing cluster analysis and classification;

Step S6, selecting a plurality of implementation well positions for any implementation process based on the established quantitative evaluation regression model, and performing well position implementation simulation, wherein the implementation well positions comprise all sample types and are already subjected to category marking, and an implementation process model exists for the well position simulation process;

step S7, for any implementation well position in the implementation process, feeding back to the quantitative evaluation regression model according to the type and the implementation abnormality of the quantitative evaluation regression model, and carrying out quantitative evaluation regression model adjustment and implementation well position grade change;

further, in the step S1, the effective porosity model is:

Φ＝69.23+0.5951DT-24.21DEN+3.12×(RMN-RMG)；

wherein the porosity correlation coefficient r=0.86;

phi is the effective pore density;

DEN is lithologic density;

DT is the acoustic time difference;

RMN is a micropotential;

RMG is a micro-gradient;

in the step S2, the regression model is established as follows:

K＝e ^{0.5891Φ-9.6399} ；

wherein the horizontal permeability correlation coefficient r=0.81;

k is the horizontal permeability;

phi is the effective pore density;

e is a natural constant;

in the step S3, the quantitative evaluation regression model is:

wherein the high correlation coefficient R ² ＝0.86；

S _{Reservoir stratum} Quantitatively evaluating the score for the single sand reservoir;

h _{Effective and effective} Is an effective thickness;

phi is the effective pore density;

k is the horizontal permeability.

Further, in the step S4, if the difference of the response characteristics of the oil-water layer logging is large and the well zone containing the oil saturation cannot be determined, an oil-water plate recognition method is adopted;

the oil-water plate identification method comprises the following steps of,

step 4.1, analyzing oil test data and actual logging data, wherein the analysis contents comprise acoustic time difference, natural gamma, resistivity and density;

step 4.2, selecting an acoustic time difference curve according to the analysis result of the step 4.1, wherein the acoustic time difference curve is determined according to the natural potential and the resistivity of the fluid property, and an oil-water layer identification plate is established;

and 4.3, checking the precision of the established oil-water layer identification plate.

Further, in step S5, for any single well, the calculation process of the single sand body quantitative evaluation score is performed as follows:

P _{single sand body} ＝S _{Reservoir stratum} ×S _Oil-water ；

Wherein P is _{Single sand body} Quantitatively evaluating the score for the single sand body;

S _oil-water The oil-water quantitative evaluation score is the oil-water quantitative evaluation score of the single sand body.

And the well site implementation risk quantitative evaluation index is as follows:

wherein P is _{Total (S)} Implementing a risk quantitative evaluation index for the well site;

P _i performing risk ith single sand body quantitative evaluation score for the well site;

i is the risk evaluation well site single sand volume number, i=1, 2,..n.

Further, in step S6, in the implementation process model, a counting unit and a well location implementation discriminating unit are provided, where the counting unit counts the whole implementation process including all well location categories, and the well location implementation discriminating unit stores a plurality of sequentially implemented minimum values of the duty ratios, and the sequentially implemented minimum values of the duty ratios are determined according to the category of the implemented well location and the total implementation times;

for any implementation process, determining the minimum implementation well position number of each category one by one according to the minimum implementation proportion value and the total implementation well position number of the sequence of the implementation process;

in the implementation process model, the determined implementation well position is marked with the actual position and type.

Further, in step S7, for any implementation well position in the implementation process, calculating, in the well position implementation determining unit, an implementation score of the implementation well position for an actual well pressure, an actual oil flow rate and an actual oil duty ratio of the implementation well position in the implementation process, where the well position implementation determining unit is provided with an implementation score threshold and an implementation compliance range, the implementation compliance range is related to a class, an oil flow rate and an oil duty ratio of the implementation well position, calculating an implementation difference value according to the implementation score and the implementation score threshold, and comparing the implementation difference value with the implementation compliance range to determine whether the implementation process of the implementation well position is in an abnormal state;

If the implementation difference value is not in the implementation coincidence range, judging that the implementation process of the implementation well position is abnormal, extracting the type information and the position information of the implementation well position, and feeding back the type information and the position information to the quantitative evaluation regression model.

Further, in step S7, for any implementation process, a range threshold is set for the actual well pressure, the actual oil flow rate, and the actual oil duty ratio in the well location implementation determination unit, where the range threshold includes a well pressure threshold for the actual well pressure, a flow rate threshold for the actual oil flow rate, and an oil duty ratio threshold for the actual oil duty ratio;

in the well position implementation judging unit, a well pressure compensating value, an oil flow compensating value and an oil duty ratio compensating value are arranged, wherein the well pressure compensating value is determined according to the difference value between the actual well pressure and the well pressure threshold value, the oil flow compensating value is determined according to the difference value between the actual oil flow and the flow threshold value, and the oil duty ratio compensating value is determined according to the difference value between the actual oil duty ratio and the oil duty ratio threshold value.

Further, in step S7, in the well location implementation determining unit, for any implementation well location in the implementation process, a well pressure scoring range for the actual well pressure, an oil flow scoring range for the actual oil flow, and an oil duty scoring range for the actual oil duty ratio are stored, and for the actual well pressure, the actual oil flow, and the actual oil duty ratio, single item scores corresponding to each item are calculated one by one, and whether the implementation process of this implementation well location is in an abnormal state is determined;

If the single item score of any item is not in the corresponding score range, judging that the implementation process of the implementation well position is abnormal, calculating other single item scores, storing all item scores in the well position implementation judging unit, extracting the implementation well position category information and the position information, and feeding back the implementation well position category information and the position information to the quantitative evaluation regression model.

Further, the judging states of the well position implementation process have three levels, including a first-level state of judging a safety state, a second-level state of judging one of the single judging anomalies, and a third-level anomaly state of judging two or three of the single judging anomalies;

when judging that the implementation process of any implementation well site is in an abnormal judgment state in the well site implementation judgment unit, summarizing the position information and the implementation well site type information of all implementation well sites in the abnormal implementation state at the moment, generating an abnormal information data packet, transmitting the abnormal information data packet to an abnormal information summarizing unit in the well site implementation judgment unit, and inputting the abnormal information data packet into an abnormal summarizing analysis module by the abnormal information summarizing unit for summarizing analysis;

The summarizing analysis comprises determining an abnormal range for the position information of each abnormal well location, displaying the abnormal range in a map display model of the well location implementation judging unit, determining all implementation well locations in the abnormal range according to the position rules of all abnormal well locations in the well location implementation judging unit, performing key marking, extracting the positions of the implementation well locations, and re-evaluating the types of the implementation well locations.

Further, in the well location implementation judging unit, for any implementation well location in an abnormal range, calculating a change grade score according to the distances from the implementation well location in an abnormal state to all implementation well locations, and carrying out grade change according to the range in which the change grade score is located and the initial category of the implementation well location;

in the well position implementation judging unit, a first level change threshold value, a second level change threshold value, a third level change threshold value and a fourth level change threshold value are stored, wherein the first level change threshold value is minimum, the fourth level change threshold value is maximum, the second level change threshold value is larger than the first level change threshold value and smaller than the third level change threshold value, and the third level change threshold value is smaller than the fourth level change threshold value;

When the grade score of the change is smaller than or equal to the first grade change threshold value, the well position category of the implementation well position is maintained unchanged;

when the change grade score is greater than the first grade change threshold and less than or equal to the second grade change threshold, the well position category of the implemented well position is reduced by one grade;

when the grade change score is greater than the second grade change threshold and less than or equal to the third grade change threshold, the well position category of the implemented well position is reduced to a drilling slowing grade closest to the initial grade;

when the grade change score is larger than the third grade change threshold and smaller than or equal to the fourth grade change threshold, the well position class of the implemented well position is reduced to the lowest drilling slowing grade, and drilling prohibition early warning is carried out;

when the change grade score is greater than the fourth grade change threshold, the well site category of the implemented well site is changed to a drilling prohibition state.

Compared with the prior art, the method has the advantages that single well evaluation is taken as a final target, a single sand body is taken as an evaluation unit, geological parameters such as reservoir thickness, physical properties, oiliness and the like are quantitatively represented, a risk evaluation model is established, the high, medium and low qualitative classification methods are subdivided, the best is made, well position implementation risk analysis errors caused by geological features of different blocks are avoided, well drilling operation of complex geological condition blocks is well guided, meanwhile, quantitative evaluation is summarized after single well evaluation is established, well position implementation risks in the blocks are determined, actual implementation condition judgment is carried out in the well position drilling process based on the established risk evaluation model, the original risk evaluation model is adjusted, the method is more fit with actual well position implementation conditions, and accuracy of the risk evaluation model is improved.

Furthermore, the invention can directly calculate the formation porosity through the related value of the logging curve by establishing the effective porosity model, does not need to core the formation and analyze the indoor core, reduces the economic cost required to be input in the well position implementation, establishes the regression model, can directly calculate the horizontal permeability of the formation through the related value of the logging curve, does not need to core the formation and analyze the indoor core, reduces the investment, and the established quantitative evaluation regression model calculates the quantitative evaluation score of the reservoir of the single sand body, and can quantitatively evaluate the development condition of the reservoir, thereby providing a data source for the subsequent risk evaluation of the well position implementation.

Furthermore, after the built oil-water layer identification plate is inspected, the coincidence rate is 87.5%, so that the characteristic difference of the oil-water layer logging response is large, the well region with oil saturation cannot be judged, the oil-water layer identification plate can practically reflect the data of the well position, and the accuracy of single sand quantitative evaluation score judgment is improved.

Furthermore, the method subdivides the qualitative classification method aiming at high, medium and low by establishing the quantitative reservoir evaluation model, optimizes and guides the drilling operation sequence, guides the well position implementation, and improves the drilling operation process guiding capability of the complex geological condition block.

Further, before the implementation process of the well site is started, the risk prediction is implemented on each implementation well site, the category determination is carried out on each implementation well site, the risk quantitative evaluation indexes are implemented on the selected number, the implementation sequence and the well site of each category of well site in the implementation process, the minimum implementation well site determination is carried out on each type of well site, and all types in an original sample can be covered in any implementation process so as to facilitate the subsequent adjustment of the well site implementation risk quantitative evaluation model.

Further, the invention determines whether the implementing well position implementing process has a normal phenomenon by carrying out real-time overall process abnormality judgment on the implementing well position in the implementing process, when the implementing well position is abnormal, the quantitative evaluation regression model is fed back to determine whether the abnormal well position type is in a dangerous state when the initial well position implementing risk judgment is carried out, and the corresponding type and position value of the implementing well position are output for correspondingly adjusting the original quantitative evaluation regression model when the original well position implementing risk judgment has the abnormality.

Furthermore, the method and the device ensure the calculation result to be more accurate by setting the corresponding calculation compensation parameters for the parameters influencing the implementation operation in the well site implementation process, and ensure the effectiveness and the accuracy of the well site implementation judgment unit in the well site implementation process.

Further, the invention carries out any implementation process parameter on any implementation well position in the implementation process to carry out abnormality judgment, determines whether the implementation well position implementation process has a normal phenomenon, when the implementation well position is abnormal, feeds back a quantitative evaluation regression model to determine whether the abnormal well position type is in a dangerous state when the initial well position implementation risk judgment is carried out, outputs the corresponding type and position value of the implementation well position for correspondingly adjusting the original quantitative evaluation regression model.

Furthermore, when judging that any well position implementation state is abnormal, the invention gathers the positions of well positions in the abnormal implementation state at the same time, determines the abnormal range so as to refine the influence of each abnormal well position on the implementation state of each implementation well position in the same block, and carries out key marking and well position category assessment according to all well positions in the abnormal range so as to ensure that the well position in a dangerous position in the implementation process can be timely adjusted and prevent the occurrence of well position operation accidents.

Further, the invention performs the equalization and the change of any implementation well position within the abnormal range in the well position implementation judging unit, including reducing the type of the implementation well position and the well position needing to analyze the abnormal state, prohibiting the implementation well position, and subdividing the type of the implementation well position into the type of the well position which becomes slow drilling and the type of the well position which becomes the prohibited early warning level, thereby ensuring the well position operation safety and the orderly adjustment of the production capacity in the operation process.

Drawings

FIG. 1 is a flow chart of a method for quantitatively evaluating risk of a well site according to an embodiment;

FIG. 2 is a graph of a grape flower reservoir fluid identification criteria according to an example;

FIG. 3 is a single item scoring state logic decision diagram for an implementation process of implementing a well site in the embodiment of FIG. 3;

FIG. 4 is a logic diagram for determining whether the implementation process of the implementation well site is in an abnormal state according to the overall score of the embodiment.

Detailed Description

In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.

It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.

Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.

Referring to fig. 1, fig. 1 is a flowchart illustrating a method for performing risk quantitative evaluation on a well site according to an embodiment.

The invention provides a well site implementation risk quantitative evaluation method, which comprises the following steps of,

and S7, feeding back the type and the implementation abnormality of any implementation well position in the implementation process to the quantitative evaluation regression model according to the type and the implementation abnormality of the quantitative evaluation regression model, and adjusting the quantitative evaluation regression model and changing the implementation well position grade.

According to the invention, single well evaluation is taken as a final target, a single sand body is taken as an evaluation unit, geological parameters such as reservoir thickness, physical properties, oiliness and the like are quantitatively represented, a risk evaluation model is established, high, medium and low qualitative classification methods are subdivided, and 'optimal medium and optimal' is made, so that well position implementation risk analysis errors caused by geological features of different blocks are avoided, well drilling operation of complex geological condition blocks is well guided, meanwhile, quantitative evaluation is summarized after single well evaluation is established, well position implementation risk in the blocks is determined, actual implementation condition judgment is carried out in the well position drilling process based on the established risk evaluation model, and the original risk evaluation model is adjusted, so that the well position implementation model is more suitable for actual well position implementation conditions, and the accuracy of the risk evaluation model is improved.

The effective porosity reflects the reservoir property of the reservoir, the effective porosity, acoustic wave time difference, lithologic density and other logging parameters of 56 layers of core analysis of 5 coring wells in a block are counted as sample points, 46 sample points are selected, and an effective porosity model is established by adopting a multiple linear regression method.

Specifically, in this embodiment, in the step S1, the effective porosity model is:

Φ＝69.23+0.5951DT-24.21DEN+3.12×(RMN-RMG)；

wherein the porosity correlation coefficient r=0.86;

phi is the effective pore density;

DEN is lithologic density;

DT is the acoustic time difference;

RMN is a micropotential;

RMG is a micro-gradient;

the remaining 10 layers were interpreted using the above formula and verified that the effective porosity flat relative error of the grape-flowered reservoir monolayer log calculation and core analysis was 4.5%.

The permeability reflects the seepage capability of the reservoir, and the method for determining the permeability of the quasi-reservoir has important significance for reservoir evaluation and reservoir development. The permeability is divided into vertical permeability and horizontal permeability according to the relation between the direction of seepage and the surface of the stratum. The vertical permeability reflects the permeability of the formation in the longitudinal direction, while the horizontal permeability data obtained by core analysis reflects the ability of the fluid to flow along the permeability, and only the horizontal permeability parameters are considered due to data limitations. According to the invention, the formation porosity can be directly calculated through the correlation value of the logging curve by establishing the effective porosity model, so that the formation is not required to be cored and the indoor core analysis is not required, and the economic cost of investment required in well position implementation is reduced.

The present example applied 46 sample points, studied the horizontal permeability versus effective porosity, and built a regression model.

In the step S2, the regression model is established as follows:

K＝e ^{0.5891Φ-9.6399} ；

wherein r=0.81; horizontal permeability coefficient of correlation

K is the horizontal permeability;

phi is the effective pore density;

e is a natural constant.

The residual 10 sample points are interpreted and verified by applying the formula, and the relative error between the single-layer predicted horizontal permeability of the grape flower oil layer and the actual horizontal permeability of the core analysis is 6.7%.

By establishing the regression model, the horizontal permeability of the stratum can be directly calculated through the correlation value of the logging curve, and the stratum does not need to be cored and the indoor core analysis, so that the investment is reduced.

In the step S3, the quantitative evaluation regression model is:

wherein the high correlation coefficient R of the equation ² ＝0.86；

h _{effective and effective} Is an effective thickness;

phi is the effective pore density;

k is the horizontal permeability;

reservoir thickness reflects the extent of reservoir development, while the magnitude of the effective reservoir thickness reflects the abundance of reserves and how much reserves are from one side. Referring to table 1, table 1 is a table of the situation of drilling the partial well region of the dextran 47 block according to the embodiment, performing interpretation and division of the effective thickness of the well region, and predicting the implementation risk of the well position mainly through the reservoir inversion result.

TABLE 1

According to the method, the quantitative evaluation score of the reservoir of the single sand body is calculated through the established quantitative evaluation regression model, and the score can quantitatively evaluate the development condition of the reservoir, so that a data source is provided for subsequent well site implementation risk evaluation.

Specifically, in the embodiment, in the step S4, if the difference of the response characteristics of the oil-water layer logging is large and the well zone containing the oil saturation cannot be determined, an oil-water plate recognition method is adopted;

the oil-water plate identification method comprises the following steps of,

Taking a glucose 49 well area as an example, firstly, parameters such as acoustic time difference, natural gamma, resistivity (R250) and density are analyzed by analyzing oil test data and actual logging data, and an acoustic time difference curve is selected according to the analysis result of the step 4.1, wherein the acoustic time difference curve is determined according to natural potential and resistivity of fluid properties, an oil-water layer identification chart is established, and as shown in fig. 2, fig. 2 is a grape-flower oil layer fluid identification standard chart.

After the oil-water plate is established, the oil-water property of the wells which are not tested by oil and are not tested by MDT can be judged by the values of the logging curves R250 and AC of the wells and placed in the plate, the quantitative evaluation score of the oil layer is 1, the quantitative evaluation score of the oil-water layer on the same layer is 0.5, and the quantitative evaluation score of the water layer is 0.

The oil-water identification standard is as follows:

oil layer: a broken line region with r250=10.8Ω·m, ac= 277.5, and r250=8.6Ω·m, ac=288 as an inflection point;

same layer: a broken line region with r250=10Ω·m, ac=231, and r250=7.1Ω·m, ac=246.5 as an inflection point;

aqueous layer: r250=5.6Ω·m, and ac=205.2 is a region of inflection point.

And (3) checking the accuracy of the established oil-water layer identification plate, wherein the interpretation plate is checked by using 5 posterior wells and 8 oil test points, and the coincidence rate is 87.5%.

After the built oil-water layer identification plate is inspected, the coincidence rate is 87.5%, so that the response characteristic difference of the oil-water layer logging is large, the well region with oil saturation cannot be judged, the oil-water layer identification plate can practically reflect the well position data, and the accuracy of quantitative evaluation score judgment of a single sand body is improved.

Specifically, in this embodiment, in step S5, the calculation process of the single sand body quantitative evaluation score is performed for any single well, which is:

P _{Single sand body} ＝S _{Reservoir stratum} ×S _Oil-water ；

i is the risk evaluation well site single sand volume number, i=1, 2,..n.

Preferably 20 single wells, preferably P _{Total (S)} MaxP _{Single sand body} As a sample, cluster analysis is carried out, and the result of the cluster analysis is displayed: when the distance coefficient was taken to be 4.0, the samples were classified into four types as shown in table 2, and table 2 is a table of classification criteria for risk evaluation of well sites as described in examples.

TABLE 2

Classification	I	II	III	IV
					MaxP _{Single sand body}	7-10	4.5-7	2.3-4.5	≤2.5
P _{Total (S)}	≥13	8-13	3-8	＜3

In step S5, after classification is completed, according to the characteristics of the actual blocks, determining which types of the high-risk wells are classified into slow drilling, wherein the risks of the other types of wells are low, and the method can be implemented; taking the glucan 47 block as an example, the risk evaluation type is shown in III _Ⅲ And, below, the well site implementation risk is high, and the implementation is suspended. For the dextran 47 block, the distance coefficient in the cluster analysis is adopted to obtain a cluster spectrum diagram, when the distance coefficient is 4.0, the sample can be divided into four types, and when the sample is subdivided downwards, the types are independent and are not connected with each other, so the distance coefficient is 4.0.

The classification evaluation standard effectively avoids the phenomenon that the score of a thin interbed is higher than that of a single thick layer in well site evaluation, and ensures the objectivity of classification evaluation. The classification can be divided into I _Ⅰ 、Ⅰ _Ⅱ 、Ⅰ _Ⅲ 、Ⅰ _Ⅳ 、Ⅱ _Ⅰ Class 16, III _Ⅲ 、Ⅲ _Ⅳ 、Ⅳ _Ⅲ 、Ⅳ _Ⅳ Four types of wells have high implementation risk, slow drilling and the rest of classified wells can be implemented.

The western region of the dextran 47 block is internally structured with the western regions of low east and high west, and the reservoir mainly develops 4-5 layers and 6-7 layers of river sand, so that the reservoir has poor continuity and strong heterogeneity. The well region is controlled by double factors of structure and lithology, oil-water distribution is complex, risk quantitative evaluation is carried out on well positions in the well region, and by inversion section analysis in different directions, the development direction of sand bodies of the dextran 122-162 well is considered to be near north-south, the development direction of the sand bodies of the dextran 122-162 well is consistent with that of the dextran 124-162 well, PI2-5 layers and PI 10-11 layers are mainly developed, and single sand body drilling thickness is predicted according to inversion results.

The effective porosity of each small layer is predicted by referring to the dextran 124-to-162 well sonic time difference, density and microelectrode curve, and the horizontal permeability is predicted by using the formula, and at the same time, the well oil saturation is predicted according to the dextran 126-to-162 and dextran 124-to-158 well MDT results of the adjacent wells.

Finally, as shown in tables 3 and 4, table 3 is a quantitative evaluation table before drilling of the glucan 122-162 well described in examples, and table 4 is a quantitative evaluation table before drilling of the glucan 122-162 well described in examples. The PI2 layer score is highest in each sand of the dextrane 122-162 wells in Table 3, P _{Single sand body} 6.5, P _{Total (S)} 17.7, belonging to type III. In Table 4, after the actual drilling is completed, the grape 122-162 wells are basically consistent with the prediction, PI2-5, 7, 8 and 10 layers are developed, risk evaluation is carried out on the well positions after the actual drilling is completed according to the actual drilling completion effect, and the evaluation results are consistent with the prediction and are all of type III.

TABLE 3 Table 3

TABLE 4 Table 4

According to the method, a reservoir quantitative evaluation model is established, qualitative classification methods aiming at high, medium and low are subdivided, the drilling operation sequence is optimized and guided, well position implementation is guided, and the drilling operation process guiding capability of complex geological condition blocks is improved.

Specifically, in this embodiment, in step S6, in the implementation process model, a counting unit and a well location implementation determining unit are provided, where the counting unit counts the entire implementation process including all well location categories, and the well location implementation determining unit has a plurality of sequentially implemented minimum values of duty ratios, where the sequentially implemented minimum values of duty ratios are determined according to the category of the implemented well location and the total implementation times;

Before the implementation process of the well site starts, the risk prediction is implemented on each implementation well site, the class determination is carried out on each implementation well site, the risk quantitative evaluation indexes are implemented on the selected number, the implementation sequence and the well site of each class of well site in the implementation process, the minimum implementation well site determination is carried out on each type of well site, and all types in an original sample can be covered in any implementation process so as to facilitate the subsequent adjustment of the well site implementation risk quantitative evaluation model.

Specifically, in this embodiment, in step S7, for any implementation procedure, a range threshold is set for each of the actual well pressure, the actual oil flow rate, and the actual oil duty ratio in the well location implementation determination unit, where the range threshold includes a well pressure threshold for the actual well pressure, a flow rate threshold for the actual oil flow rate, and an oil duty ratio threshold for the actual oil duty ratio;

In this embodiment, for any one of the implementation processes, a range threshold is set for the actual well pressure Y01, the actual oil flow rate L1, and the actual oil duty ratio B1 stored in the well location implementation determination unit, and the range threshold includes a well pressure threshold Y02 for the actual well pressure Y01, a flow rate threshold L2 for the actual oil flow rate L1, and an oil duty ratio threshold B2 for the actual oil duty ratio B1.

The well position implementation judging unit is internally provided with a well pressure compensation value Y, an oil flow compensation value l and an oil duty ratio compensation value b, wherein the well pressure compensation value Y is determined according to the difference value between the actual well pressure Y01 and a well pressure threshold Y02, and the larger the difference value between the actual well pressure Y01 and the well pressure threshold Y02 is, the larger the well pressure compensation value Y is; the oil flow compensation value L is determined according to the difference value between the actual oil flow L1 and the flow threshold L2, and the larger the difference value between the actual oil flow L1 and the flow threshold L2 is, the larger the oil flow compensation value L is; the larger the oil duty compensation value B is according to the difference between the actual oil duty ratio B1 and the oil duty ratio threshold value B2, the larger the oil duty ratio compensation value B is.

According to the method and the device, the corresponding calculation compensation parameters are set for the parameters influencing the implementation operation in the well site implementation process, so that the calculation result is more accurate, and the effectiveness and accuracy of the well site implementation judgment unit in judgment of the well site implementation process are ensured.

Referring to FIG. 3, a single term scoring state logic decision diagram for an implementation of a well site in the embodiment of FIG. 3 is shown.

Specifically, in this embodiment, in step S7, in the well location implementation determining unit, for any implementation well location in the implementation process, there are a well pressure scoring range for the actual well pressure, an oil flow scoring range for the actual oil flow, and an oil duty scoring range for the actual oil duty, and for the actual well pressure, the actual oil flow, and the actual oil duty, single item scores corresponding to each item are calculated one by one, and whether the implementation process of this implementation well location is in an abnormal state is determined;

In the well location implementation discrimination unit, a well pressure scoring range, an oil flow scoring range and an oil ratio scoring range are stored.

Any of the well locations in the process of being implemented,

the well pressure score PY is: py= (Y02-Y01) x Y; oil flow score PL: pl= (L2-L1) ×l; the oil duty cycle score PB is: pb= (B2-B1) ×b.

Wherein PY is the well pressure score of any implemented well site in the implementation process;

PL is the oil flow score for any implemented well site in the implementation;

PB is the oil duty cycle score for any implemented well site in the implementation;

y01 is the actual well pressure of any implementation well position in the implementation process and MPa;

y02 is a well pressure threshold value, MPa;

l1 is the actual oil flow rate of any implementation well position in the implementation process, and the unit is m ³ /s；

L2 is the flow threshold in m ³ /s；

B1 is the actual oil ratio of any implementation well position in the implementation process;

b2 is the oil duty cycle threshold;

y is a well pressure compensation value, the well pressure compensation value Y is determined according to the difference value between the actual well pressure Y01 and the well pressure threshold Y02, and,the larger the difference value between the actual well pressure Y01 and the well pressure threshold Y02 is, the larger the well pressure compensation value Y is, and the unit is

L is an oil flow compensation value, the oil flow compensation value is determined according to the difference value between the actual oil flow L1 and the flow threshold L2, and the larger the difference value between the actual oil flow L1 and the flow threshold L2 is, the larger the oil flow compensation value L is, the unit is

B is an oil duty compensation value, and the larger the oil duty compensation value B is according to the difference value between the actual oil duty B1 and the oil duty threshold B2, the larger the oil duty compensation value B is.

If the well pressure scoring PY is in the well pressure scoring range, judging that the implementation process of the implementation well position is normal, and continuing to judge that the implementation process of the implementation well position is abnormal; if the well pressure score PY is not in the well pressure score range, judging that the implementation process of the implementation well position is abnormal, calculating an oil flow score PL and an oil duty ratio score PB, storing all item scores in the well position implementation judging unit, extracting the implementation well position category information and the position information, and feeding back the implementation well position category information and the position information to the quantitative evaluation regression model.

If the oil flow rate score PL is within the oil flow rate score range, judging that the implementation process of the implementation well position is normal, and continuing to judge that the implementation process of the implementation well position is abnormal; if the oil flow rate score PL is not in the oil flow rate score range, judging that the implementation process of the implementation well position is abnormal, extracting the type information and the position information of the implementation well position, and feeding back the type information and the position information of the implementation well position to the quantitative evaluation regression model.

If the oil duty ratio score PB is within the oil duty ratio score range, judging that the implementation process of the implementation well position is normal, and continuing to judge that the implementation process of the implementation well position is abnormal; if the oil ratio score PB is not in the oil ratio score range, judging that the implementation process of the implementation well position is abnormal, extracting the type information and the position information of the implementation well position, and feeding back the type information and the position information to the quantitative evaluation regression model.

The invention carries out any implementation process parameter on any implementation well position in the implementation process to carry out abnormality judgment, determines whether the implementation well position implementation process has a normal phenomenon, when the implementation well position is abnormal, feeds back a quantitative evaluation regression model to determine whether the abnormal well position type is in a dangerous state when the initial well position implementation risk judgment is carried out, outputs the corresponding type and position value of the implementation well position for correspondingly adjusting the original quantitative evaluation regression model.

Referring to fig. 4, fig. 4 is a logic determination diagram for determining whether the implementation process of the implementation well position is in an abnormal state according to the overall score of the embodiment.

Specifically, in this embodiment, in step S7, for any implementation well location in the implementation process, in the well location implementation determination unit, an implementation score of the implementation well location is calculated for an actual well pressure, an actual oil flow rate, and an actual oil duty ratio of the implementation well location in the implementation process, and an implementation score threshold and an implementation compliance range are set in the well location implementation determination unit, where the implementation compliance range is related to a type of the implementation well location, an oil flow rate, and an oil duty ratio, an implementation difference value is calculated according to the implementation score and the implementation score threshold, and whether the implementation process of the implementation well location is in an abnormal state is determined by comparing the implementation difference value with the implementation compliance range;

For any of the implemented well sites in the implementation process,

the implementation score is ps=py+pl+pb= (Y02-Y01) ×y+ (L2-L1) ×l+ (B2-B1) ×b;

wherein PS is an implement score for any implement well site in the process of implementing.

Difference Δps is implemented:

ΔPS＝PS-PS0；

wherein Δps is the difference in implementation of the implementation well position during implementation;

PS0 is an implementation score threshold, which is set in the well location implementation discrimination unit.

If the implementation difference value delta PS is not in the implementation compliance range, judging that the implementation process of the implementation well position is abnormal, extracting the type information and the position information of the implementation well position, and feeding back the type information and the position information to the quantitative evaluation regression model.

The invention determines whether the implementing well position implementing process has a normal phenomenon or not by carrying out real-time overall process abnormality judgment on the implementing well position in the implementing process, when the implementing well position is abnormal, feeds back a quantitative evaluation regression model to determine whether the abnormal well position type is in a dangerous state when the initial well position implementing risk judgment is carried out, outputs the corresponding type and position value of the implementing well position when the initial well position implementing risk judgment has the abnormality, and correspondingly adjusts the original quantitative evaluation regression model.

Specifically, in this embodiment, the determination states of the well location implementing process have three levels, including, a first level state of determining a safe state, a second level state of determining one of the single determination anomalies, and a third level anomaly state of determining two or three of the single determination anomalies;

In the well position implementation judging unit, the implementation well position J1 (X1, Y1) is judged to be in a three-level state with two judging anomalies, and the implementation well position J2 (X2, Y2) is judged to be in a two-level state with one judging anomaly.

An abnormality information collection unit in the well position implementation determination unit establishes an abnormality range in the map display model for the implementation well position J1 (X1, Y1) and the implementation well position J2 (X2, Y2).

The determination result of the abnormal range is that the implementation well position J1 (X1, Y1) is taken as a round dot, and R1 is taken as the radius to determine the abnormal influence range S1 of the well position J1; the anomaly influence range S2 of the well position J2 is determined by taking the implementation well position J2 (X2, Y2) as a round dot and taking R2 as a radius. The values of R1 and R2 are calculated from the positions of the implement well position J1 and the implement well position J2 in the implement well position implementation discrimination unit, wherein R1> R2 because the implement well position J1 is in a three-stage state and the implement well position J2 is in a two-stage state.

Because R1>R2, the distance between the implementation well position J1 (X1, Y1) and the implementation well position J2 (X2, Y2) is calculatedIf->Less than the sum of the radii of the two sphere of influence circles, i.e., (r1+r2). Then calculating the coincidence area S12 of the two abnormal influence ranges, and calculating the ratio of the coincidence area S12 to the sum of the area values (S1+S2) of the two influence ranges according to the coincidence area ∈ >Will->An associated value g determined as implementing well position J1 and implementing well position J2, and +.>

In the well position implementation determination unit, a distance range threshold G0 for implementing a well position distance for two anomalies is stored.

If it isIf the value is smaller than or equal to the distance range threshold G0, judging that the two abnormal influence ranges are mutually influenced; />And if the influence range is larger than the distance range threshold G0, judging that the two abnormal influence ranges have no influence.

When it is determined that the two abnormality influence ranges affect each other, but there is no overlap between the abnormality influence ranges of the two abnormality execution well positions, the abnormality execution determination unit determines that the abnormality influence ranges are not overlapped with each otherThe g value was calculated.

Based on the judgment that the two abnormal influence ranges are mutually influenced, an abnormal range is established, which is elliptical. The abnormal range is such that the midpoint of the distance between the well position J1 (X1, Y1) and the well position J2 (X2, Y2) is the center of the ellipse, (R2+2R1) is the major axis, and (1+g). Times.R1 is the minor axis.

Based on judging that two abnormal influence ranges are not influenced, establishing an abnormal range, namely two circles, wherein one of the two circles is a circle with an implementation well position J1 (X1, Y1) as a round point and R1 as a radius; secondly, the implementation well position J2 (X2, Y2) is taken as a round point, and R2 is taken as a circle with a radius.

In the abnormal range, the implementation well position J3 is stored, the key mark is carried out, and the implementation well position J3 is extracted as (X3, Y3) and the implementation well position category is re-assessed.

For the implementation well position in the abnormal state, only one circle with the implementation well position as the center of a circle is established, the radius of the influence range on the implementation well position in the implementation well position identification unit is calculated according to the well position, and the circle obtained by combining the implementation well position is the abnormal range.

For the implementation well positions with three or more than three abnormal states, the intersection area judgment and the abnormal implementation well position distance judgment are carried out on two pairs of the implementation well positions, whether the real-time well positions with the mutually affected abnormal influence ranges exist or not is determined, a plurality of abnormal ranges with associated values or abnormal ranges without associated values are determined, and the abnormal ranges are summarized into an actual abnormal range.

When judging that the implementation state of any well position is abnormal, the invention gathers the positions of the well positions in the abnormal implementation state at the same time, determines the abnormal range so as to refine the influence of each abnormal well position on the implementation state of each implementation well position in the same block, and carries out key marking and well position category assessment according to all well positions in the abnormal range so as to ensure that the well position in the dangerous position in the implementation process can be adjusted in time and prevent the occurrence of well position operation accidents.

Specifically, in this embodiment, in the well location implementation determination unit, for any implementation well location within an abnormal range, a change grade score is calculated according to the distances from any implementation well location in an abnormal state, and grade change is performed according to the range within which the change grade score is located and the initial type of the implementation well location;

If any of the well locations J3 (X3, Y3) is within the abnormal range, it is A1 m from the well location J1 (X1, Y1) and A2 m from the well location J2 (X2, Y2).

The change grade score PJ3 for the implemented well site J3 is:wherein PJ3 is a change grade score for implementing well site J3;

a1 is the distance from the implementation well position J3 to the implementation well position J1 (X1, Y1), m;

a2 is the distance from the implementation well position J3 to the implementation well position J2 (X2, Y2), m;

b1 is a distance compensation value of the implementation well position J3 from the implementation well position J1, the distance compensation value b1 of the implementation well position J3 from the implementation well position J1 is related to the distance A1 of the implementation well position J3 from the implementation well position J1, and the larger the distance A1 of the implementation well position J3 from the implementation well position J1 is, the smaller the distance compensation value b1 of the implementation well position J3 from the implementation well position J1 is, the unit is/>

b2 is a distance compensation value of the implementation well position J3 from the implementation well position J2, the distance compensation value b2 of the implementation well position J3 from the implementation well position J2 is related to the distance A2 of the implementation well position J3 from the implementation well position J2, and the larger the distance A2 of the implementation well position J3 from the implementation well position J2 is, the smaller the distance compensation value b2 of the implementation well position J3 from the implementation well position J2 is, the unit is

In the well position implementation determination unit, a first level change threshold BG1, a second level change threshold BG2, a third level change threshold BG3, and a fourth level change threshold BG4 are stored, wherein the first level change threshold BG1 is the smallest, the fourth level change threshold is the largest, the second level change threshold BG2 is larger than the first level change threshold BG1 and smaller than the third level change threshold BG3, and the third level change threshold BG3 is smaller than the fourth level change threshold BG4.

When the grade change score PJ3 of the implemented well site J3 is less than or equal to the first grade change threshold BG1, and the well site class of the implemented well site J3 is II _Ⅱ The well site type of the implemented well site J3 is still II _Ⅱ 。

When the change grade score PJ3 of the implemented well site J3 is larger than the first grade change threshold BG1 and smaller than or equal to the second grade change threshold BG2, and the well site class of the implemented well site J3 is II _Ⅱ Then change the well position type of the well position J3 to II _Ⅲ 。

When the change grade score PJ3 of the implemented well position J3 is larger than the second grade change threshold BG2 and smaller than or equal to the third grade change threshold BG3, the well position category of the implemented well position J3 is II _Ⅱ Then the well position type of the well position J3 is changed to III _Ⅲ 。

When the grade change score PJ3 of the implemented well site J3 is larger than the third grade change threshold BG3 and smaller than or equal to the fourth grade change threshold BG4, the well site class of the implemented well site J3 is III _Ⅲ Then change the well position category of the well position J3 to IV _Ⅳ And performing drilling prohibition early warning.

When the change grade score PJ3 of the implemented well site J3 is greater than the fourth grade change threshold BG4, the well site type of the implemented well site is changed to the drilling prohibition state.

The invention carries out the equalization and change to any implementation well position in the abnormal range in the well position implementation judging unit, including, reduce the type of implementation well position, the well position needing to analyze the abnormal state, inhibit the implementation well position, and subdivide the type of implementation well position into the type of well position becoming slow drilling and the type of well position becoming inhibit the early warning level, in the course of operation, ensure the well position operation safety and at the same time ensure the orderly adjustment of the production capacity.

Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.

The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A well site implementation risk quantitative evaluation method is characterized by comprising the following steps of,

2. The well site implementation risk quantitative evaluation method according to claim 1, wherein in the step S1, the effective porosity model is:

Φ＝69.23+0.5951DT-24.21DEN+3.12×(RMN-RMG)；

wherein the porosity correlation coefficient r=0.86;

phi is the effective pore density;

DEN is lithologic density;

DT is the acoustic time difference;

RMN is a micropotential;

RMG is a micro-gradient;

in the step S2, the regression model is established as follows:

K＝e ^{0.5891Φ-9.6399} ；

wherein the horizontal permeability correlation coefficient r=0.81;

k is the horizontal permeability;

e is a natural constant;

phi is the effective pore density;

in the step S3, the quantitative evaluation regression model is:

Wherein the high correlation coefficient R ² ＝0.86；

h _{effective and effective} Is an effective thickness;

phi is the effective pore density;

k is the horizontal permeability.

3. The method for quantitatively evaluating the risk of well placement implementation according to claim 2, wherein in the step S4, an oil-water pattern recognition method is adopted if the difference of response characteristics of oil-water layer logging is large and oil saturation cannot be determined for the well area;

the oil-water plate identification method comprises the following steps of,

4. The well site implementation risk quantitative evaluation method according to claim 3, wherein in step S5, the calculation process of the single sand body quantitative evaluation score is performed for any single well:

P _{single sand body} ＝S _{Reservoir stratum} ×S _Oil-water ；

i is the risk evaluation well site single sand volume number, i=1, 2,..n.

5. The well placement implementation risk quantitative evaluation method according to claim 4, wherein in step S6, in the implementation process model, a counting unit and a well placement implementation discrimination unit are provided, the counting unit counts the whole implementation process including all well placement categories, the well placement implementation discrimination unit stores a plurality of sequential implementation duty minimum values, and the sequential implementation duty minimum values are determined according to the categories of the implementation well placement and the total implementation times;

6. The method according to claim 5, wherein in step S7, for any implementation well position in the implementation process, in the well position implementation determination unit, an implementation score of the implementation well position is calculated for an actual well pressure, an actual oil flow rate and an actual oil duty ratio of the implementation well position in the implementation process, and in the well position implementation determination unit, an implementation score threshold value and an implementation compliance range are set, the implementation compliance range is related to a class of the implementation well position, an oil flow rate and an oil duty ratio, an implementation difference value is calculated according to the implementation score and the implementation score threshold value, and whether the implementation process of the implementation well position is in an abnormal state is determined by comparing the implementation difference value with the implementation compliance range;

7. The well placement implementation risk quantitative evaluation method according to claim 6, wherein in step S7, for any implementation process, a range threshold is set for each of the actual well pressure, the actual oil flow rate, and the actual oil duty ratio in the well placement implementation determination unit, and the range threshold includes a well pressure threshold for the actual well pressure, a flow rate threshold for the actual oil flow rate, and an oil duty ratio threshold for the actual oil duty ratio;

8. The well site implementation risk quantitative evaluation method according to claim 7, wherein in step S7, in the well site implementation discrimination unit, for any implementation well site in the implementation process, there is a well pressure scoring range for the actual well pressure, an oil flow scoring range for the actual oil flow, an oil duty scoring range for the actual oil duty ratio, and single item scores corresponding to each item are calculated one by one for the actual well pressure, the actual oil flow, and the actual oil duty ratio, and whether the implementation process of the implementation well site is in an abnormal state is determined;

9. The method for quantitatively evaluating the risk of well placement implementation according to claim 8, wherein the judging states of the well placement implementation process have three levels, including a first level state for judging a safe state, a second level state for judging one of the single judging anomalies, and a third level anomaly state for judging two or three of the single judging anomalies;

10. The well site implementation risk quantitative evaluation method according to claim 9, wherein in the well site implementation discrimination unit, for any implementation well site within an abnormal range, a change grade score is calculated according to the distances from any implementation well site in an abnormal state, and grade change is performed according to the range in which the change grade score is located and the initial class of the implementation well site;