Automatic system and equipment for predicting oil reservoir water plugging position

Abstract

The present invention belongs to the field of data processing, and provides an automatic method, system and device for predicting the location of water plugging in oil reservoirs. The method can generate formation water breakthrough factors of different strata in each oil well by detecting the synchronous rising trend of water content and salinity of different strata along the depth direction in each oil well; detect the degree of permeability of each stratum in each oil well compared with other strata to generate a dominant water channel factor; sequence the formation water breakthrough factors and the dominant water channel factors of each stratum in each oil well to obtain a formation water flow factor sequence, and form a formation water flow factor matrix with the formation water flow factor sequence of each oil well; and use the formation water flow factor matrix to predict and output the location of water plugging in oil reservoirs. The present invention has a smaller amount of calculation and can quickly and in real time predict the location of water plugging in oil reservoirs. The water plugging strategy is optimized through the formation water flow factor matrix to improve the overall adaptability and success rate of the water plugging scheme.

Description

Technical Field

[0001] The invention belongs to the field of data processing, and in particular relates to an automatic system and equipment for predicting the water plugging position of an oil reservoir. Background Art

[0002] During the development of oil fields, as the mining period increases, oil reservoirs often face water invasion problems, that is, formation water enters the oil well along the dominant permeability channel, causing the bottom hole water content to gradually increase, affecting the crude oil recovery rate, and even causing the oil well to fail. Therefore, effectively predicting the water plugging position of the oil reservoir and implementing precise plugging at the water breakthrough point is one of the key technologies to improve the economic benefits of the oil field and extend the life of the oil well. Generally speaking, oil layers with high permeability usually have high water content, because high permeability oil layers are prone to water breakthrough into the oil well, increasing the water content. When predicting the future water content of oil wells, high permeability areas are more likely to have water invasion and need to be paid special attention. Oil layers with high permeability usually have low pressure, because the greater the permeability, the smaller the resistance to fluid flow and the faster the pressure loss. When predicting the water content of oil wells, it is possible to combine the pressure drop to assess whether serious water channeling has occurred. Oil layers with higher permeability often have lower mineralization, which may be related to the flow characteristics of formation water. Low permeability formations may be more closed, making the mineralization of formation water higher. Wells with high water content usually have low pressure, indicating that the pressure dissipates quickly after water breakthrough. When predicting the water content of an oil well, the degree of water invasion can be determined by the pressure change trend. Oil layers with high water content usually have high salinity, which may be due to the high salt content of the formation water from bottom water or lateral water breakthrough. In water content prediction, the water invasion situation can be judged in advance in combination with salinity monitoring. Where the bottom hole pressure is high, the salinity is usually high, which may be related to the increased solubility of deep, high-salinity formation water.

[0003] At present, the determination of the water plugging position of the reservoir mainly relies on geological analysis, well logging interpretation, historical data experience or numerical simulation, but these methods have obvious shortcomings, resulting in low water plugging prediction accuracy and difficulty in accurately implementing water plugging strategies. At present, the selection of water plugging points in the reservoir mainly relies on the experience of geological engineers, and is roughly estimated in combination with historical well data and formation permeability. Due to the complexity of the reservoir, such as fractured reservoirs, heterogeneous oil layers, etc., for example, the patent document with publication number CN113464087B provides a selective water plugging method for high water-content oil wells in bottom water reservoirs. Although directional water plugging can be achieved, this experience-based analysis method is difficult to guarantee high-precision prediction, especially in high-permeability reservoirs, where the water breakthrough path may not be consistent with traditional empirical judgments, resulting in water plugging failure.

[0004] Existing logging data analysis methods cannot accurately identify water breakthrough points. Traditional electrical logging methods such as resistivity logging, acoustic logging, and saturation logging are mainly used to identify the location of aquifers and oil layers, but they cannot dynamically track the changing trend of oil well water content with depth, resulting in a delay in identifying water breakthrough points. Well testing methods can infer water breakthrough by measuring changes in bottom hole pressure, but the test cycle is long, data updates lag, and it is difficult to predict the water plugging location in real time.

[0005] Traditional numerical simulation methods require a lot of calculations. Currently, one method for predicting reservoir water blocking is numerical simulation based on seepage theory, such as the finite element method FEM and the finite difference method FDM, which simulates reservoir water invasion by establishing a mathematical model of oil and water migration. For example, a method for optimizing the parameters of a profile control agent segment plug suitable for reservoir water blocking provided in the patent document with announcement number CN117057145A combines the target reservoir temperature, pressure, salinity and the target residual resistance coefficient that can be achieved under these conditions, but requires a large amount of historical logging data and reservoir physical parameters, making it difficult to obtain accurate input data. The large amount of calculations and the inability to predict in real time make it difficult to deal with sudden water invasion problems.

[0006] In the prior art, the injection of water plugging agents is generally based on the permeability distribution of the oil layer to simply plug the high permeability area. However, the dynamic changes of water content and salinity are not taken into account, resulting in the selection of water plugging points that may deviate from the actual breakthrough point. The existence of dominant water channels is not fully considered, which may result in the inability to effectively control water breakthrough after water plugging. Due to inaccurate selection of water plugging points, over-sealing or plugging failure may occur. The water plugging agent enters the low permeability oil layer, resulting in a decrease in oil well production and affecting economic benefits. The water invasion channel cannot be effectively identified, resulting in continued water breakthrough and ineffective water plugging.

[0007] At present, reservoir management still mainly relies on manual analysis and traditional reservoir numerical simulation, lacks data-driven automated methods, and does not conduct comprehensive analysis of well logging data, historical oil and water migration data, and real-time production data, resulting in inaccurate prediction of water plugging points. Water plugging strategies cannot be adjusted adaptively, and water plugging solutions cannot be dynamically optimized based on real-time monitoring data. Summary of the invention

[0008] The purpose of the present invention is to provide an automatic system and device for predicting the water plugging position in an oil reservoir, so as to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0009] In order to achieve the above-mentioned purpose, according to one aspect of the technical solution of the present invention, the present invention provides an automatic system for predicting the position of water plugging in an oil reservoir, the automatic system for predicting the position of water plugging in an oil reservoir comprising: a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the steps in the automatic method for predicting the position of water plugging in an oil reservoir when executing the computer program, the automatic system for predicting the position of water plugging in an oil reservoir can be run in computing devices such as desktop computers, laptop computers, PDAs and cloud data centers, the executable system may include, but not limited to, a processor, a memory and a server cluster, the processor executing the computer program may be run in the following system units:

[0010] A data acquisition unit, used to acquire the position coordinates of each oil well from a plurality of oil wells in the selected area to be tested, and to acquire the water content, permeability and mineralization data of different strata along the depth direction of each oil well;

[0011] a formation water breakthrough factor calculation unit, used to detect the synchronous rising trend of water content and salinity of different strata in each of the multiple oil wells along the depth direction, and calculate the formation water breakthrough factor of different strata in each of the oil wells based on the synchronous rising trend;

[0012] A dominant water channel factor calculation unit, used to detect the comparison degree between the permeability of each formation of each oil well in the plurality of oil wells and the permeability of other formations, and calculate the dominant water channel factor based on the comparison degree;

[0013] A formation water flow factor matrix construction unit is used to serialize the formation water breakthrough factors and the dominant water channel factors of each formation of the plurality of oil wells to generate a formation water flow factor sequence, and to combine the formation water flow factor sequences of the plurality of oil wells to form a formation water flow factor matrix;

[0014] The water blocking position prediction unit is used to predict based on the formation water flow factor matrix and output information data of the water blocking position of the oil reservoir.

[0015] Furthermore, in the data acquisition unit, each formation along the depth direction between each oil well is kept consistent, and the consistency includes the consistent alignment of the depths between sampling formations at the same level.

[0016] Furthermore, the formation water breakthrough factor calculation unit is specifically used for:

[0017] Calculating the derivative of the water content of each formation of each oil well in the plurality of oil wells with respect to the depth to obtain a water content depth derivative;

[0018] Calculating the derivative of the salinity of each formation of each oil well in the plurality of oil wells with respect to the depth to obtain a depth derivative of the salinity;

[0019] Normalizing the water content depth derivative and the mineralization depth derivative to obtain a water content depth normalized derivative and a mineralization depth normalized derivative, respectively;

[0020] The depth normalized derivative of water content is multiplied by the depth normalized derivative of salinity to obtain formation water breakthrough factors of different formations in each oil well.

[0021] Furthermore, the calculation procedure of the formation water breakthrough factor includes:

[0022] Calculating the average of the water cut depth derivatives of each formation of each oil well in the plurality of oil wells to obtain a water cut depth average;

[0023] Calculating the average of the salinity depth derivatives of each stratum of each oil well in the plurality of oil wells to obtain a salinity depth average;

[0024] The product of the mean water content depth and the mean mineralization depth is calculated as the formation water breakthrough factor.

[0025] Furthermore, the dominant water channel factor calculation unit is specifically used for:

[0026] Calculating an average permeability of formations at the same depth in the plurality of oil wells to obtain an average permeability at the same depth;

[0027] Calculating an average of the formation permeability at all depths of each of the multiple oil wells to obtain an average permeability of the same well;

[0028] Calculating the ratio of the permeability of each formation of the plurality of oil wells to the average permeability at the same depth to obtain the degree of permeability at the same depth;

[0029] Calculating the ratio of the permeability of each formation of the plurality of oil wells to the average permeability of the same well to obtain the permeability degree of the same well;

[0030] The dominant water channel factor is calculated by combining the permeability at the same depth and the permeability at the same well.

[0031] Furthermore, the calculation procedure of the dominant water channel factor includes:

[0032] The permeability at the same depth and the permeability at the same well are added, averaged or multiplied, and the calculation result is used as the dominant water channel factor.

[0033] Furthermore, the formation water flow factor matrix construction unit is specifically used for:

[0034] Calculating the product of the formation water breakthrough factor and the dominant water channel factor of each formation of each oil well in the plurality of oil wells to generate a formation water flow factor sequence;

[0035] Calculating the geometric center points of the position coordinates of the plurality of oil wells;

[0036] According to the Euclidean distance of the position coordinates of the multiple oil wells relative to the geometric center point, the formation water flow factor sequences of the multiple oil wells are sorted, and the formation water flow factor sequences of the oil wells are arranged in order of Euclidean distance from small to large to form the formation water flow factor matrix.

[0037] Furthermore, the formation water flow factor matrix construction unit is also used to include:

[0038] The formation water flow factor matrix is ​​visually outputted through a terminal device.

[0039] Furthermore, the water blocking position prediction unit is specifically used for:

[0040] Selecting the position corresponding to the maximum value in each row of the formation water flow factor matrix as the position of reservoir water plugging;

[0041] generating prediction results and marking them on the formation water flow factor matrix;

[0042] The prediction results are visualized and output through the terminal device.

[0043] Correspondingly, the present invention also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute each system unit of the automatic system for predicting the position of oil reservoir water plugging. The device may be composed of modules including but not limited to the following:

[0044] A data storage module, used for storing data related to reservoir water plugging prediction;

[0045] A calculation processing module, used for running various system units of the automatic system for predicting the oil reservoir water plugging position, including the data acquisition module, the formation water breakthrough factor calculation unit, the dominant water channel factor calculation unit, the formation water flow factor matrix construction unit and the water plugging position prediction unit;

[0046] Visualization output module, used to display prediction results.

[0047] A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the automatic method for predicting the location of water shutoff in an oil reservoir and the steps thereof.

[0048] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the computer program realizes the automatic method for predicting the water shutoff position of an oil reservoir and the methods of the various steps therein.

[0049] The beneficial effects of the present invention are as follows: the present invention provides an automatic system and equipment for predicting the location of water plugging in oil reservoirs, which generates formation water breakthrough factors of different strata in each oil well by detecting the synchronous rising trend of water content and salinity of different strata along the depth direction in each oil well; detects the degree of permeability of each stratum in each oil well compared with other strata, and generates a dominant water channel factor; the formation water breakthrough factors and the dominant water channel factors of each stratum in each oil well are serialized to obtain a formation water flow factor sequence, and the formation water flow factor sequence of each oil well is used to form a formation water flow factor matrix; the formation water flow factor matrix is ​​used to predict and output the location of water plugging in the oil reservoir. This data-driven water plugging location prediction system can accurately identify water breakthrough and dominant water channels. The use of matrices to represent data improves the calculation efficiency of the system and facilitates visual analysis. The use of electronic equipment to integrate calculation and visualization can improve the automation level of reservoir management equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The above and other features of the present invention will become more obvious by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present invention represent the same or similar elements. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work. In the accompanying drawings:

[0051] Figure 1 The figure shows a unit flow chart of an automatic system for predicting the location of water plugging in oil reservoirs;

[0052] Figure 2 Shown is a module structure diagram of an automatic electronic device for predicting the water blocking position in an oil reservoir. DETAILED DESCRIPTION

[0053] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0054] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0055] According to an automatic method for predicting the water plugging position of an oil reservoir of the present invention, an automatic method, system and device for predicting the water plugging position of an oil reservoir according to an implementation mode of the present invention are described below in conjunction with an embodiment of the present invention.

[0056] The present invention provides an automatic method for predicting the position of water plugging in an oil reservoir. The method is applied to an automatic system for predicting the position of water plugging in an oil reservoir, so that the system runs the following functional steps:

[0057] From multiple oil wells in the selected area to be tested, obtain the location coordinates of each oil well, and obtain data on water content, permeability and mineralization of different formations of each oil well along the depth direction;

[0058] Detect the synchronous rising trend of water content and salinity of different formations along the depth direction in each oil well, and generate formation water breakthrough factors of different formations in each oil well;

[0059] Detect the degree of permeability of each formation in each oil well compared with other formations and generate a dominant water channel factor;

[0060] The formation water breakthrough factor and the dominant water channel factor of each formation in each oil well are serialized to obtain a formation water flow factor sequence, and the formation water flow factor sequence of each oil well is used to form a formation water flow factor matrix;

[0061] The formation water flow factor matrix is ​​used to predict the location of reservoir water blocking.

[0062] Among them, there are some embodiments in which the formations along the depth direction between each oil well are consistent, and maintaining consistency includes that the depths between sampling formations at the same level can be completely consistent with the same level depths or roughly aligned with the same level depths.

[0063] In some embodiments, the oil field area is gridded and interpolated by Kriging to generate lateral permeability scores, and Petrel, CMG, etc. can be used.

[0064] In some embodiments, well testing seismic inversion, SCADA, etc. may be used to acquire lateral permeability data from logging tools and obtain horizontal permeability changes in the reservoir.

[0065] In some embodiments, the longitudinal profile data may be collected to monitor water content, pressure, and salinity, wherein PLT, PNL logging, and downhole pressure sensors may be used.

[0066] In some embodiments, data fusion and interpolation can be achieved by constructing a three-dimensional geological model, generally using Kriging, IDW or Python's GeoStatPy.

[0067] In some embodiments, the result visualization can be done using the 3D visualization ParaView.

[0068] Among them, the synchronous rising trend of water content and salinity of different formations along the depth direction in each oil well is detected, and the formation water breakthrough factor of different formations in each oil well is generated. The specific method is as follows:

[0069] The derivative of the water content value of each formation in each oil well with the value of the depth is calculated as the water content depth derivative, and the derivative of the salinity value of each formation with the value of the depth is calculated as the salinity depth derivative;

[0070] The water content depth derivatives of each formation in each oil well are normalized to obtain the water content depth normalized derivatives, and the salinity depth derivatives of each formation in the oil well are normalized to obtain the salinity depth normalized derivatives;

[0071] The depth normalized derivative of water content and the depth normalized derivative of salinity of each formation in the oil well are combined to obtain the formation water breakthrough factors of different formations in each oil well.

[0072] In some embodiments, the derivative D1 of the water content with each depth is calculated, the derivative D2 of the salinity with each depth is calculated, and the absolute value Dabs of each D1 minus D2 is calculated respectively;

[0073] However, there are some problems with this. Directly calculating the absolute value of D1-D2 will lose directional information. For example, if D1=0.8 and D2=0.2, then |0.8−0.2|=0.6. If D1=0.2 and D2=0.8, then |0.2−0.8|=0.6. However, these two situations may be different characteristics of water invasion in geology. One is a rapid increase in water content, and the other is a rapid increase in mineralization. In addition, because the numerical scale does not match, the units and magnitudes of water content and mineralization are different. The water content is between 0-100%, and the mineralization (mg / L) value may be as high as thousands to tens of thousands. If you do subtraction directly, the value of mineralization is much larger than the water content. Even if there is a clear correlation between the two, the D2 mineralization derivative may dominate the calculation, which will cause Dabs distortion. This will cause different types of formation water breakthrough situations to be classified as the same value, thus affecting the prediction accuracy. Therefore, calculate the normalized value D1m of the D1 sequence, and calculate the normalized value D2m of the D2 sequence, and calculate D1m*D2m=Dabs. The necessity of normalization and scaling is due to the different magnitudes of D1 and D2, and they need to be normalized to have the same scale to avoid one variable dominating the calculation. Normalization methods usually include minimum-maximum normalization and Z-Score normalization.

[0074] In some embodiments, the absolute difference between the normalized depth derivative of the water content of each formation in the oil well and the normalized depth derivative of the salinity is subtracted respectively, but preferably, the product of the normalized depth derivative of the water content of each formation and the normalized depth derivative of the salinity is calculated as the formation water breakthrough factor of different formations in each oil well.

[0075] The product method can retain directional information, and D1m*D2m can reflect the synchronous change relationship between the two. For example, D1m>0, D2m>0 means that both water content and mineralization increase, that is, a strong breakthrough; D1m>0, D2m<0 means that water content increases and mineralization decreases, that is, a weak breakthrough; D1m<0, D2m>0 means that water content decreases and mineralization increases, even if it is a weak breakthrough; D1m<0, D2m<0, both decrease, that is, no breakthrough. This method can maintain directional information, improve the accuracy of prediction, and enhance the ability to identify abnormal points.

[0076] The product method can identify extreme water invasion. If the water content or mineralization derivative of a certain formation is large and changes dramatically, its breakthrough factor will also be amplified, which helps to identify high-risk areas of water invasion in advance. In addition, this method avoids abnormal value interference in numerical value. After normalization, the value range of D1m and D2m is between [0,1] or [-1,1]. The value range of the product is limited, and the calculation will not be distorted due to a certain abnormal point.

[0077] In a specific implementation, the numerical derivative of discrete data can be calculated using NumPy's np.gradient() or np.diff() function, and a sequence of depth values, a sequence of water content values, and a sequence of mineralization values ​​are used as input variables of the function to calculate the derivative D1 of the water content with each depth, and the derivative D2 of the mineralization with each depth.

[0078] In some embodiments, the dynamic time warping (DTW) method can also be used to identify the time point when the water content and salinity change synchronously to optimize the calculation of the formation water breakthrough factor. The stability of the calculation can also be improved by using an optimization scheme based on time series analysis or machine learning methods, such as sliding window averaging, dynamic time warping, local regression analysis, etc.

[0079] Compared with the existing technology based only on water content or mineralization, the technical solution of the present invention comprehensively considers the synchronous change trend of the two, breaks through the single parameter limitation of the existing technology, and can more accurately reflect the water breakthrough situation. Directly calculating the absolute value of the derivative will lose directional information, while taking the product after normalization can retain the positive and negative relationship of the breakthrough factor. The directional sensitivity of the technology of the present invention avoids misjudgment. Through numerical calculation, it is possible to automatically identify strata where the water content and mineralization rise synchronously, providing a more targeted decision-making basis for water plugging.

[0080] Among them, the permeability of each formation in each oil well is detected compared with other formations to generate the dominant water channel factor. The specific method is as follows:

[0081] The permeability values ​​of the strata at the same depth in each oil well are compared, and the average permeability of the strata at the same depth in each oil well is taken as the average permeability of the strata at the same depth, and the average permeability of the strata at each depth in each oil well is taken as the average permeability of the oil well;

[0082] The permeability of each formation in each oil well compared with the average permeability of the formation at the same depth is the permeability degree at the same depth, and the permeability of each formation in each oil well compared with the average permeability of the formation at the same depth is the permeability degree at the same well. The dominant water channel factor is generated by combining the permeability degree at the same depth of each formation in each oil well and the permeability degree at the same well.

[0083] In some embodiments, the method for comparing the permeability of each formation with the average permeability of the formation at the same depth, and the permeability of each formation with the average permeability of the oil well at the same well can be to directly calculate the ratio, or to first perform index processing and then calculate the ratio.

[0084] In some embodiments, the processing options for the permeability at the same depth and the permeability at the same well of each formation in each oil well include adding the permeability at the same depth and the permeability at the same well, averaging the permeability at the same depth and the permeability at the same well, and multiplying the permeability at the same depth and the permeability at the same well. Preferably, the multiplication of the permeability at the same depth and the permeability at the same well is used to generate a dominant water channel factor.

[0085] In some embodiments, a kernel density estimation method may also be used to analyze the distribution of permeability at the same depth and permeability at the same well to enhance the robustness of abnormal permeability data.

[0086] Traditional methods only consider high permeability layers, but the present invention breaks through the assumption that high permeability is a water channel through the same depth / same well comparison, avoiding the problem of misjudgment due to local high permeability abnormal values. Combined with the same depth / same well comparison method, it can effectively distinguish the flow path between the oil layer and the aquifer, more accurately identify the dominant water channel, and provide a more reliable basis for water plugging. This can effectively exclude false permeability layers, improve the accuracy of water plugging location selection, and reduce the failure rate of water plugging.

[0087] Among them, the formation water breakthrough factor and the dominant water channel factor of each formation in each oil well are serialized to obtain the formation water flow factor sequence, which is specifically:

[0088] The formation water breakthrough factor of each formation in each oil well is multiplied by the dominant water channel factor, and the sequence composed of the products is the formation water flow factor sequence.

[0089] In some embodiments, the geometric center point of the location coordinates of each oil well is calculated, and the formation water flow factor sequences of each oil well are arranged in order from small to large according to the Euclidean distance values ​​of the location coordinates of each oil well from the geometric center point, thereby forming the formation water flow factor matrix.

[0090] The formation water flow factor matrix is ​​outputted visually through a terminal.

[0091] In some embodiments, the formation water flow factor matrix may be subjected to feature dimension reduction processing to improve the stability of water plugging position prediction.

[0092] The present invention uses a matrix to represent data, which greatly reduces the computational complexity, structures the data, improves computational efficiency, and is applicable to large-scale oil fields. In the prior art, the data of each oil well is usually analyzed in isolation. The present invention uses geometric center sorting to allow the water blocking conditions of adjacent wells to be considered as a whole, and uses geometric center optimization prediction to improve the prediction effect. The visual output in the form of a matrix can intuitively present the water blocking position, visualize the water blocking position, and improve practicality, so that oil field management can act more quickly.

[0093] The formation water flow factor matrix is ​​used to predict the location of reservoir water plugging, specifically:

[0094] The position with the largest value in each row of the formation water flow factor matrix is ​​selected as the position for predicting reservoir water plugging and outputted visually.

[0095] In the formation water flow factor matrix, each column can represent an oil well, and each row can represent the position of the bottom layer at the same depth in each oil well. The position of the predicted reservoir water plugging is marked and visualized output after marking.

[0096] An embodiment of the present invention provides an automatic system for predicting the water plugging position of an oil reservoir, such as Figure 1 As shown, the system runs in any computing device of a desktop computer, a laptop computer or a cloud data center, and the computing device includes a processor, a memory, and a computer program stored in the memory and running on the processor, and the processor executes the computer program to run in the following system units:

[0097] A data acquisition unit, used to acquire the position coordinates of each oil well from a plurality of oil wells in the selected area to be tested, and to acquire the water content, permeability and mineralization data of different strata along the depth direction of each oil well;

[0098] a formation water breakthrough factor calculation unit, used to detect the synchronous rising trend of water content and salinity of different strata in each of the multiple oil wells along the depth direction, and calculate the formation water breakthrough factor of different strata in each of the oil wells based on the synchronous rising trend;

[0099] A dominant water channel factor calculation unit, used to detect the comparison degree between the permeability of each formation of each oil well in the plurality of oil wells and the permeability of other formations, and calculate the dominant water channel factor based on the comparison degree;

[0100] A formation water flow factor matrix construction unit is used to serialize the formation water breakthrough factors and the dominant water channel factors of each formation of the plurality of oil wells to generate a formation water flow factor sequence, and to combine the formation water flow factor sequences of the plurality of oil wells to form a formation water flow factor matrix;

[0101] The water plugging position prediction unit is used to predict the water plugging position of the reservoir based on the formation water flow factor matrix.

[0102] Furthermore, in the data acquisition unit, each formation along the depth direction between each oil well is kept consistent, and the consistency includes the consistent alignment of the depths between sampling formations at the same level.

[0103] Furthermore, the formation water breakthrough factor calculation unit is specifically used for:

[0104] Calculating the derivative of the water content of each formation of each oil well in the plurality of oil wells with respect to the depth to obtain a water content depth derivative;

[0105] Calculating the derivative of the salinity of each formation of each oil well in the plurality of oil wells with respect to the depth to obtain a depth derivative of the salinity;

[0106] Normalizing the water content depth derivative and the mineralization depth derivative to obtain a water content depth normalized derivative and a mineralization depth normalized derivative, respectively;

[0107] The depth normalized derivative of water content is multiplied by the depth normalized derivative of salinity to obtain formation water breakthrough factors of different formations in each oil well.

[0108] Furthermore, the calculation procedure of the formation water breakthrough factor includes:

[0109] Calculating the average of the water cut depth derivatives of each formation of each oil well in the plurality of oil wells to obtain a water cut depth average;

[0110] Calculating the average of the salinity depth derivatives of each stratum of each oil well in the plurality of oil wells to obtain a salinity depth average;

[0111] The product of the mean water content depth and the mean mineralization depth is calculated as the formation water breakthrough factor.

[0112] Furthermore, the dominant water channel factor calculation unit is specifically used for:

[0113] Calculating an average permeability of formations at the same depth in the plurality of oil wells to obtain an average permeability at the same depth;

[0114] Calculating an average of the formation permeability at all depths of each of the multiple oil wells to obtain an average permeability of the same well;

[0115] Calculating the ratio of the permeability of each formation of the plurality of oil wells to the average permeability at the same depth to obtain the degree of permeability at the same depth;

[0116] Calculating the ratio of the permeability of each formation of the plurality of oil wells to the average permeability of the same well to obtain the permeability degree of the same well;

[0117] The dominant water channel factor is calculated by combining the permeability at the same depth and the permeability at the same well.

[0118] Furthermore, the calculation procedure of the dominant water channel factor includes:

[0119] The permeability at the same depth and the permeability at the same well are added, averaged or multiplied, and the calculation result is used as the dominant water channel factor.

[0120] Furthermore, the formation water flow factor matrix construction unit is specifically used for:

[0121] Calculating the product of the formation water breakthrough factor and the dominant water channel factor of each formation of each oil well in the plurality of oil wells to generate a formation water flow factor sequence;

[0122] Calculating the geometric center points of the position coordinates of the plurality of oil wells;

[0123] According to the Euclidean distance of the position coordinates of the multiple oil wells relative to the geometric center point, the formation water flow factor sequences of the multiple oil wells are sorted, and the formation water flow factor sequences of the oil wells are arranged in order of Euclidean distance from small to large to form the formation water flow factor matrix.

[0124] Furthermore, the formation water flow factor matrix construction unit is also used to include:

[0125] The formation water flow factor matrix is ​​visually outputted through a terminal device.

[0126] Furthermore, the water blocking position prediction unit is specifically used for:

[0127] Selecting the position corresponding to the maximum value in each row of the formation water flow factor matrix as the position of reservoir water plugging;

[0128] generating prediction results and marking them on the formation water flow factor matrix;

[0129] The prediction results are visualized and output through the terminal device.

[0130] In some embodiments, the automatic system for predicting the location of water plugging in oil reservoirs runs on any computing device such as a desktop computer, a laptop computer, a PDA or a cloud data center. The computing device includes: a processor, a memory and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps in the automatic method for predicting the location of water plugging in oil reservoirs are implemented. The executable system may include, but is not limited to, a processor, a memory, and a server cluster.

[0131] Among them, in order to better unify the numerical linear relationship and probabilistic connection between physical quantities of different units of measurement, different physical quantities can be dimensionally processed.

[0132] Among them, preferably, all undefined variables in the present invention, if not clearly defined, can be manually set thresholds.

[0133] The automatic system for predicting the location of water plugging in oil reservoirs can be run in computing devices such as desktop computers, laptop computers, PDAs, and cloud data centers. The automatic system for predicting the location of water plugging in oil reservoirs includes, but is not limited to, processors and memories. Those skilled in the art can understand that the example is only an example of an automatic method, system, and device for predicting the location of water plugging in oil reservoirs, and does not constitute a limitation on an automatic method, system, and device for predicting the location of water plugging in oil reservoirs. It may include more or fewer components than the example, or a combination of certain components, or different components. For example, the automatic system for predicting the location of water plugging in oil reservoirs may also include input and output devices, network access devices, buses, etc.

[0134] The present invention also provides an electronic device, a readable storage medium and a computer program product:

[0135] An electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the automatic method for predicting the location of water shutoff in an oil reservoir and the method of each step therein.

[0136] A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the automatic method for predicting the location of water shutoff in an oil reservoir and the steps thereof.

[0137] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the computer program realizes the automatic method for predicting the water shutoff position of an oil reservoir and the methods of the various steps therein.

[0138] The automatic system for predicting the position of water plugging in an oil reservoir is applied to the electronic device, and may include: at least one processor; and a memory connected to the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the system units of the automatic system for predicting the position of water plugging in an oil reservoir according to the present invention, such as Figure 2 As shown, the device may include the following modules:

[0139] A data storage module, used for storing data related to reservoir water plugging prediction;

[0140] A calculation processing module, used for running various system units of the automatic system for predicting the oil reservoir water plugging position, including the data acquisition module, the formation water breakthrough factor calculation unit, the dominant water channel factor calculation unit, the formation water flow factor matrix construction unit and the water plugging position prediction unit;

[0141] Visualization output module, used to display prediction results.

[0142] In some embodiments, the electronic device performs data processing through cloud computing or edge computing to improve the computing efficiency of the system.

[0143] Among them, the data storage module can be used to store historical reservoir data, real-time oil well measurement data and prediction result data required for reservoir water plugging prediction; the data storage module can also be used to obtain reservoir data through oilfield logging equipment, seismic exploration equipment, data acquisition and monitoring control systems including SCADA, or other remote monitoring systems, and transmit them through wireless networks, industrial Ethernet or satellite communications.

[0144] Among them, the visualization output module can be used to present the prediction results in the form of including but not limited to three-dimensional geological modeling, contour maps, thermal maps or oil well profiles, and supports display on remote terminals, handheld devices, and monitoring center workstations.

[0145] Wherein, electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0146] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0147] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0148] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0149] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0150] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0151] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0152] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete component gate circuits or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the automatic system for predicting the position of water plugging in oil reservoirs, and uses various interfaces and lines to connect the various sub-regions of the automatic system for predicting the position of water plugging in oil reservoirs.

[0153] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the automatic method, system and device for predicting the position of water plugging in oil reservoirs by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0154] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in the present disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document is not limited here.

[0155] The present invention provides an automatic system and device for predicting the position of water plugging in oil reservoirs. The system generates formation water breakthrough factors of different strata in each oil well by detecting the synchronous rising trend of water content and salinity of different strata along the depth direction in each oil well; detects the degree of permeability of each stratum in each oil well compared with other strata to generate a dominant water channel factor; serializes the formation water breakthrough factors and the dominant water channel factors of each stratum in each oil well to obtain a formation water flow factor sequence, and forms a formation water flow factor matrix with the formation water flow factor sequences of each oil well; and uses the formation water flow factor matrix to predict and output the position of water plugging in oil reservoirs.

[0156] This data-driven water plugging location prediction system can improve the accuracy of water plugging prediction based on multi-parameter analysis of water content, salinity, and permeability. The existing technology mainly relies on a single parameter, such as permeability or pressure for analysis, while the present invention uses multi-parameter fusion such as water content, salinity, and permeability to calculate the formation water breakthrough factor and the dominant water channel factor, which can more accurately identify the water breakthrough point of the oil well and improve the reliability of water plugging prediction.

[0157] The software system provided by the present invention constructs a formation water flow factor matrix, spatially processes the water plugging factors of multiple oil wells, automatically analyzes the trend of water breakthrough, and uses data algorithms to predict water plugging points, thereby reducing manual intervention and improving the intelligence of water plugging decisions.

[0158] Most existing methods analyze single wells in isolation. The present invention proposes geometric center sorting + water flow factor matrix analysis, which can optimize regional water plugging solutions as a whole and improve the global adaptability of water plugging strategies.

[0159] Correspondingly, the electronic device of the present invention integrates data acquisition, preprocessing, predictive calculation, visual output, and remote monitoring, and can be combined with cloud computing and edge computing to achieve real-time analysis of reservoir water plugging prediction and improve the level of intelligent reservoir management.

[0160] The present invention breaks through the traditional experience-based water plugging position judgment method and uses a data-driven approach to improve prediction accuracy. Compared with traditional well logging methods, it can automatically calculate the formation water breakthrough factor and the dominant water channel factor, and more accurately identify the water breakthrough point. Compared with traditional numerical simulation methods, the amount of calculation is smaller, and the water plugging position of the reservoir can be predicted quickly and in real time. The water plugging strategy is optimized through the formation water flow factor matrix to improve the overall adaptability and success rate of the water plugging scheme. The electronic equipment integrates remote monitoring, cloud computing and data processing functions to improve the intelligent level of oilfield water plugging management. The present invention can be widely used in the fields of oilfield water drive development, old oilfield water plugging and potential tapping, intelligent reservoir management, etc., which can effectively improve the oilfield recovery rate, reduce the economic losses caused by water invasion, and provide new technical support for the intelligent development of oilfields.

[0161] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An automatic system for predicting the location of water plugging in an oil reservoir, the system running on a desktop computer, a laptop computer or any computing device in a cloud data center, the computing device comprising a processor, a memory and a computer program stored in the memory and running on the processor, characterized in that: The processor executes the computer program to run in the following system units: A data acquisition unit, used to acquire the position coordinates of each oil well from a plurality of oil wells in the selected area to be tested, and to acquire the water content, permeability and mineralization data of different strata along the depth direction of each oil well; a formation water breakthrough factor calculation unit, used to detect the synchronous rising trend of water content and salinity of different strata in the depth direction of the multiple oil wells, and calculate the formation water breakthrough factor of different strata in each oil well based on the synchronous rising trend; A dominant water channel factor calculation unit, used to detect the comparison degree between the permeability of each formation of each oil well in the plurality of oil wells and the permeability of the formations of other oil wells, and calculate the dominant water channel factor based on the comparison degree; A formation water flow factor matrix construction unit is used to serialize the formation water breakthrough factors and the dominant water channel factors of each formation of the plurality of oil wells to generate a formation water flow factor sequence, and to combine the formation water flow factor sequences of the plurality of oil wells to form a formation water flow factor matrix; A water plugging position prediction unit, used for predicting and outputting information on the position of water plugging in the reservoir based on the formation water flow factor matrix; Wherein, the formation water breakthrough factor calculation unit is specifically used for: Calculating the derivative of the water content of each formation of each oil well in the plurality of oil wells with respect to the depth to obtain a water content depth derivative; Calculating the derivative of the salinity of each formation of each oil well in the plurality of oil wells with respect to the depth to obtain a depth derivative of the salinity; Normalizing the water content depth derivative and the mineralization depth derivative to obtain a water content depth normalized derivative and a mineralization depth normalized derivative, respectively; The water content depth normalized derivative and the salinity depth normalized derivative are multiplied to obtain formation water breakthrough factors of different formations in each oil well; Wherein, the dominant water channel factor calculation unit is specifically used for: Calculating an average permeability of formations at the same depth in the plurality of oil wells to obtain an average permeability at the same depth; Calculate the average permeability of the formation at all depths of each oil well to obtain the average permeability of the same well; Calculating the ratio of the permeability of each formation of the plurality of oil wells to the average permeability at the same depth to obtain the degree of permeability at the same depth; Calculate the ratio of the permeability of each formation of each oil well to the average permeability of the same well to obtain the permeability degree of the same well; Calculate the dominant water channel factor by combining the same-depth permeability and the same-well permeability; Wherein, the water blocking position prediction unit is specifically used for: Selecting the position corresponding to the maximum value in each row of the formation water flow factor matrix as the position of reservoir water plugging; generating prediction results and marking them on the formation water flow factor matrix; The prediction results are visualized and output through the terminal device.

2. The automatic system for predicting the oil reservoir water plugging position according to claim 1 is characterized in that: In the data acquisition unit, each layer in the depth direction between each oil well is kept consistent, and the consistency includes the consistent alignment of the depths between the sampling layers at the same level.

3. The automatic system for predicting the oil reservoir water plugging position according to claim 1 is characterized in that: The calculation procedure of the formation water breakthrough factor includes: Calculating the average of the water cut depth derivatives of each formation of each oil well in the plurality of oil wells to obtain a water cut depth average; Calculating the average of the salinity depth derivatives of each stratum of each oil well in the plurality of oil wells to obtain a salinity depth average; The product of the mean water content depth and the mean mineralization depth is calculated as the formation water breakthrough factor.

4. The automatic system for predicting the oil reservoir water plugging position according to claim 1 is characterized in that: The calculation procedure of the dominant water channel factor includes: The permeability at the same depth and the permeability at the same well are added, averaged or multiplied, and the calculation result is used as the dominant water channel factor.

5. The automatic system for predicting the oil reservoir water plugging position according to claim 1 is characterized in that: The formation water flow factor matrix construction unit is specifically used for: Calculating the product of the formation water breakthrough factor and the dominant water channel factor of each formation of each oil well in the plurality of oil wells to generate a formation water flow factor sequence; Calculating the geometric center points of the position coordinates of the plurality of oil wells; According to the Euclidean distance of the position coordinates of the multiple oil wells relative to the geometric center point, the formation water flow factor sequences of the multiple oil wells are sorted, and the formation water flow factor sequences of the oil wells are arranged in order of Euclidean distance from small to large to form the formation water flow factor matrix.

6. The automatic system for predicting the oil reservoir water plugging position according to claim 5 is characterized in that: The formation water flow factor matrix construction unit is also used to include: The formation water flow factor matrix is ​​visually outputted through a terminal device.

7. An electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute each system unit of the automatic system for predicting the position of water shutoff in oil reservoirs according to any one of claims 1 to 6, characterized in that the device is composed of the following modules: A data storage module, used for storing data related to reservoir water plugging prediction; A calculation processing module, used for running various system units of the automatic system for predicting the oil reservoir water plugging position, including the data acquisition unit, the formation water breakthrough factor calculation unit, the dominant water channel factor calculation unit, the formation water flow factor matrix construction unit and the water plugging position prediction unit; Visualization output module, used to display prediction results.

Images