A method for optimizing and evaluating water control and oil stabilization measures for carbonate fractured-vug reservoirs

Abstract

This invention discloses an optimization and evaluation method for water control and oil stabilization measures in carbonate fractured-vuggy reservoirs. It can reasonably optimize water control and oil stabilization measures in oil wells, determine the optimal water control and oil stabilization measures, and predict the adjustment effects of different measures based on geological indicators, production indicators, and measure indicators. This method can effectively guide the adjustment of oil well measures and improve development results.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas reservoir energy development technology, specifically relating to an optimization and evaluation method for water control and oil stabilization measures in carbonate fractured-vuggy reservoirs. Background Technology

[0002] Carbonate fractured-vuggy reservoirs are highly heterogeneous, with well-developed caverns and fractures, and significant vertical and horizontal variations in the reservoir structure, leading to severe water production during development. Currently, the main water control and oil stabilization measures employed in carbonate fractured-vuggy reservoirs include: shut-in cone pressure, shut-in cone pressure, water injection cone pressure, water plugging, and acid backflow fracturing. Through years of laboratory experiments and field development practice, researchers have mastered the water control mechanisms and key technologies of different water control and oil stabilization measures. However, for oil wells in carbonate fractured-vuggy reservoirs, a more significant challenge lies in optimizing the best water control and oil stabilization measures at different production stages and predicting their development effects. With the increasing proportion of reserves and production in carbonate fractured-vuggy reservoirs, optimizing water control and oil stabilization measures for these wells to achieve long-term stable production has become an urgent problem to solve. However, research on water control and oil stabilization measures for oil wells in carbonate fractured-vuggy reservoirs is limited, and a comprehensive evaluation method for optimizing and predicting the effects of water control and oil stabilization measures has not yet been developed.

[0003] Therefore, it is of great significance to establish a set of optimization and evaluation methods for water control and oil stabilization measures in carbonate fractured-vuggy reservoirs that can guide the optimization and effect prediction of water control and oil stabilization measures. Summary of the Invention

[0004] In view of the problems existing in the prior art, the purpose of this invention is to provide a method for optimizing and evaluating water control and oil stabilization measures in carbonate fractured-vuggy reservoirs. This method can reasonably optimize water control and oil stabilization measures in oil wells, determine the optimal water control and oil stabilization measures, and predict the adjustment effects of different measures based on geological indicators, production indicators, and measure indicators, thereby effectively guiding the adjustment of oil well measures and improving development results.

[0005] Therefore, the first aspect of the present invention provides an optimization method for water control and oil stabilization measures in carbonate fractured-vuggy reservoirs, which includes the following steps:

[0006] (1) Determine the evaluation index for water control and oil stabilization measures in carbonate fractured-vuggy reservoirs: The evaluation index includes primary evaluation index and secondary evaluation index. The secondary evaluation index is a refinement of the primary evaluation index. The primary evaluation index includes the geological conditions of the oil well and the production status of the oil well.

[0007] (2) Each secondary evaluation index is independently divided into at least 3 factor levels according to its impact on the oil well development effect or the difficulty of preventing water cones. Each factor level corresponds to a grade, and each grade is assigned a value to obtain the grade value corresponding to each factor level of the secondary evaluation index.

[0008] (3) Determine the primary weight of each primary evaluation indicator and the secondary weight of each secondary evaluation indicator to obtain the comprehensive weight ω of the evaluation indicators. j The comprehensive weight ω j It is the product of the first-level weight and the second-level weight;

[0009] (4) Based on the factor levels of the secondary evaluation indicators applicable to different water control and oil stabilization measures, obtain the grade range and upper limit b corresponding to the factor levels applicable to the different water control and oil stabilization measures. kjmax and lower limit value b kjmin , where b kjmax b refers to the upper limit of the applicable value of the k-th water control and oil stabilization measure under index j. kjmin This refers to the lower limit of applicability of the k-th measure under index j;

[0010] (5) Determine the grade value a of each secondary evaluation index corresponding to the target well based on the geological conditions and production status of the target well. ij a ij This represents the grade value corresponding to the j-th evaluation index of the i-th target well;

[0011] (6) Compare the target well grade value a ij The upper limit value b mentioned in different water control and oil stabilization measures kjmax and the lower limit value b kjmin The relationship between the target well and the evaluation indexes under different water control and oil stabilization measures is used to determine the grade value (c) of the target well. ikj ), c ikj This represents the grade value of the j-th evaluation index for the i-th target well under the k-th water control and oil stabilization measures.

[0012] Where, when a ij kjmin At that time, c ikj =b kjmin -a ij

[0013] when a ij >b kjmax At that time, c ikj =a ij -b kjmax

[0014] When b kjmin i kjmax ​​​At that time, c ikj =0

[0015] (7) Determine the proximity of the target well to different water control and oil stabilization measures. The measure with the smallest proximity is the optimal measure. Among them, the proximity F ik The calculation formula is shown in Equation I below.

[0016] F ik This indicates the degree of similarity between the i-th target well and the k-th water control and oil stabilization measure.

[0017] According to some embodiments of the present invention, in step (2), each secondary evaluation index is divided into 5 factor levels.

[0018] According to some embodiments of the present invention, the secondary evaluation indicators of the oil well geological conditions include fractured-vuggy reservoir type a1, formation energy conditions a2, interlayer conditions a3, bottom water connection method a4, and production section opening location a5.

[0019] According to some embodiments of the present invention, the secondary evaluation indicators of the oil well production status include the development stage a6, water cut a7, water cut change rate a8, production volume a9, relative production volume a10, and monthly production decline value a11.

[0020] In this invention, "relative value of liquid production" refers to liquid production / critical production.

[0021] According to some embodiments of the present invention, in step (2), the three levels corresponding to the three factor levels are good, medium and poor, respectively, and preferably the values ​​assigned to good, medium and poor are 0.9, 0.5 and 0.1 respectively.

[0022] According to some embodiments of the present invention, in step (2), the five levels corresponding to the five factor levels are good, relatively good, medium, relatively poor, and poor, respectively, and preferably the values ​​assigned to good, relatively good, medium, relatively poor, and poor are 0.9, 0.7, 0.5, 0.3, and 0.1, respectively.

[0023] According to the present invention, the level of the reservoir type a1 is determined by the impact on the oil well development effect or the ease with which water cones do not occur. The better the level, the higher the value, which represents the more favorable it is for oil well development or the less likely water cones are to occur. According to some embodiments of the present invention, in step (2), the factor level of the fractured-vuggy reservoir type a1 includes: cavern type, small cavern type, fracture-cavitation type, small fracture type, and fracture type, which correspond to good, relatively good, medium, relatively poor, and poor levels, respectively.

[0024] According to some embodiments of the present invention, in step (2), the factor level of the formation energy condition a2 includes: weak, relatively weak, medium, relatively strong, and strong, which correspond to good, relatively good, medium, relatively poor, and poor levels, respectively.

[0025] According to the present invention, the formation energy condition a2 is characterized by the monthly decrease in formation pressure: weak (a2>0.5MPa), relatively weak (0.4MPa<a2≤0.5MPa), moderate (0.3MPa<a2≤0.4MPa), relatively strong (0.2MPa<a2≤0.3MPa), and strong (a2≤0.2MPa).

[0026] According to some embodiments of the present invention, in step (2), the factor levels of the partition condition a3 include: good, better, medium, poor, and bad, which correspond to the levels of good, better, medium, poor, and bad, respectively.

[0027] According to the present invention, the interlayer condition a3 is characterized by the interlayer thickness: good (a3>50m), weak (40<a3≤50m), medium (20<a3≤40m), strong (10<a3≤20m), and strong (a3≤10m).

[0028] According to some embodiments of the present invention, in step (2), the factor levels of the bottom water communication method a4 include: unconnected, partially sealed water; poorly connected, partially sealed water; poorly connected, connected small water body; connected, cracked, connected small water body; connected, cracked, connected large water body, which respectively correspond to good, relatively good, medium, relatively poor, and poor levels.

[0029] According to some embodiments of the present invention, in step (2), the factor level of the production section opening position a5 includes: upper, middle and bottom, which correspond to good, medium and poor levels respectively.

[0030] According to some embodiments of the present invention, in step (2), the factor level of the development stage a6 includes: early, middle and late, which correspond to good, medium and poor levels respectively.

[0031] According to some embodiments of the present invention, in step (2), the factor level of the moisture content a7 includes: a7≤10%, 10%<a7≤40%, 40%<a7≤60%, 60%<a7≤80%, a7>80%, which correspond to good, relatively good, medium, relatively poor, and poor grades, respectively.

[0032] According to some embodiments of the present invention, in step (2), the factor level of the water content change rate a8 includes: a7≤1%, 1%<a8≤2%, 2%<a8≤3%, 3%<a8≤5%, a8>5%, which correspond to good, relatively good, medium, relatively poor and poor grades, respectively.

[0033] According to some embodiments of the present invention, in step (2), the factor level of the liquid production a9 includes: a9 > 100 t / d, 60 t / d < a9 ≤ 100 t / d, 40 t / d < a9 ≤ 60 t / d, 20 t / d < a9 ≤ 40 t / d, a9 ≤ 20 t / d, which correspond to good, relatively good, medium, relatively poor, and poor grades, respectively.

[0034] According to some embodiments of the present invention, in step (2), the factor level of the relative value a10 of the liquid production includes: a10 < 0.5, 0.5 < a10 ≤ 0.7, 0.7 < a10 ≤ 0.9, 0.9 < a10 ≤ 1.1, and a10 > 1.1, which correspond to good, relatively good, medium, relatively poor, and poor grades, respectively.

[0035] According to some embodiments of the present invention, in step (2), the factor levels of the monthly output decline value a11 include: a11>12, 10<a11≤12, 8<a11≤10, 6<a11≤8, a11≤6, which correspond to good, relatively good, medium, relatively poor, and poor levels, respectively.

[0036] According to some embodiments of the present invention, in step (3), the first-level weights and the second-level weights are determined by the analytic hierarchy process.

[0037] According to some embodiments of the present invention, in step (3), the steps of the analytic hierarchy process include: firstly, comparing the importance of each factor in each level pairwise, and expressing the comparison results using a suitable scale, as shown in Table 1 below, in matrix form, thereby obtaining the judgment matrix U. Then, solving for the largest eigenvalue and the corresponding eigenvector of the judgment matrix to determine the weight of the relative importance of each indicator in each level.

[0038]

[0039] Table 1. Scale Table for Analytic Hierarchy Process (AHP)

[0040] Scale meaning 1 Both factors are equally important. 3 Between the two factors, one factor is slightly more important than the other. 5 When comparing two factors, one factor is significantly more important than the other. 7 Between two factors, one factor is more important than the other. 9 Between two factors, one is extremely more important than the other. 2、4、6、8 The median of the two adjacent judgments above reciprocal If the scale for comparing factor i and j is Uij, then the scale for comparing factor j and i is Uij = 1 / Uji

[0041] According to some embodiments of the present invention, the corresponding levels and weight coefficients of each factor of each evaluation index are shown in Table 2 below:

[0042] Table 2 Evaluation Criteria and Values ​​for Target Well Conditions

[0043]

[0044]

[0045] According to some embodiments of the present invention, the different water control and oil stabilization measures include unchanged working system, expanding nozzle to lift fluid, narrowing nozzle to press cone, shutting in well to press cone, water injection to press cone, upward acid pressure method, and water shut-off upward return method.

[0046] According to some embodiments of the present invention, the applicable grade ranges of the different water control and oil stabilization measures are shown in Table 3 below:

[0047] Table 3 Applicable Level Range of Water Control and Oil Stabilization Measures

[0048]

[0049]

[0050] According to some embodiments of the present invention, the upper and lower limits of different water control and oil stabilization measures are shown in Table 4 below:

[0051] Table 4 Range of Water Control and Oil Stabilization Measures Level Values

[0052]

[0053] According to some embodiments of the present invention, in step (5), based on the geological conditions and production status of the target well, corresponding values ​​from the indicator value set (0.1, 0.3, 0.5, 0.7, 0.9) are assigned to the five oil well geological indicators and six production status indicators of the target well, forming a target well indicator value vector A. Wherein, A... i =[a i1 ,a i2 ,…,a ij ], i represents the i-th target well, j represents the j-th evaluation index, j = 11.

[0054] The second aspect of this invention provides a method for evaluating water control and oil stabilization measures in carbonate fractured-vuggy reservoirs, which predicts the effectiveness of the optimal water control and oil stabilization measures obtained by the method described in the first aspect of this invention, and includes the following steps:

[0055] 1) Determine the input and output indicators of the BP neural network: The input indicators include the oil well water cone risk index, the production index when water control and oil stabilization measures are implemented, and the technical indicators of water control and oil stabilization measures. The output indicators are the effective period of water control and oil stabilization measures and the cumulative oil increase.

[0056] 2) Input index digitization: Digitize the input indexes of the oil well to obtain the input vector of the oil well;

[0057] 3) Optimization of BP neural network model structure: By adjusting the number of hidden layers and the number of nodes in each layer, the BP neural network model structure is optimized using a trial-and-error method to determine the number of hidden layers and the number of nodes in each layer;

[0058] 4) Training the BP neural network model: Divide the sample well into n parts, one part is the test sample and the remaining n-1 parts are the training samples. Normalize the input and output data, give the weights and thresholds of each layer, train the samples and adjust the corresponding parameters until the model meets the error requirements, and obtain the BP neural network model.

[0059] 5) Prediction of the effect of water control and oil stabilization measures in target wells: Based on the trained BP neural network model, input the corresponding input index of the target well to obtain the effective period and cumulative oil increase of the water control and oil stabilization measures, and predict the effect of the water control and oil stabilization adjustment measures.

[0060] According to some embodiments of the present invention, the oil well water cone risk indicators include reservoir type, formation energy conditions, interlayer conditions, bottom water connection method, and the location of the production section opening.

[0061] According to some embodiments of the present invention, the production indicators when the water control and oil stabilization measures are implemented include production time, water content, water content rise rate, oil production, liquid production; cumulative oil production, cumulative liquid production, and pressure drop level.

[0062] According to some embodiments of the present invention, the technical specifications of the water control and oil stabilization measures are as follows:

[0063] The nozzle extraction method includes the size of the nozzle and its variation range, and the pressure and its variation range;

[0064] The nozzle reduction and cone pressing method includes nozzle size, nozzle reduction amplitude, and nozzle reduction time;

[0065] The shut-in cone method includes nozzle size and shut-in time;

[0066] The water injection cone method includes injection volume, injection pressure, and shut-in time;

[0067] The back-flush acid pressing method includes the amount of acid pressing solution used, the acid pressing time, and changes in the production layer;

[0068] The water-blocking backflow method includes the dosage of plugging agent, water-blocking time, and changes in the production layer.

[0069] According to some embodiments of the present invention, in step 2), the characteristics of the oil well water cone risk index are digitized, which is performed by a method including the following steps:

[0070] a) Divide the different impacts of the oil well water cone risk index on the oil well development effect into at least five evaluation levels: good, relatively good, medium, relatively poor, and poor. Each evaluation level corresponds to an evaluation vector consisting of 5 numbers, the sum of which is 1. The evaluation vector is determined by combining the experience of those skilled in the art and expert scoring.

[0071] b) Determine the evaluation level corresponding to the water cone risk index of each oil well, and thus obtain the evaluation vector matrix corresponding to the oil well;

[0072] c) Assign different weights to each oil well water cone risk index to obtain the weight vector of the oil well water cone risk index. Multiply the evaluation vector matrix with the weight vector to finally obtain the numerical result of each oil well water cone risk index.

[0073] According to some embodiments of the present invention, in step a), the evaluation vector is as shown in Table 5 below:

[0074] Table 5. Evaluation Set of Indicator Levels

[0075]

[0076] According to some embodiments of the present invention, in step a), the evaluation level can be determined by the method of the first aspect of the present invention as described above. Specifically, in some embodiments, each water cone risk index is divided into 5 factor levels according to its impact on the oil well development effect or the difficulty of water cone occurrence. Each factor level corresponds to a level, namely good, relatively good, medium, relatively poor, and poor.

[0077] According to some embodiments of the present invention, the oil well water cone risk index includes reservoir type, formation energy conditions, interlayer conditions, bottom water connection method and production section opening location, and its levels and corresponding factor levels are as described in the first aspect of the present invention. Specifically, the factor levels of the fractured-vuggy reservoir type a1 include: cavern type, small cavern type, fracture-cavitation type, small fracture type, and fracture type, which correspond to good, relatively good, medium, relatively poor, and poor levels, respectively.

[0078] And / or the factor levels of the formation energy condition a2 include: weak, weak, moderate, strong, and strong, which correspond to good, relatively good, moderate, poor, and bad levels, respectively;

[0079] And / or the factor levels of the aforementioned partition condition a3 include: good, relatively good, medium, relatively poor, and poor, which correspond to the levels of good, relatively good, medium, relatively poor, and poor, respectively;

[0080] And / or the factor levels of the bottom water communication method a4 mentioned above include: unconnected, partially sealed water; poorly connected, partially sealed water; poorly connected, connected small water bodies; connected, fissured connected small water bodies; connected, fissured connected large water bodies, which correspond to good, relatively good, medium, relatively poor, and poor levels, respectively;

[0081] And / or the factor level of the production section opening position a5 includes: upper, middle and bottom, which correspond to good, medium and poor levels respectively.

[0082] According to some embodiments of the present invention, the method for assigning different weights to each oil well water cone risk index in step c) can employ the analytic hierarchy process (AHP) as described in the first aspect of the present invention. Specifically: firstly, the importance of each factor within each level is compared pairwise, and the comparison results are expressed using a suitable scale, as shown in Table 1 above, and written in matrix form to obtain the judgment matrix U. Then, the largest eigenvalue and the corresponding eigenvector of the judgment matrix are solved to determine the relative importance weights of each index at each level.

[0083] According to the present invention, steps b)-c) are specifically described as follows:

[0084] For example, the reservoir type, formation energy conditions, interlayer conditions, bottom water connection method, and production zone opening location of a certain oil well's water coning risk index are each assigned a level. Based on the evaluation vector table in Table 5, a 5×5 evaluation matrix can be obtained for the well's water coning risk index. The weights of each of the reservoir type, formation energy conditions, interlayer conditions, bottom water connection method, and production zone opening location constitute a 1×5 weight vector. Multiplying this 1×5 weight vector by the 5×5 evaluation matrix of the well's "oil well water coning risk index" yields a 1×5 vector. The five numbers in this vector collectively represent the comprehensive evaluation value of an oil well's "reservoir type, interlayer conditions, formation energy conditions, bottom water connection method, and production zone opening location," and become part of the BP neural network input indicators.

[0085] The third aspect of the present invention provides the application of the optimization method as described in the first aspect of the present invention and / or the evaluation method as described in the second aspect of the present invention in the development of carbonate fracture-vuggy reservoirs.

[0086] The present invention has the following beneficial effects:

[0087] The method of this invention can rationally optimize water control and oil stabilization measures in oil wells, determine the optimal water control and oil stabilization measures, and predict the adjustment effects of different measures based on geological indicators, production indicators, and measure indicators. It can effectively guide the adjustment of oil well measures and improve development results. Attached Figure Description

[0088] Figure 1 This is a schematic diagram of the optimized process for water control and oil stabilization measures in carbonate rock fractured-vuggy reservoirs according to the present invention.

[0089] Figure 2 This is a schematic diagram of the evaluation method for water control and oil stabilization measures in carbonate rock fractured-vuggy reservoirs according to the present invention. Detailed Implementation

[0090] To make the present invention easier to understand, the present invention will be described in detail below with reference to embodiments. It should be emphasized that the following description is merely exemplary and should not be regarded as a limitation on the scope of the present invention.

[0091] Example 1

[0092] For a certain carbonate fractured-vuggy reservoir, the optimization and evaluation method of water control and oil stabilization adjustment measures of the present invention was used to optimize the oil well measures and predict their effects.

[0093] The specific steps are as follows: (1) Determine the evaluation indicators for water control and oil stabilization measures in carbonate fractured-vuggy reservoirs: Among them, the primary evaluation indicators are the geological conditions of the oil well and the production status of the oil well. The geological conditions of the oil well are further subdivided into secondary evaluation indicators: fractured-vuggy reservoir type, formation energy conditions, interlayer conditions, bottom water connection method and opening location of production section. The production status of the oil well is further subdivided into secondary evaluation indicators: development stage, water cut, water cut change rate, production volume, relative value of production volume, and monthly production decline value.

[0094] (2) Divide each secondary evaluation indicator into factor levels, each factor level corresponds to a grade, and assign values ​​to each grade. The specific grade and weight coefficient of each factor level of each evaluation indicator are shown in Table 2 of the instruction manual.

[0095] (3) The first-level weights of each first-level evaluation indicator and the second-level weights of each second-level evaluation indicator are determined using the analytic hierarchy process (AHP) to obtain the comprehensive weight ω of the evaluation indicators. j The comprehensive weight ω j The product of the first-level weight and the second-level weight is shown in Table 6-8 below;

[0096] (4) Based on the factor levels of the secondary evaluation indicators that different water control and oil stabilization measures meet, the grade range, upper limit and lower limit values ​​corresponding to the different water control and oil stabilization measures are obtained, as shown in Table 3-4 of the invention.

[0097] (5) Select 6 target wells (Well-1 to Well-6), and determine the grade values ​​of each secondary evaluation index corresponding to the target wells based on their geological conditions and production status; compare the grade values ​​of the target wells with the upper and lower limits of different water control and oil stabilization measures to determine the grade values ​​of the target wells under different evaluation indexes of different water control and oil stabilization measures (c ikj ),

[0098] when a ij kjmin At that time, c ikj =b kjmin -a ij

[0099] when a ij >b kjmax At that time, c​ikj =a ij -b kjmax

[0100] When b kjmin i kjmax At that time, c ikj =0,

[0101] Among them, c ikj Let represent the grade value of the j-th evaluation index corresponding to the k-th water control and oil stabilization measures for the i-th target well. Then, the proximity degree is calculated according to the following formula I.

[0102] F ik The approximation is given by Table 9 below, which shows the degree of similarity between the i-th target well and the k-th water control and oil stabilization measure.

[0103] Table 6 Weights of Primary Indicators for Water Control and Oil Stabilization Measures

[0104] Primary indicators Oil well geological conditions Oil well production status Weight 0.40 0.60

[0105] Table 7 Weights of Secondary Indicators for Oil Well Geological Conditions

[0106]

[0107] Table 8 Weights of Secondary Indicators for Oil Well Production

[0108]

[0109] Table 9. Results of the closeness of water control and oil stabilization measures in oil wells

[0110]

[0111] As shown in Table 8, the optimal water control and oil stabilization measures corresponding to the minimum proximity of the six target wells (Well-1 to Well-6) are respectively: expanding the nozzle to lift fluid, narrowing the nozzle to press the cone, shutting in the well to press the cone, injecting water to press the cone, returning acid pressure, and shutting in water to return.

[0112] The optimal water control and oil stabilization measures determined for each of the six oil wells were used to predict their effectiveness using a BP neural network method. The specific steps are as follows:

[0113] ​​1) Determine the input and output indicators of the BP neural network: The input indicator package includes the oil well water coning risk indicator, the production indicators during the implementation of water control and oil stabilization measures, and the technical indicators of the water control and oil stabilization measures. The output indicators are the effective period of the water control and oil stabilization measures and the cumulative oil increase. Among them, the oil well water coning risk indicator includes reservoir type, formation energy conditions, interlayer conditions, bottom water connection method, and the opening location of the production section. The production indicators during the implementation of water control and oil stabilization measures include production time, water cut, water cut rise rate, oil production, and fluid production; cumulative oil production, cumulative fluid production, and pressure drop level. The technical indicators of the water control and oil stabilization measures are as follows:

[0114] The nozzle extraction method includes the size of the nozzle and its variation range, and the pressure and its variation range;

[0115] The nozzle reduction and cone pressing method includes nozzle size, nozzle reduction amplitude, and nozzle reduction time;

[0116] The shut-in cone method includes nozzle size and shut-in time;

[0117] The water injection cone method includes injection volume, injection pressure, and shut-in time;

[0118] The back-flush acid pressing method includes the amount of acid pressing solution used, the acid pressing time, and changes in the production layer;

[0119] The water-blocking backflow method includes the dosage of plugging agent, water-blocking time, and changes in the production layer.

[0120] 2) The different impacts of the above-mentioned water cone risk indicators on the oil well development effect are divided into five evaluation levels: good, relatively good, medium, relatively poor, and poor. Each evaluation level corresponds to an evaluation vector, as shown in Table 5 of this invention. Then, according to Table 2, the evaluation level corresponding to each water cone risk indicator of the oil well is determined, thereby obtaining the evaluation vector matrix corresponding to the oil well. Finally, the analytic hierarchy process is used to assign different weights to each water cone risk indicator of the oil well (as shown in Table 7) to obtain the weight vector of the water cone risk indicator of the oil well. The evaluation vector matrix is ​​multiplied by the weight vector to finally obtain the digital result of each water cone risk indicator of the oil well, which is used as part of the input indicators.

[0121] 3) BP Neural Network Model Structure Optimization: By adjusting the number of hidden layers and the number of nodes in each layer, and using a trial-and-error method, the BP neural network model structure was optimized to determine the number of hidden layers and the number of nodes in each layer. Figure 2 ;

[0122] 4) Training the BP neural network model: Divide the sample well into n parts, one part is the test sample and the remaining n-1 parts are the training samples. Normalize the input and output data, give the weights and thresholds of each layer, train the samples and adjust the corresponding parameters until the model meets the error requirements, and obtain the BP neural network model.

[0123] 5) Prediction of the effect of water control and oil stabilization measures in target wells: Based on the trained BP neural network model, the input index corresponding to the target well is input to obtain the effective period and cumulative oil increase of the water control and oil stabilization measures, and the effect of the water control and oil stabilization adjustment measures is predicted. The results are shown in Table 10 below.

[0124] Results: The predicted results were compared with the actual situation of wells (Well-1-Well-6). The predicted results were basically consistent with the actual situation. The method of this invention can accurately predict the effect of water control and oil stabilization measures in the target well.

[0125] Table 10 Prediction of the Effects of Water Control and Oil Stabilization Measures in Oil Wells

[0126]

[0127] It should be noted that the embodiments described above are only for explaining the present invention and do not constitute any limitation on the present invention. The present invention has been described with reference to typical embodiments, but it should be understood that the words used therein are descriptive and explanatory terms, not limiting terms. Modifications can be made to the present invention within the scope of the claims, and revisions can be made to the present invention without departing from the scope and spirit of the present invention. Although the present invention described herein relates to specific methods, materials, and embodiments, it does not mean that the present invention is limited to the specific examples disclosed herein; on the contrary, the present invention can be extended to all other methods and applications with the same function.

Claims

1. An optimization method for water control and oil stabilization measures in carbonate fractured-vuggy reservoirs, comprising the following steps: (1) Determine the evaluation indicators for water control and oil stabilization measures in carbonate fractured-vuggy reservoirs: The evaluation indicators include primary evaluation indicators and secondary evaluation indicators. The secondary evaluation indicators are the refinement of the primary evaluation indicators. The primary evaluation indicators include well geological conditions and well production status. The secondary evaluation indicators for well geological conditions include fractured-vuggy reservoir type a1, formation energy conditions a2, interlayer conditions a3, bottom water connection method a4, and the opening position of the production section a5. The secondary evaluation indicators for well production status include development stage a6, water cut a7, water cut change rate a8, production volume a9, relative production volume a10, and monthly production decline value a11. (2) Each secondary evaluation index is independently divided into 3 or 5 factor levels according to its impact on the oil well development effect or the difficulty of preventing water cone formation. Each factor level corresponds to a grade, and a value is assigned to each grade to obtain the grade value corresponding to each factor level of the secondary evaluation index. The 3 grades corresponding to the 3 factor levels are good, medium and poor, and the corresponding values ​​are 0.9, 0.5 and 0.1, respectively. The 5 grades corresponding to the 5 factor levels are good, relatively good, medium, relatively poor and poor, and the corresponding values ​​are 0.9, 0.7, 0.5, 0.3 and 0.1, respectively. (3) determining a first-level weight of each of the first-level evaluation indexes and a second-level weight of each of the second-level evaluation indexes to obtain a comprehensive weight ω of the evaluation indexes j , the comprehensive weight ω j being a product of the first-level weight and the second-level weight; (4) According to the factor level of the secondary evaluation index suitable for different water control and oil stabilization measures, the grade range and the upper limit value b corresponding to the factor level suitable for the different water control and oil stabilization measures are obtained kjmax and the lower limit value b kjmin , wherein b kjmax is the applicable upper limit value of the kth water control and oil stabilization measure under the j index, and b kjmin is the applicable lower limit value of the kth measure under the j index; (5) According to the geological conditions and production situation of the target well, the grade value a of each secondary evaluation index corresponding to the target well is determined ij , a ij represents the grade value corresponding to the jth evaluation index of the ith target well; (6) Compare the target well grade value a ij The upper limit value b mentioned in different water control and oil stabilization measures kjmax and the lower limit value b kjmin The relationship between the target well and the evaluation indexes under different water control and oil stabilization measures is used to determine the grade value c. ikj c ikj This represents the grade value of the j-th evaluation index for the i-th target well under the k-th water control and oil stabilization measures. wherein when a ij < b kjmin then c ikj = b kjmin - a ij When a ij > b kjmax , c ikj = a ij - b kjmax When b kjmin i < b kjmax At that time, c ikj =0​ (7) Determine the target well and different water control and oil stability measures close to the degree, the minimum of the degree of close to the measure is the most optimal measure, wherein, the close to the degree F ik The calculation formula is shown in the following formula I. (I), F ik represents the closeness of the ith target well to the kth water control and oil stabilization measure.

2. The optimization method according to claim 1, characterized in that, In step (2), the factor levels of the fissure reservoir type a1 include: cave type, small cave type, fissure-cave type, small fissure type, and fissure type, which correspond to good, relatively good, medium, relatively poor, and poor levels, respectively; And / or the factor levels of the formation energy condition a2 include: weak, weak, moderate, strong, and strong, which correspond to good, relatively good, moderate, poor, and bad levels, respectively; And / or the factor levels of the aforementioned partition condition a3 include: good, relatively good, medium, relatively poor, and poor, which correspond to the levels of good, relatively good, medium, relatively poor, and poor, respectively; And / or the factor levels of the bottom water communication method a4 mentioned above include: unconnected, partially sealed water; poorly connected, partially sealed water; poorly connected, connected small water bodies; connected, fissured connected small water bodies; connected, fissured connected large water bodies, which correspond to good, relatively good, medium, relatively poor, and poor levels, respectively; And / or the factor levels of the production section opening position a5 include: upper, middle and bottom, which correspond to good, medium and poor levels, respectively; And / or the factor levels of the development stage a6 include: early, middle and late, which correspond to good, medium and poor levels, respectively; And / or the factor levels of the moisture content a7 include: a7≤10%, 10%<a7≤40%, 40%<a7≤60%, 60%<a7≤80%, a7>80%, which correspond to good, relatively good, medium, relatively poor, and poor grades, respectively; And / or the factor levels of the water content change rate a8 include: a7≤1%, 1%<a8≤2%, 2%<a8≤3%, 3%<a8≤5%, a8>5%, which correspond to good, relatively good, medium, relatively poor, and poor grades, respectively; And / or the factor levels of the liquid production a9 include: a9 > 100 t / d, 60 t / d < a9 ≤ 100 t / d, 40 t / d < a9 ≤ 60 t / d, 20 t / d < a9 ≤ 40 t / d, a9 ≤ 20 t / d, which correspond to good, relatively good, medium, relatively poor, and poor grades, respectively; And / or the factor levels of the relative value of the production volume a10 include: a10 < 0.5, 0.5 < a10 ≤ 0.7, 0.7 < a10 ≤ 0.9, 0.9 < a10 ≤ 1.1, and a10 > 1.1, which correspond to good, relatively good, medium, relatively poor, and poor grades, respectively; And / or the factor levels of the monthly output decline value a11 include: a11>12, 10<a11≤12, 8<a11≤10, 6<a11≤8, a11≤6, which correspond to good, relatively good, medium, relatively poor, and poor grades, respectively.

3. The optimization method according to claim 1 or 2, characterized in that, In step (3), the first-level weights and the second-level weights are determined by the analytic hierarchy process.

4. The optimization method according to claim 1 or 2, characterized in that, The different water control and oil stabilization measures include: unchanged working system, expanding nozzle to lift fluid, narrowing nozzle to press cone, shutting in well to press cone, water injection to press cone, upward acid pressure, and water shut-off upward return.

5. A method for evaluating water control and oil stabilization measures in carbonate fractured-vuggy reservoirs, comprising predicting the effectiveness of the optimal water control and oil stabilization measures obtained by the method described in any one of claims 1-4, including the following steps: 1) Determine the input and output indicators of the BP neural network: The input indicators include the oil well water cone risk index, the production index when water control and oil stabilization measures are implemented, and the technical indicators of water control and oil stabilization measures. The output indicators are the effective period of water control and oil stabilization measures and the cumulative oil increase. 2) Input index digitization: Digitize the input indexes of the oil well to obtain the input vector of the oil well; 3) Optimization of BP neural network model structure: By adjusting the number of hidden layers and the number of nodes in each layer, the BP neural network model structure is optimized using a trial-and-error method to determine the number of hidden layers and the number of nodes in each layer; 4) Training the BP neural network model: Divide the sample well into n parts, one part is the test sample and the remaining n-1 parts are the training samples. Normalize the input and output data, give the weights and thresholds of each layer, train the samples and adjust the corresponding parameters until the model meets the error requirements, and obtain the BP neural network model. 5) Prediction of the effect of water control and oil stabilization measures in target wells: Based on the trained BP neural network model, input the corresponding input index of the target well to obtain the effective period and cumulative oil increase of the water control and oil stabilization measures, and predict the effect of the water control and oil stabilization adjustment measures.

6. The evaluation method according to claim 5, characterized in that, The oil well water cone risk indicators include reservoir type, formation energy conditions, interlayer conditions, bottom water connection method, and the location of the production section opening. And / or the production indicators during the implementation of the water control and oil stabilization measures include production time, water content, water content rise rate, oil production, liquid production; cumulative oil production, cumulative liquid production, and pressure drop level; And / or the technical specifications of the water control and oil stabilization measures are as follows: The nozzle extraction method includes the size of the nozzle and its variation range, and the pressure and its variation range. The nozzle reduction and cone pressing method includes nozzle size, nozzle reduction amplitude, and nozzle reduction time; The shut-in cone method includes nozzle size and shut-in time; The water injection cone method includes injection volume, injection pressure, and shut-in time; The back-flush acid pressing method includes the amount of acid pressing solution used, the acid pressing time, and changes in the production layer; The water-blocking backflow method includes the dosage of plugging agent, water-blocking time, and changes in the production layer.

7. The evaluation method according to claim 5 or 6, characterized in that, In step 2), the characteristics of the oil well water cone risk index are digitized, which is done through a method including the following steps: a) The different impacts of the oil well water cone risk index on the oil well development effect are divided into at least five evaluation levels: good, relatively good, medium, relatively poor, and poor. Each evaluation level corresponds to an evaluation vector consisting of 5 numbers, the sum of which is 1. The evaluation vector is determined by combining the experience of those skilled in the art and expert scoring. b) Determine the evaluation level corresponding to the water cone risk index of each oil well, thereby obtaining the evaluation vector matrix corresponding to that oil well; c) Assign different weights to each oil well water cone risk index to obtain the weight vector of the oil well water cone risk index. Multiply the evaluation vector matrix with the weight vector to finally obtain the digital result of each oil well water cone risk index.

8. The evaluation method according to claim 7, characterized in that, The evaluation vector is: {Good: 0.83, 0.16, 0.01, 0.00, 0.00;} Good: 0.16, 0.68, 0.14, 0.02, 0.00; Medium: 0.02, 0.27, 0.42, 0.27, 0.02; Poor: 0.00, 0.02, 0.14, 0.68, 0.16; Difference: 0.00, 0.00, 0.01, 0.16, 0.83}.

9. The application of an optimization method as described in any one of claims 1-4 and / or an evaluation method as described in any one of claims 5-8 in the development of carbonate fracture-vuggy reservoirs.

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