Method for calculating water saturation of extra-high-water-content oil reservoir by fusing logging and sedimentary rhythm

Abstract

The invention relates to the field of oil production engineering, in particular to a method for calculating the water saturation of an extra-high-water-content oil reservoir through fusion of logging and sedimentary rhythm. The problems that the saturation of a water flooded layer in the extra-high-water-content later period is difficult to calculate and the saturation calculation precision is low are solved. The method comprises the following steps: S1, calculating physical property parameters of a reservoir by using a logging curve; s2, identifying the rhythm of the reservoir; determining the rhythm type of the reservoir; s3, according to the development and application requirements and the stratigraphic deposition rule principle, the reservoir is subjected to subdivision treatment on the basis of the logging peak-valley change characteristics; s4, according to the square wave curve and the rhythm type of the reservoir, digitally identifying the rhythm part of the oil layer; s5, generating an oil layer rhythm formation water mixed liquid resistivity dynamic change curve, and performing parameter optimization processing on the classical Archie model to obtain an extra-high water content oil reservoir saturation calculation model based on formation water resistivity parameter dynamic change; and calculating the water saturation of the oil layer by using the parameter-optimized saturation model.

Description

Technical Field

[0001] This invention relates to the field of well logging technology in oilfield development, and in particular to a method for calculating the water saturation of ultra-high water-cut reservoirs by integrating well logging and sedimentary rhythm. Background Technology

[0002] Water saturation is a crucial geological parameter in well logging interpretation during oil and gas field development. For water-drive development of reservoirs, this parameter reflects the degree of water flooding, water production rate, and remaining oil enrichment. Accurately calculating this parameter using well logging data provides a basis for the rational selection of perforation intervals during development. Analysis of publicly available literature indicates that the calculation of this parameter primarily relies on well logging curve data and is based on Archie's formula. The key parameter in the saturation calculation model is the determination of the formation water-mixture resistivity Rwz, which directly affects the accuracy of the saturation calculation. The following methods are commonly used to select the formation water resistivity value Rwz, a key parameter in the model: First, it is obtained using the original formation water salinity. However, this method ignores the effect of injected water on the desalination of the original formation water during oil reservoir development, often leading to an underestimation of water saturation and an overly optimistic interpretation of water flooding, resulting in a high water production rate after perforation. Second, it is calculated using the spontaneous potential (SP) theory formula from well logging curves. However, this method is affected by multiple factors, including drilling mud filtrate salinity, reservoir clay content, formation temperature, sandstone layer thickness, and formation pressure. Different factors often play a dominant role in different reservoirs. If the main influencing factors are not corrected, the accuracy of the calculation cannot be guaranteed. Third, formation fluid samples are taken using a formation testing device, and the formation water resistivity Rwz is obtained through laboratory analysis. Z The disadvantages of this method are that it requires a long well site time, a long analysis cycle, a large capital investment, and an extremely limited number of sample points. Summary of the Invention

[0003] This invention addresses the problems of difficulty in calculating the saturation of water-flooded reservoirs in the later stages of ultra-high water cut and the low accuracy of saturation calculation in the prior art. It provides a method that integrates well logging and sedimentary rhythm analysis, optimizes the dynamically changing formation water parameters in the saturation model, and then further calculates the water saturation of ultra-high water cut reservoirs. The absolute error of the saturation calculated by this method is 5.0%, which is 6.9% more accurate than the original method, meeting the needs of oilfield development.

[0004] The present invention solves its problem through the following technical solution: the method for calculating the water saturation of ultra-high water-cut reservoirs by integrating well logging and sedimentary rhythm includes the following steps:

[0005] S1: Calculate reservoir physical properties using well logging curves;

[0006] S2: Identify reservoir rhythms based on calculated physical property parameters; determine the reservoir rhythm type;

[0007] S3: Based on the development and application needs and the principles of stratigraphic sedimentation, and taking into account the peak and valley variation characteristics of well logging, the reservoir is further subdivided into layers.

[0008] S4: The logging curve is processed into a square wave to obtain a square wave curve; the rhythmic parts of the oil layer are digitally identified according to the square wave curve and the rhythmic type of the reservoir.

[0009] S5: Generate dynamic change curves of the resistivity of the formation water mixture in the oil reservoir, optimize the parameters of the classic Archie model to obtain a saturation calculation model for ultra-high water-cut oil reservoirs based on the dynamic change of formation water resistivity parameters, and calculate the water saturation of the oil reservoir using the saturation model with optimized parameters.

[0010] Furthermore, the reservoir physical properties in step S1 include porosity and clay content; the calculation formulas for porosity and clay content are as follows:

[0011] Vsh=(2(Gcur*Vsh')-1) / ((2GcuR)-1)

[0012] Where: Vsh'=(GR-GRmin) / (GRmax-GRmin)

[0013] PHIE=2.3*HAC-3.5*DEN-0.2*Vsh

[0014] In the formula: Vsh is the clay content; Gcur is the stratigraphic age coefficient, a constant;

[0015] Vsh' is the relative value of natural gamma; GRmin is the natural gamma value of pure sandstone formation;

[0016] GRmax is the natural gamma value of pure mudstone formations; PHIE is the effective porosity.

[0017] GR, Natural Gamma ray logging value; HAC, High Resolution Acoustic Logging value;

[0018] DEN, density logging curve value.

[0019] Furthermore, the method for identifying reservoir rhythm based on calculated physical property parameters in step S2 is as follows:

[0020] The rhythm type of a reservoir is determined based on the increasing or decreasing patterns of its physical property curves: within the reservoir, a reservoir is defined as having positive rhythm when the porosity parameter curve increases with depth; a reservoir is defined as having homogeneous rhythm when the porosity parameter curve does not show significant increases or decreases with depth; a reservoir is defined as having negative rhythm when the porosity parameter curve decreases with depth; and a reservoir is defined as having multiple overlapping trends in porosity parameter curves with depth.

[0021] Furthermore, in step S3, based on development and application needs and the principles of formation sedimentation, and taking the peak and valley variation characteristics of well logging as a basis, the reservoir is subdivided into layers. The specific method is as follows:

[0022] Using lateral deep resistivity as the stratification control curve, five variation characteristics of the peak and valley morphology of the logging curve are determined, including valley, peak, monotonically decreasing, monotonically increasing, and straight section of the curve. Simulating artificial stratification experience, firstly, a threshold value α for the difference between adjacent peaks and valleys of the stratification is given to control the degree of stratification refinement. Then, the average value of the maximum (Xmax) and minimum (Xmin) of the monotonically rising and falling intervals of the curve, i.e., the half-amplitude point, is used as the dividing point (X) to determine the interface point of the curve stratification, dividing a thick sedimentary rhythm layer into smaller sedimentary units.

[0023] Furthermore, the formula for determining the interface points of the curve layering is:

[0024] X = [Xmin + (N-1)*Xmax] / N

[0025] |Xmin-Xmax|>α

[0026] In the formula: X is the curve value of the interface point of the curve layer;

[0027] Xmin is the minimum value of the curve;

[0028] Xmax is the maximum value of the curve;

[0029] N represents the layered location at the root of the curve, and N=2 indicates taking a half-amplitude point;

[0030] α is the threshold value for adjusting the thickness of the layers.

[0031] Furthermore, the method for processing the logging curve into a square wave curve in step S4 is as follows: based on the interpretation of the layering using the simulated artificial curve half-amplitude point method, values ​​are selected according to the peak and valley characteristics, with the maximum value taken at the peak and the minimum value taken at the valley, and the average value taken at the straight section of the curve, thereby converting the smooth curve into a square wave and obtaining the square wave curve.

[0032] Furthermore, the method for digitally identifying the rhythmic regions of the oil layer based on the square wave curve and rhythm type in step S4 is as follows:

[0033] After obtaining the square wave curve and rhythm type of the reservoir, the thick oil layer was subdivided. The subdivided layers were digitally identified according to their rhythm location. In a complete rhythm layer, the top and bottom of the rhythm were interpolated using a 0-1 interval at a certain sampling frequency. The average value within the top and bottom boundaries of each square wave subdivided layer was taken as the digital identifier of the longitudinal rhythm location of the subdivided layer, resulting in the rhythm location curve YLBW.

[0034] Furthermore, the method for generating dynamic change curves of formation water mixed resistivity in different rhythmic parts of the oil layer in step S5 is as follows: assign dynamic change values ​​of formation water mixed resistivity according to the rhythmic part of the subdivided layer.

[0035] The expression for the dynamic change in the resistivity of the formation water mixture is as follows:

[0036] Rwz = Rwi + YLBW * (Rwh - Rwi)

[0037] Wherein, Rwz is the dynamic change value of the resistivity of the formation water mixture;

[0038] Rwi is the formation water resistivity in its original state, which is calculated from the spontaneous potential curve of a thick oil layer in a well that has not been flooded with water.

[0039] YLBW is the prosodic position curve;

[0040] Rwh is the resistivity of the high-flooded formation water, obtained from water sample analysis data.

[0041] Furthermore, the method for optimizing the parameters of the classic Archie model to obtain the ultra-high water-cut reservoir saturation model in step S5 is as follows:

[0042] By utilizing information from different rhythmic locations and the original formation water resistivity as well as the monitored values ​​of formation water resistivity in the high water-flooded layer, dynamic formation water resistivity values ​​are obtained. Simultaneously, considering the influence of mud, a saturation model for ultra-high water-cut oil reservoirs is derived.

[0043] Furthermore, the saturation model for the ultra-high water-cut reservoir is as follows:

[0044]

[0045] Where: SW represents water saturation;

[0046] Rwi represents the original formation water resistivity.

[0047] YLBW is the prosodic position;

[0048] Rwh is the resistivity of the high-flooded formation water, obtained from water sample analysis data.

[0049] RT represents the deep lateral resistivity curve value.

[0050] Compared with the above-mentioned background technology, the present invention has the following beneficial effects:

[0051] 1. The accuracy of water saturation calculation, a key parameter in oilfield development, has been improved by 6.9%, providing assistance for a deeper understanding of oil reservoirs. Water saturation values ​​obtained through laboratory experiments using subsurface cored samples were used as verification data. Compared with the previous method, the saturation calculated by the new method shows significantly better agreement with the laboratory analysis data, with an average absolute error of 5%. Based on the saturation calculation results of this invention, reliable fundamental data can be provided for a detailed description and understanding of oil reservoirs in ultra-high water-cut stages, including the distribution patterns of remaining oil in thick oil layers and the evaluation of waterflooding development effectiveness.

[0052] 2. This invention provides a reference for improvement measures and enhances the development effectiveness of oil reservoirs in the ultra-high water-cut stage. The results can be directly applied to the tapping of potential in ultra-high water-cut reservoirs. Based on the accurately calculated water saturation level, during the drilling process, the drilling can avoid areas with excessively high water saturation or target these areas for sealing, reducing ineffective measures by 15%, thus achieving cost reduction and efficiency improvement. The data provided by this invention generates significant economic benefits. Attached Figure Description

[0053] Figure 1 This is a flowchart of a method for calculating water saturation in ultra-high water-cut reservoirs by integrating well logging and sedimentary rhythm analysis according to the present invention.

[0054] Figure 2 The extreme values ​​are determined by the frequency histogram of the natural gamma curve in this embodiment of the invention;

[0055] Figure 3 This is a reservoir rhythm diagram for identifying reservoir porosity curve features according to an embodiment of the present invention;

[0056] Figure 4 Square wave processing diagram of well logging electrical curves in this embodiment of the invention;

[0057] Figure 5 Examples of the steps and results for calculating saturation using the invention method in well Apricot 2-10-3E7. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0059] This method involves a reservoir water saturation calculation method. Based on well logging curves, through analysis and comparison of core data, it was found that the formation water resistivity and reservoir rhythm type of high-porosity and high-permeability sandstone rhythm reservoirs in large fluvial facies are closely related to the vertical location of the reservoir. The water saturation of the rhythm layer in the dual-high stage is calculated by integrating well logging information with geological rhythm characteristics. This method is used to evaluate the quantitative amount and distribution characteristics of the remaining oil in the rhythm layer during the dual-high development stage, thus meeting the needs of oilfield development.

[0060] like Figure 1 As shown, a method for calculating water saturation in ultra-high water-cut reservoirs by fusing well logging and sedimentary rhythm analysis includes the following steps:

[0061] Step 1: Calculate the reservoir's porosity, clay content, and other physical properties using well logging curves.

[0062] The formulas for calculating porosity and clay content are as follows:

[0063] Vsh=(2(Gcur*Vsh')-1) / ((2GcuR)-1)

[0064] Vsh'=(GR-GRmin) / (GRmax-GRmin)

[0065] PHIE=2.3*HAC-3.5*DEN-0.2*Vsh

[0066] Where: Vsh is the clay content; Gcur is the stratigraphic age coefficient, a constant.

[0067] Vsh' is the relative value of natural gamma; GRmin is the natural gamma value of pure sandstone formations; GRmax is the relative value of pure sandstone formations.

[0068] Natural gamma value of mudstone strata;

[0069] PHIE represents effective porosity; GR represents the natural gamma ray logging curve value.

[0070] HAC, High-Resolution Acoustic Logging Curve; DEN, Density Logging Curve.

[0071] Step 2: Based on the calculated physical property parameters, the reservoir rhythm is first identified, and homogeneous rhythm, normal rhythm, and composite rhythm are identified.

[0072] The method for identifying reservoir rhythms based on calculated physical property parameters is as follows:

[0073] The rhythm type of a reservoir is determined based on the increasing or decreasing patterns of its physical property curves: within the reservoir, a reservoir is defined as having positive rhythm when the porosity parameter curve increases with depth; a reservoir is defined as having homogeneous rhythm when the porosity parameter curve does not show significant increases or decreases with depth; a reservoir is defined as having negative rhythm when the porosity parameter curve decreases with depth; and a reservoir is defined as having multiple overlapping trends in porosity parameter curves with depth.

[0074] S3: Based on the development and application needs and the principles of stratigraphic sedimentation, and taking into account the peak and valley variation characteristics of well logging, the reservoir is further subdivided into layers.

[0075] Using lateral deep resistivity as the stratification control curve, five variation characteristics of the peak and valley morphology of the logging curve are determined, including valley, peak, monotonically decreasing, monotonically increasing, and straight section of the curve. Simulating artificial stratification experience, firstly, a threshold value α for the difference between adjacent peaks and valleys of the stratification is given to control the degree of stratification refinement. Then, the average value of the maximum (Xmax) and minimum (Xmin) of the monotonically rising and falling intervals of the curve, i.e., the half-amplitude point, is used as the dividing point (X) to determine the interface point of the curve stratification, dividing a thick sedimentary rhythm layer into small sedimentary units.

[0076] The formula for determining the interface points of curve layering is as follows:

[0077] X = [Xmin + (N-1)*Xmax] / N

[0078] |Xmin-Xmax|>α

[0079] In the formula: X is the curve value of the interface point of the curve layer;

[0080] Xmin is the minimum value of the curve;

[0081] Xmax is the maximum value of the curve;

[0082] N represents the layered location at the root of the curve, and N=2 indicates taking a half-amplitude point;

[0083] α is the threshold value for adjusting the thickness of the layers.

[0084] S4: The logging curve is processed into a square wave to obtain a square wave curve; the rhythmic parts of the oil layer are digitally identified according to the square wave curve and the rhythmic type of the reservoir.

[0085] The method for obtaining a square wave curve by square wave conversion of the well logging curve is as follows:

[0086] Based on the interpretation of the layering using the simulated artificial curve half-amplitude point method, values ​​are selected according to the peak and valley characteristics, with the maximum value taken at the peak and the minimum value taken at the valley, and the average value taken at the straight section of the curve. In this way, the smooth curve is converted into a square wave, resulting in a square wave curve.

[0087] The method for digitally identifying the rhythmic regions of oil reservoirs based on square wave curves and rhythm types specifically includes:

[0088] After obtaining the square wave curve and rhythm type of the reservoir, the thick oil layer was subdivided. The subdivided layers were digitally identified according to their rhythm location. In a complete rhythm layer, the top and bottom of the rhythm were interpolated using a 0-1 interval at a certain sampling frequency. The average value within the top and bottom boundaries of each square wave subdivided layer was taken as the digital identifier of the longitudinal rhythm location of the subdivided layer, resulting in the rhythm location curve YLBW.

[0089] S5: Generate dynamic change curves of the resistivity of the formation water mixture in the oil reservoir, optimize the parameters of the classic Archie model to obtain a saturation calculation model for ultra-high water-cut oil reservoirs based on the dynamic change of formation water resistivity parameters, and calculate the water saturation of the oil reservoir using the saturation model with optimized parameters.

[0090] Based on steps 1-4 above, dynamic change values ​​of formation water mixed fluid resistivity are assigned according to the rhythmic location of the subdivided layers. The formation water resistivity in the original state is obtained from MDT test data of unflooded wells; the formation water resistivity of highly flooded layers is obtained from water sample analysis data; and finally, the dynamic change saturation model of formation water mixed fluid resistivity parameters in ultra-high water-cut oil reservoirs is used for calculation.

[0091] The expression for the dynamic change in the resistivity of the formation water mixture is as follows:

[0092] Rwz = Rwi + YLBW * (Rwh - Rwi)

[0093] Where: Rwz is the dynamic change value of the resistivity of the formation water mixture;

[0094] Rwi represents the formation water resistivity in its original state, calculated from the spontaneous potential curve of a thick oil layer in a well that has not been flooded; YLBW represents the rhythmic part curve; Rwh represents the formation water resistivity of a highly flooded layer, obtained from water sample analysis data.

[0095] The method for optimizing the parameters of the classic Archie model to obtain a saturation model for ultra-high water-cut reservoirs is as follows:

[0096] By utilizing information from different rhythmic locations, the original underlying formation water resistivity, and monitoring values ​​of formation water resistivity in the high water-flooded layer, dynamic formation water resistivity values ​​are obtained, while also considering the influence of mud quality.

[0097] The model for saturation in ultra-high water-cut reservoirs is as follows:

[0098]

[0099] Where: SW represents water saturation;

[0100] Rwi represents the original formation water resistivity.

[0101] YLBW is the prosodic position, which is obtained through step 4;

[0102] Rwh is the resistivity of the high-flooded formation water, obtained from water sample analysis data.

[0103] RT represents the deep lateral resistivity curve value.

[0104] Example 1

[0105] This invention utilizes extensive well analysis data from the test area. Through real core samples extracted from underground and laboratory analysis data, this data reveals the water-washing variation patterns of thick rhythmic reservoirs. This invention leverages these patterns to quantify the rhythmic regions and further assigns dynamic change values ​​related to the rhythmic regions to the key parameter in the water saturation model—formation water mixed resistivity. Simultaneously, it uses MDT testing and water composition analysis data to determine the initial value of the original formation water resistivity and the formation water mixed resistivity value under high water-flood conditions. This invention integrates well logging data, experimental analysis data, and geological information data, overcoming the shortcomings of other water saturation calculations with low accuracy, and is convenient, practical, and highly operable.

[0106] The block where the Xing 2-10-Jian 3E7 well is located has been under water-drive development since November 1966. At the time of drilling, it had been under development for 32 years, undergoing multiple adjustments to the formation and well network. The block's average water cut is over 90%, indicating it has entered the late-stage development phase of ultra-high water cut. Sampling data shows that the main oil-bearing layer of the PI group in this block is a large fluvial facies medium-high porosity sandstone reservoir with significant thickness and stable sedimentary distribution. The degree of water flooding within the layers is closely related to the reservoir's rhythm type and the vertical depth sequence of the rhythm location. The degree of water flooding gradually increases vertically in homogeneous and positive rhythms. This pattern is prevalent in the study area, manifested as the top of the rhythm layer being predominantly unflooded, with the formation water mixed fluid resistivity being essentially the same as in its original state, averaging 0.310 ohm-meters. Conversely, the bottom of the rhythm layer is predominantly water-flooded, with a formation water mixed fluid resistivity of 0.765 ohm-meters (see Table 1 for data). It is evident that the formation water mixture varies greatly in different locations, sometimes by more than two times, which significantly affects saturation calculations. Therefore, dynamic values ​​need to be assigned based on the rhythmic location.

[0107] The following explanation uses the Xing 2-10-Jian 3E7 well as an example to illustrate the processing method of this invention.

[0108] Step 1: Read the natural gamma-ray curve values ​​of the pure sandstone and pure mudstone sections, calculate the reservoir clay content, and simultaneously calculate the reservoir porosity as a function of depth using the porosity formula. See Figure 2 Based on the frequency distribution diagram of the natural gamma curve, the minimum value GRmin (65) is read when the cumulative frequency is 5%, and the maximum value GRmin (130) is read when the cumulative frequency is 95%. The physical property parameters are calculated using the following formula:

[0109] V cl =(2(GcuR*Vsh)-1) / ((2GcuR)-1)

[0110] Vsh=(GR-GRmin) / (GRmax-GRmin)

[0111] PHIE=0.0359*HAC-15.58*DEN-0.21*V cl

[0112] Among them, V cl Gcur represents the clay content, and is taken as a constant of 2.3.

[0113] Step 2: Identify the increasing and decreasing patterns of the calculated physical property parameter curves. When the porosity parameter curve increases with increasing depth, it is identified as a positive rhythmic reservoir and marked as number 1; when the porosity parameter curve does not show significant increasing or decreasing patterns with increasing depth, it is identified as a homogeneous rhythmic reservoir and marked as number 2; when the porosity parameter curve decreases with increasing depth, it is identified as an inverse rhythmic reservoir and marked as number 3; when the porosity parameter curve shows multiple overlapping trends with depth, it is identified as a composite rhythmic reservoir and marked as number 4. Figure 3 The image in the middle shows the identification diagrams for various rhythm types.

[0114] Step 3: Based on the actual needs of oilfield development, the reservoir thickness is subdivided according to the shape of the electrical logging curve. The purpose is to divide a thick layer with a sedimentary rhythm into smaller sedimentary units. Generally, this is based on the peak and valley variation characteristics of the well logging, using the half-amplitude point of the curve as the dividing point, converting the curve into a stepped curve, thus completing the subdivision of the rhythmic layer. Taking the resistivity curve as an example, the first step is to identify and judge the waveform, which is divided into "positive waveform" and "negative waveform". Figure 4 The image shown is a square wave diagram of the curve.

[0115] Step 4: Based on the square wave curve and rhythm type, the thick oil layer is subdivided into smaller layers and digitized according to the rhythm position. Within a complete rhythm layer, the spatial position is marked by the 0-1 interval from the top of the rhythm to the bottom of the rhythm.

[0116] Step 5: Based on steps 1-4 above, according to the rhythmic location of the subdivided layers, assign a dynamic change value to the formation water mixed resistivity Rwz, using the formula: Rwz = Rwi + YLBW * (Rwh - Rwi). The original formation water resistivity is taken as 0.310 ohms-meters, and the resistivity of the high water-flooded layer is taken as 0.765 ohms-meters. The dynamic change saturation model of formation water resistivity parameters in ultra-high water-cut oil reservoirs is used for calculation, with the following formula:

[0117]

[0118] Where: SW represents water saturation;

[0119] Rwi represents the original formation water resistivity.

[0120] YLBW is the prosodic position, which is obtained through step 4;

[0121] Rwh is the resistivity of the high-flooded formation water, obtained from water sample analysis data.

[0122] RT represents the deep lateral resistivity curve value.

[0123] Figure 5 This is a standard template image used in well logging interpretation during oilfield development. The image shows the processing results of the invention method for well Xing 2-10-Jian 3E7. From left to right, channels 1-6 represent well logging curve information, and channels 7-13 represent the results obtained during the method processing. Channel 7 represents the porosity parameter calculated using the natural gamma sonic density curve in step 1; channel 8, YLLX, is the rhythm type indicator curve identified in step 2, mainly of two types: homogeneous rhythm 1 and positive rhythm 2; channel 9, FBQZ, is the resistivity curve RLLD from step 3. The first curve is a sub-layer curve of the rhythmic layer subdivision of the baseline curve; the 10th curve, YLBW, is the result of the rhythmic part identification curve in step 3, with an interval of 0-1; the 11th curve is the dynamic change curve of the formation water mixed fluid resistivity, a key parameter for saturation calculation determined based on the rhythmic part in step 4; the 12th curve compares the final calculated continuous saturation curve with the discrete saturation data points from core sampling analysis. It can be seen that the saturation calculated by the new method matches the core analysis data better than the saturation curve calculated by the old method in the 13th curve. A detailed accuracy comparison is shown in Table 2. Table 2 compares the saturation calculation errors of the two methods. To verify the feasibility of the method provided by this invention, the average absolute error is used to measure the calculation accuracy. The average absolute error of the new method is 2.8%, while the average absolute error of the old method is 9.7%. The saturation calculation error is reduced by 6.9%, and the parameter accuracy is significantly improved. This indicates that the method established by this invention is reliable and feasible, and can be promoted and applied in the development of oilfields in the dual-high development stage.

[0124] Table 1. Resistivity values ​​of the mixture obtained from sampling tests of fluids within the reservoir rhythm.

[0125]

[0126] Table 2 Comparison of the accuracy of laboratory analytical saturation and saturation calculated by new and old methods.

[0127]

[0128]

[0129] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the implementation methods of the present invention, and should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of the present invention.

Claims

1. A method of calculating water saturation in ultra-high water cut reservoirs by merging well logs and sedimentary rhythms, characterized in that: The method comprises the following steps: S1: calculating the physical property parameters of the reservoir by using the well logging curve; S2: identifying the reservoir rhythm according to the calculated physical property parameters; determining the rhythm type of the reservoir; S3: according to the development application requirement and the principle of stratum sedimentation rule, taking the peak and valley change characteristics of the well logging as the basis, the reservoir is subdivided into layers; S4: the well logging curve is processed into a square wave curve; according to the square wave curve and the rhythm type of the reservoir, the rhythm part of the oil layer is digitally identified; S5: generating the dynamic change curve of the oil layer rhythm stratum water mixed liquid resistivity, the classical Archie model is processed by using the optimized parameters to obtain the saturation calculation model of the extra-high water cut reservoir based on the dynamic change of the stratum water resistivity parameter; the saturation model with the optimized parameters is used to calculate the water saturation of the oil layer.

2. The method of claim 1, wherein the method is a method of calculating water saturation in a high water cut reservoir using a combination of well logs and depositional rhythm. The physical property parameters of the reservoir in the step S1 include porosity and shale content; The calculation formula of the shale content and the porosity is: Vsh=(2(Gcur*Vsh')-1) / ((2Gcur)-1) Wherein: Vsh'= (GR-GRmin) / (GRmax-GRmin) PHIE=2.3*HAC-3.5*DEN-0.2*Vsh Wherein: Vsh is the shale content; Gcur is the stratum age coefficient, a constant; Vsh' is the natural gamma relative value; GRmin is the natural gamma value of the pure sandstone stratum; GRmax is the natural gamma value of the pure mudstone stratum; PHIE is the effective porosity; GR is the natural gamma logging curve value; HAC is the high resolution acoustic logging curve value; DEN is the density logging curve value.

3. The method of claim 1, wherein the method is a method of calculating water saturation in a high water cut reservoir using a combination of well logs and depositional rhythm. The method for identifying the reservoir rhythm according to the calculated physical property parameters in the step S2 is: According to the increasing and decreasing change form of the physical property curve, the rhythm type of the reservoir is determined: in the reservoir, when the porosity parameter curve increases with the increase of the depth sequence, it is defined as a positive rhythm reservoir; when the porosity parameter curve does not have obvious increase and decrease change with the increase of the depth sequence, it is defined as a homogeneous rhythm reservoir; when the porosity parameter curve decreases with the increase of the depth sequence, it is defined as a reverse rhythm reservoir; when the porosity parameter curve has multiple change trends superimposed with the increase of the depth sequence, it is defined as a composite rhythm reservoir.

4. The method of claim 1, wherein the method is a method of calculating water saturation in a high water cut reservoir using a combination of well logs and depositional rhythm. The step S3 is to subdivide the reservoir into layers according to the development application requirement and the principle of stratum sedimentation rule, and taking the peak and valley change characteristics of the well logging as the basis, the specific method is: Taking the lateral deep resistivity as the layer control curve, five change characteristics of the peak and valley form of the well logging curve are determined, including valley, peak, monotonous decrease, monotonous increase and curve flat section, the experience of artificial layering is simulated, firstly, the adjacent peak and valley difference threshold value α of layering is given to control the refinement degree of layering, then the average value of the maximum and minimum of the interval of the monotonous rising and falling curve is taken as the half amplitude point to determine the interface point of the curve layering, and a thick sedimentary rhythm layer is divided into small sedimentary units.

5. The method of claim 4, wherein the method is a method of calculating water saturation in a high water cut reservoir using a combination of well logs and depositional rhythm. The formula for determining the interface point of the curve layering is: X=[Xmin+(N-1)*Xmax] / N Wherein: |Xmin-Xmax|>α In the formula, X is the interface point curve value of the curve layering; Xmin is the minimum value of the curve; ​ Xmax is the maximum value of the curve; N is the root part of the curve, N=2 represents the half amplitude point; Alpha is the threshold value for adjusting the thickness of the sublayer.

6. The method of claim 1, wherein: The method of step S4 for square wave processing of the well logging curve to obtain the square wave curve is: on the basis of interpreting the sublayer by using the simulated artificial curve half amplitude point method, the values are taken according to the peak and valley characteristics, the maximum value is taken at the peak, the minimum value is taken at the valley, and the average value is taken at the flat curve section, so as to convert the smooth curve into a square wave and obtain the square wave curve.

7. The method of claim 1, wherein the method is a method of calculating water saturation in a high water cut reservoir using a combination of well logs and depositional rhythm. The method of step S4 for digital identification of the oil layer rhythm part according to the square wave curve and the rhythm type is: After obtaining the square wave curve and the rhythm type of the reservoir, the subdivision processing in the thick oil layer is realized, and the digital identification of the subdivided small layer according to the rhythm part is that, in a complete rhythm layer, the interval of 0-1 is used for interpolation processing according to a certain sampling frequency from the top of the rhythm to the bottom of the rhythm; and the average value of the top and bottom limits of each square wave subdivided small layer is taken as the digital identification of the vertical rhythm part of the small layer, and the rhythm part curve YLBW is obtained.

8. The method of claim 1, wherein the method is a method of calculating water saturation in a high water cut reservoir using a combination of well logs and depositional rhythm. The method of step S5 for generating the dynamic change curve of the formation water mixture resistivity of the oil layer in different rhythm parts is: according to the rhythm part of the subdivided small layer, the dynamic change value of the formation water mixture resistivity is given; The expression of the dynamic change value of the formation water mixture resistivity is: Rwz=Rwi+YLBW*(Rwh-Rwi) Wherein, Rwz is the dynamic change value of the formation water mixture resistivity; Rwi is the formation water resistivity in the original state, which is calculated by the natural potential curve of the thick oil layer of the un-watered well; YLBW is the rhythm part curve; Rwh is the formation water resistivity of the high water flooded layer, which is obtained by water sample analysis data.

9. The method of claim 1, wherein the method is a method of calculating water saturation in a high water cut reservoir using a combination of well logs and depositional rhythm. The method of step S5 for obtaining the saturation model of the ultra-high water cut reservoir by optimizing the parameters of the classical Archie model is: The dynamic formation water resistivity value is obtained by using different rhythm part information, the formation water resistivity in the original state and the formation water resistivity monitoring value of the high water flooded layer, and the saturation model of the ultra-high water cut reservoir is obtained by considering the influence of mud.

10. The method of claim 9, wherein the method is a method of calculating water saturation in a high water cut reservoir using a combination of well logs and depositional rhythm. The super-high water cut reservoir saturation model is as follows: Wherein: SW is the water saturation; Rwi is the formation water resistivity in the original state; YLBW is the rhythm part; Rwh is the formation water resistivity of the high water flooded layer, which is obtained by water sample analysis data; RT is the deep lateral resistivity curve value.

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